research topics on integrated science education

• Open access
• Published: 13 December 2021

Beyond the basics: a detailed conceptual framework of integrated STEM

• Gillian H. Roehrig   ORCID: orcid.org/0000-0002-6943-7820 1 ,
• Emily A. Dare 2 ,
• Joshua A. Ellis 2 &
• Elizabeth Ring-Whalen 3  

Disciplinary and Interdisciplinary Science Education Research volume  3 , Article number:  11 ( 2021 ) Cite this article

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Given the large variation in conceptualizations and enactment of K − 12 integrated STEM, this paper puts forth a detailed conceptual framework for K − 12 integrated STEM education that can be used by researchers, educators, and curriculum developers as a common vision. Our framework builds upon the extant integrated STEM literature to describe seven central characteristics of integrated STEM: (a) centrality of engineering design, (b) driven by authentic problems, (c) context integration, (d) content integration, (e) STEM practices, (f) twenty-first century skills, and (g) informing students about STEM careers. Our integrated STEM framework is intended to provide more specific guidance to educators and support integrated STEM research, which has been impeded by the lack of a deep conceptualization of the characteristics of integrated STEM. The lack of a detailed integrated STEM framework thus far has prevented the field from systematically collecting data in classrooms to understand the nature and quality of integrated STEM instruction; this delays research related to the impact on student outcomes, including academic achievement and affect. With the framework presented here, we lay the groundwork for researchers to explore the impact of specific aspects of integrated STEM or the overall quality of integrated STEM instruction on student outcomes.

Since the term “STEM” (Science-Technology-Engineering-Mathematics) was coined in 2001, there have been numerous efforts to improve K − 12 STEM teaching and learning around the world (Freeman et al., 2014 ). With the release of STEM policy documents across the globe (e.g., Australian Curriculum, Assessment, and Reporting Authority, 2016 ; European Commission, 2015 ; Hong, 2017 ; National Research Council (NRC), 2012), the implementation of STEM in K − 12 education has focused on interdisciplinary or integrated instruction, commonly referred to as “integrated STEM education”, rather than separate disciplinary approaches to the teaching of science, technology, engineering, and mathematics. While integrated STEM education is well established through national and international policy documents, disagreement on models and effective approaches for integrated STEM instruction continues to be pervasive and problematic (Moore et al., 2020 ). Sgro et al. ( 2020 ) argue that, in essence, integrated STEM is “whatever someone decides it means” and that the large variation across integrated STEM curricula suggests a need for “greater clarity about not only what constitutes STEM education, but how educators as a whole conceptualize STEM and the process of integration” (p. 185). In response, this paper puts forth a detailed conceptual framework for K − 12 integrated STEM education that can be used by researchers, educators, and curriculum developers as a common vision.

Various broad definitions of integrated STEM education exist in the literature and policy documents. For example, Moore, Stohlmann, and colleagues (2014) defined integrated STEM education as “an effort to combine some or all of the four disciplines of science, technology, engineering, and mathematics into one class, unit, or lesson that is based on connections between the subjects and real-world problems” (p. 38). Similarly, Kelley and Knowles ( 2016 ) defined integrated STEM as “the approach to teaching the STEM content of two or more STEM domains, bound by STEM practices within an authentic context for the purpose of connecting these subjects to enhance student learning” (p. 3). Common across almost all definitions is the use of real-world contexts to both contextualize learning and motivate student engagement (e.g., Kelley & Knowles, 2016 ; Kloser et al., 2018 ; National Academy of Engineering (NAE) and NRC, 2014). While some researchers argue for integration across all four of the STEM disciplines (e.g., Burrows et al., 2018 ; Chandan et al., 2019 ), others call for the integration of at least two of the STEM disciplines (e.g., Moore et al., 2020 ). Given the prominence of engineering within STEM policy documents (e.g., NRC, 2012; NGSS Lead States, 2013 ), many approaches to integrated STEM specifically include an engineering context or engineering design problem as the context for learning (e.g., Berland & Steingut, 2016 ; Mehalik et al., 2008 ; Moore, Stohlmann, et al., 2014). Indeed, Nathan et al. ( 2013 ) argue, the ideals of STEM integration are not likely to be fulfilled by the integration of any pair of STEM fields … the pairing of technology with engineering (the design sciences) is insufficient to satisfy STEM integration, and also excludes pairing science and math (the natural sciences). Rather, it calls for STEM integration that spans the design and natural sciences. (p. 82).

In addition to the centrality of engineering and connection to real-world problems, other aspects of integrated STEM on which there is consensus in the literature include: (a) the use of student-centered pedagogies (e.g., Asunda & Mativo, 2017 ; Johnson et al., 2016 ; Thibaut et al., 2018 ), (b) supporting the development of twenty-first century skills such as creativity, collaboration, communication, and critical thinking (e.g., Sias et al., 2017 ; Wang & Knoblach, 2018), and (c) connections between STEM disciplines should be made explicit to students (e.g., English, 2016 ; Kelley & Knowles, 2016 ; NAE and NRC, 2014). While there is consensus on these aspects as being central to broad definitions of STEM, the literature does not provide detail on how these aspects should be operationalized for quality implementation of integrated STEM education in K − 12 classrooms.

While integrated STEM education is not restricted to implementation in science classrooms, in the United States there exists a policy mandate to K − 12 science teachers through the Framework for K − 12 Science Education (NRC, 2012) and the Next Generation Science Standard s (NGSS Lead States, 2013 ) and consequently the preponderance of integrated STEM research occurs within the context of science education (Takeuchi et al., 2020 ). Thus, in this paper we specifically focus on STEM integration within K − 12 science classrooms. It is also important to state that integrated STEM is not promoted to the exclusion of other important learning goals within a K − 12 science classroom. Plainly stated, not all science content can and should be taught using an integrated STEM approach; attention should also be paid to the nature of science and engaging students in learning science concepts through inquiry-based learning.

While the field has moved towards increased agreement on definitions and broad characteristics of integrated STEM education, there remains a lack of specification in how these characteristics should be operationalized within curricula and classrooms. Educators and curriculum developers need specifics if the implementation of integrated STEM education is to meet the policy goals of using interdisciplinary and integrated approaches to teaching STEM content to increase students’ interest and readiness for STEM careers (e.g., National Academy of Science, National Academy of Engineering, and Institute of Medicine, 2007; President’s Council of Advisors on Science and Technology [PCAST], 2011). Without clear guidelines, implementation of integrated STEM education comprises a broad range of approaches (Moore et al., 2020 ), many of which, as discussed below, are problematic (e.g., Gunckel & Tolbert, 2018 ; McComas & Burgin, 2020 ). There is a clear need for research to provide critical evidence of the impact of integrated STEM education on student learning and affect toward STEM, as many arguments for integrated STEM are argued from policy and theoretical positions (e.g., NAE and NRC, 2014). The development of valid assessments and protocols to research integrated STEM teaching and learning requires that characteristics of integrated STEM education are developed in explicit detail. Thus, this paper develops a detailed framework for integrated STEM education that expands on previously established components of quality integrated STEM as broad statements to detailed constructs that describe fully what quality integrated STEM implementation should look like in the classroom. First, we examine the policy environment in which integrated STEM education is being promoted. Second, we provide an extensive literature review which expands on the consensus aspects of integrated STEM education described above to provide a more nuanced and detailed discussion of key characteristics of integrated STEM.

STEM policy

It is important to understand the policy context in which integrated STEM education is being promoted, as the myriad approaches are in response to policy directives, originating within the US, that call for addressing pressing issues such as STEM workforce needs (Takeuchi et al., 2020 ). Indeed, dominating policy arguments is the suggestion that continued national prosperity is dependent on meeting STEM workforce needs to address critical challenges such as energy, health, the environment, national security, and global development (e.g., National Academy of Science, National Academy of Engineering, and Institute of Medicine, 2007; PCAST, 2011). The number of STEM jobs is growing faster than non-STEM jobs (U.S. Bureau of Labor Statistics, 2020 ), which may result in a shortage of up to 3.5 million STEM workers in the United States by 2025 (National Association of Manufacturing and Deloitte Report, 2018 ). STEM workforce arguments are used in countries throughout the world to establish new STEM education policies and initiatives (Freeman et al., 2014 ). However, policy documents do not unpack specifics about STEM workforce needs beyond shortages of STEM workers. For integrated STEM education to address policy calls related to the STEM workforce, it is necessary to better understand the knowledge and skills that students need to be successful as STEM professionals.

More specific to the needs of the STEM workforce are concerns about a “creativity crisis” in the United States and around the world (Bronson & Merryman, 2011 ; Kim, 2011 ; Lin, 2011 ). STEM employers are looking for a workforce with not only strong STEM content knowledge and skills, but also an ability to compete in the global economy in a workforce with strong twenty-first century skills (e.g., critical thinking, communication, collaboration, and creativity) (Bronson & Merryman, 2011 ; Charyton, 2015 ). According to a World Economic Forum survey, approximately 65% of today’s Kindergarteners will end up working in jobs that do not currently exist given the rapid growth of automation and artificial intelligence in the workplace (World Economic Forum, 2016 ). Thus, it is no longer enough to expect our students to simply learn isolated facts and content. Rather than positioning students as consumers of information, students should be involved in knowledge construction. The deep understanding of content developed through knowledge construction forms the basis for students to apply twenty-first century skills to create, analyze, evaluate, innovate, and address real-world problems (Stehle & Peters-Burton, 2019 ).

Less visible in the current STEM policy rhetoric are arguments that integrated STEM education should promote increased STEM literacy and awareness, as well as addressing issues in developing countries related to equitable education and poverty reduction (Freeman et al., 2014 ; National Academy of Sciences [NAS], 2014). Indeed, teaching STEM solely from a workforce rationale is viewed by some science educators as problematic (e.g., Hoeg & Bencze, 2017 ; Zeidler, 2016 ; Zeidler et al., 2016 ). For example, Gunckel and Tolbert ( 2018 ) call out the technocratic, utilitarian, and neoliberal underpinnings of engineering design as portrayed in the Framework (NRC, 2012). These critiques are carefully considered and integrated in our development of an understanding of integrated STEM education to guide both educators and researchers seeking to better understand integrated STEM and ensure a positive learning experience for all students.

Integrated STEM framework

Throughout this literature review, we propose a framework for K − 12 integrated STEM education that provides essential details for consistent implementation and evaluation of integrated STEM teaching. Without common understandings of integrated STEM education, it is difficult at best to draw conclusions across studies about teacher practices related to integrated STEM instruction and student outcomes. This common understanding needs to move past definitions and lists of consensus features of integrated STEM that can be interpreted in myriad ways by educators. Our framework includes seven key characteristics of integrated STEM: (a) focus on real-world problems, (b) centrality of engineering, (c) context integration, (d) content integration, (e) STEM practices, (f) twenty-first century skills, and (g) informing students about STEM careers. Table 1 provides a summary of these characteristics, and a detailed literature review for each characteristic follows this overview of the framework. These key characteristics are aligned with and expand upon three of the four consensus features of integrated STEM identified in the preceding sections: (a) integrated STEM is contextualized by a real-world problem, (b) integrated STEM supports the development of twenty-first century skills, and (c) connections between STEM disciplines should be made explicit to students. We note agreement within our framework that integrated STEM requires the use of student-centered pedagogies; however, we focus on student engagement in STEM practices rather than broad notions of student-centered pedagogies. Our framework extends conceptualizations of integrated STEM to explicitly address the nature of integration, the role of engineering, and STEM career awareness. Finally, our framework directly attends to issues of diversity and equity as opposed to the techno-centric focus of prevalent conceptualizations of integrated STEM. It is important to note that none of the characteristics in Table 1 operate in isolation from each other (see Fig. 1 ). The following section grounds each characteristic in the literature and illustrates the connections amongst the characteristics.

Interactions between critical characteristics of integrated STEM

Focus on real-world problems

If learning is not centered on developing solutions to a real-world problem (Characteristic 1), a lesson cannot be considered to be representative of integrated STEM education. Indeed, as noted earlier, the most common feature included in definitions of integrated STEM in the literature is that STEM integration should be centered around a real-world problem or context (e.g., Kelley & Knowles, 2016 ; Kloser et al., 2018 ; Moore et al., 2020 ). Indeed, many students find it difficult to relate to STEM content presented using traditional, disciplinary approaches (Kelley & Knowles, 2016 ). Proponents of integrated STEM education argue that using real-world or authentic problems as a context for learning provides motivation and purpose for learning STEM content (e.g., Kelley & Knowles, 2016 ; Monson & Besser, 2015 ). Research shows that engaging students in learning through authentic engineering design problems improves student interest in science and engineering (Guzey, Moore, & Morse, 2016 ; Lachapelle & Cunningham, 2014 ; McClure et al., 2021 ). However, the selection of a real-world problem requires careful consideration as the ability to engage students with all characteristics of integrated STEM education hinges on the nature of the real-world problem (Fig. 1 ).

Our framework expands consideration of the importance of the nature of these real-world problems as care needs to be taken that these authentic problems generate interest and motivation in learning for all students (Carter et al., 2015 ; Monson & Besser, 2015 ). Given the lack of diversity within many of the STEM fields (Vakil & Ayers, 2019 ), there is a need to increase STEM interest for students that are historically under-represented in STEM. It is important to engage students in real-world problems that are personally motivating and connect STEM content to students’ lived experiences. This has been shown to make learning more meaningful and relevant, which enhances student engagement in science (Djonko-Moore et al., 2018 ) and positions students as epistemic agents in their learning (Miller et al., 2018 ). Often, integrated STEM classroom activities tend to focus on the male-oriented, technical aspects of engineering related to the design of “things”, such as designing cars and rockets (Gunckel & Tolbert, 2018 ). However, research shows that girls and students of color are more motivated by projects with a communal goal orientation, focused on societal issues such as health, the environment, and social justice as opposed to these types of gendered engineering projects (Billington et al., 2013 ; Diekman et al., 2010 ; Leammukda & Roehrig, 2020 ). The emphasis on “things” and technical criteria is oppositional to a communal goal orientation which negatively impacts interest in STEM careers (Diekman et al., 2010 ). This line of research parallels the arguments of Gunckel and Tolbert ( 2018 ), who argue for considerations of the dimensions of care and empathy in integrated STEM. While the literature has demonstrated a clear consensus that integrated STEM education should include an authentic problem to contextualize learning (e.g., Kelley & Knowles, 2016 ; Moore, Stohlmann, et al., 2014), there are important considerations about the nature of such problems if content learning and student motivation are to be promoted as argued in policy documents (e.g., Australian Curriculum, Assessment, and Reporting Authority, 2016 ; European Commission, 2015 ; NRC, 2012 ). Drawing on personal and community interests and lived experiences of students will be more motivating for students, and with purposeful consideration of students’ interests there is the potential to diversify STEM fields.

Centrality of engineering

Given the prominence of engineering within STEM policy documents (e.g., NRC, 2012 ), real-world problems are represented as an engineering design challenge (Characteristic 2) (Moore et al., 2020 ). Engineering is considered central in most definitions of integrated STEM (e.g., Berland & Steingut, 2016 ; Mehalik et al., 2008 ; Moore, Stohlmann, et al., 2014; Nathan et al., 2013 ); even within research that calls for the integration of only two disciplines to be considered integrated STEM, the most common combination is science and engineering (Moore et al., 2020 ). Thus, our framework links real-world problems to engineering design challenges (Characteristics 1 and 2 in Fig. 1 ) to promote the practices called for within current reform documents (e.g., NRC 2012 ).

Developing solutions to an overarching real-world problem relies on using and developing understanding of content from multiple disciplines (e.g., Cavlazoglu & Stuessy, 2017 ; Thibaut et al., 2018 ; Walker et al., 2018 ). Specifically, within integrated STEM education, students are expected to engage in engineering practices to develop possible design solutions to real-world problems (Berland & Steingut, 2016 ; NAE and NRC, 2014 ; NRC, 2012 ). Engineering practices are loosely defined within the NGSS through the eight science and engineering practices; however, successful integration of engineering practices into science classrooms requires a more robust articulation of engineering practices (Cunningham & Carlsen, 2014 ; Moore, Glancy, et al., 2014). In our work, we draw heavily on the Framework for Quality K − 12 Engineering Education (Moore, Glancy, et al., 2014), which proposes three domains consisting of 12 key indicators of quality K-12 engineering (see Table 2 ).

Engineering is a systematic and iterative approach to designing solutions (products, processes, and systems) based on the needs of a client (NRC, 2012 ). As such, design is widely considered to be the central activity of engineering (Dym, 1999 ). Engineering design is an iterative process of “testing the most promising solutions and modifying what is proposed on the basis of the test results leads to greater refinement and ultimately to an optimal solution” (NRC, 2012 , p. 210). In other words, response to failure is central to the engineering design process; failure is expected if innovation is to occur as it can lead to stronger, more innovative designs (Henry et al., 2021 ; Simpson et al., 2018 ). Thus, it is critical that K-12 students have opportunities within integrated STEM curriculum to fully engage in the iterative engineering design process and engage in at least one cycle of evaluating and redesigning a proposed solution or set of solutions (Moore, Stohlmann, et al., 2014). Learning from failure needs to be explicitly scaffolded for students, purposefully engaging them in a reflective decision-making process (Wendell et al., 2017 ).

Unfortunately, in K-12 classrooms engineering design is usually depicted solely as a technical problem (Gunckel & Tolbert, 2018 ). Thus, our framework expands on the Framework for Quality K-12 Engineering Education (Moore, Glancy, et al., 2014) to extend its focus on the technical aspects of engineering design to explicitly consider diversity and equity within STEM. Parallel to the work of professional engineers, students are expected to understand and address the criteria and constraints of a problem in developing possible design solutions (Watkins et al., 2014). Yet, these constraints are usually limited to realistic, but surface-level, issues such as time, access to materials, and budget, often ignoring the social, political, and ethical issues that are inherent in most real-world problems (Gunckel & Tolbert, 2018 ; Roehrig et al., 2020 ). Indeed, some researchers argue the NGSS (NGSS Lead States, 2013 ) and the Framework (NRC, 2012 ) marginalize the moral and ethical considerations within engineering design (e.g., Kahn, 2015 ). Gunckel and Tolbert ( 2018 ) caution that, while engineering education has elevated a focus on ethics, the focus of this approach still draws on technocratic and utilitarian principles. An approach grounded in care and empathy is necessary to reframe engineering education to engage students in considering the societal implications of their design solutions (Gunckel & Tolbert, 2018 ; Jackson et al., 2021 ). Similarly, researchers have promoted the inclusion of socio-scientific issues (SSI) into integrated STEM instruction (Kahn, 2015 ; Owens & Sadler, 2020 ; Roehrig et al., 2020 ). In addition to promoting scientific solutions to a real-world problem, SSI explicitly address moral and ethical considerations (Kahn, 2015 ; Zeidler, 2016 ). This approach to integrated STEM education not only elevates the purpose to include STEM literacy for all citizens regardless of their future participation in a STEM career, but also reimagines the necessary skills needed in the STEM workforce to improve and diversify thinking and approaches to engineering design.

Context integration

The real-world problem and/or engineering design challenge used to motivate student learning should be complex enough to foster multiple solutions (Lachapelle & Cunningham, 2014 ) and engage learners in applying and expanding their knowledge of the STEM disciplines (Berland & Steingut, 2016 ; Monson & Besser, 2015 ). There needs to be clear alignment between the engineering design challenge or real-world problem and specific content learning objectives (see Fig. 1 ), with the challenge or problem framed such that students need to draw upon STEM content knowledge to generate possible designs and make evidence-based decisions. This is represented in Fig. 1 as context integration (Characteristic 3).

Without clear and explicit integration between the problem context and content learning goals, students will resort to tinkering (a form of trial and error), negating the achievement of content learning objectives (McComas & Burgin, 2020 ; Moore, Glancy, et al., 2014; Roehrig et al., 2021 ). This relates to a significant problem pointed out by Takeuchi et al. ( 2020 ) in that there is a lack of a clear focus on specific STEM concepts. In their systematic review of the literature, Takeuchi et al. ( 2020 ) reported that almost 40% of the 154 integrated STEM articles they reviewed focused on students’ career aspirations and choices rather than learning of specific STEM concepts. The real-world problem and engineering design challenge must provide a context for learning target STEM content, as well as being motivating and engaging for students to help promote positive STEM identities (e.g., Tai et al., 2006 ).

Unfortunately, even with a real-world context, design tasks can degenerate into simply making crafts or tinkering solely through trial and error, neither of which require knowledge of STEM content or practices to develop solutions. While engineers develop both products and processes as solutions to real-world problems, K-12 engineering and integrated STEM educators tend to gravitate toward the building of physical products. For example, engineering courses, makerspaces, and digital fabrication labs have proliferated in K-12 schools over the past decade (Adams Becker et al., 2016 ). The focus of makerspaces and fabrication labs is the development of a product, often through “tinkering with materials with an endpoint in mind” (Sheffield et al., 2017 , p.149). In effect, these spaces are the modernized versions of vocational education or shop class (Blackley et al., 2017 ; McComas & Burgin, 2020 ). Studies demonstrate limited content learning in science and mathematics for students participating in hands-on, project-based engineering courses because of the lack of clear and explicit connections to science and mathematics content (Tank et al., 2019 ). Makerspaces, fabrication labs, and engineering programs are not commensurate with characteristics of integrated STEM education unless teachers make explicit connections to mathematics and science content (Sheffield et al., 2015). As such, integrated STEM education requires an authentic problem or engineering design challenge that engages students in explicitly learning and applying science and mathematics concepts.

The practice of engineering requires the use and application of science, mathematics, and engineering knowledge. K-12 STEM education should emphasize this interdisciplinary nature by providing students with opportunities to apply developmentally appropriate mathematics or science content within the context of solving engineering problems (Arık & Topçu, 2020 ; NRC, 2012 ; Reynante et al., 2020 ). Indeed, engineering as a discipline involves an “understanding of the science undergirding physical relationships and the mathematical foundations of models that guide engineering design, as opposed to tinkering or making random modifications without basing those changes upon mathematical and/or scientific analyses” (Householder & Hailey, 2012 , p.12). Design iterations throughout the engineering design process are based on evidence, scientific and mathematical knowledge, and analyses of the data generated through the testing of prototype designs (Mathis et al., 2016 ; Mathis et al., 2018 ).

Our argument is that integrated STEM education at its core is driven by real-world problems and the development of possible solutions to those problems using knowledge and practices from any relevant discipline. If students are to consider and understand the full socio-historical-political context of the problems in developing and evaluating design solutions to real-world problems (e.g., Gunckel & Tolbert, 2018 ), then knowledge and practices from the social sciences are necessary in addition to the technical knowledge of the STEM disciplines. In addition, critical to addressing issues of equity and diversity in STEM, is promoting students’ lived experiences and cultural knowledge, as well as disciplinary knowledge, as relevant to proposing solutions to real-world problems and engineering design challenges. Unfortunately, the cultural knowledge of students who are marginalized and under-represented in STEM are often perceived as deficit and not as legitimate ways of engaging in STEM (Tan & Calabrese Barton, 2018 ). Limited attention has been paid within the integrated STEM education literature to elevating the application of cultural and indigenous knowledge in engineering design; however, promoting STEM interest and learning for all students needs to attend to approaches such as cultural maker education (Tan & Calabrese Barton, 2018 ) and ethno-engineering (Friesen & Herrmann, 2018 ; Kilada et al., 2021 ).

Content integration

In addition to explicit connections between the real-world problem/engineering design challenge and the targeted science and/or mathematics content (Characteristic 3 - contextual integration), it is important that connections between the disciplines (Characteristic 4 - content integration) are also made explicit to students (English, 2016 ; Kelley & Knowles, 2016 ; NAE and NRC, 2014 ). Although teachers may understand the connections across the range of content representations and activities within an integrated STEM lesson, students often struggle to make these connections on their own (Dare et al., 2018 ; Tran & Nathan, 2010 ). Since students seldom make these connections spontaneously (Tran & Nathan, 2010 ), teachers must either help students recognize and identify these connections or explicitly make these connections clear for students. In a study of a high school engineering classroom, Nathan et al. ( 2013 ) discuss productive pedagogical moves to help make these interdisciplinary connections explicit to students. Their suggestions include asking questions, facilitating problem solving, creating models and representations, and explicitly foregrounding disciplinary knowledge to help students to identify the presence of specific content.

Content integration can be achieved through multidisciplinary, interdisciplinary, or transdisciplinary approaches (Bybee, 2013 ; Moore & Smith, 2014 ; Vasquez et al., 2013 ). Some researchers argue that one approach is not superior to another (Rennie et al., 2012 ), whereas others define a continuum of increasing integration from disciplinary to transdisciplinary (e.g., Vasquez et al., 2013 ; Wang & Knoblach, 2018 ). Proponents of an interdisciplinary approach argue that this approach is superior because a theme or real-world problem anchors the learning (e.g., Vasquez et al., 2013 ) in contrast to multidisciplinary approaches that “begin and end with the subject-based content and skills [with] students expected to connect the content and skills in different subjects that had been taught in different classrooms” (Wang et al., 2011 , p.2).

While many researchers define multidisciplinary integration as occurring across multiple classrooms (e.g., Vasquez et al., 2013 ), the calls to integrate engineering and mathematical thinking in science classrooms (e.g., NRC, 2012 ) require integration across the disciplines within a science lesson or unit of instruction (Capobianco & Rupp, 2014 ; Moore, Stohlmann, et al., 2014). In a multidisciplinary approach, each STEM discipline would be identifiable within the curriculum and instruction, whereas in an interdisciplinary approach, each discipline would be difficult to distinguish from one another (Lederman & Niess, 1997 ). Given the argument that integrated STEM education can improve students’ learning of science and mathematics concepts (e.g., Berland & Steingut, 2016 ; Fan & Yu, 2017 ; Guzey et al., 2017 ) and the difficulty faced by students in recognizing the way in which different content areas support and complement each other (English, 2016 ; NAE and NRC, 2014 ), the connections between content areas need to be made explicit for students (English, 2016 ; Kelley & Knowles, 2016 ). As stated in the NAE and NRC ( 2014 ) report:

Connecting ideas across disciplines is challenging when students have little or no understanding of the relevant ideas in the individual disciplines. Also, students do not always or naturally use their disciplinary knowledge in integrated contexts. Students will thus need support to elicit the relevant scientific or mathematical ideas in an engineering or technological design context, to connect those ideas productively, and to reorganize their own ideas in ways that come to reflect normative, scientific ideas and practices. (p. 5)

While not discounting transdisciplinary and interdisciplinary approaches to integrated STEM education, multidisciplinary approaches yield the best approach for students to learn and apply disciplinary content and develop an understanding of the ways in which disciplinary content is connected.

Given the positioning of engineering within national and state science standards, mathematics and technology have received little attention in the literature and their inclusion within integrated STEM curriculum is often limited (Roehrig et al., 2021 ) (e.g., Roehrig et al., 2021 )). Thus, it is critical that more explicit attention is given to mathematics and technology in the development of more robust and detailed models of integrated STEM education.

The case of mathematics

Despite a long history of integration between science and mathematics (e.g., Berlin & White, 1995 ; Davison et al., 1995 ; Huntley, 1998 ), the integration of mathematics is particularly difficult within integrated STEM education (Walker, 2017 ; Zhang et al., 2015 ), and studies show only small impacts on students’ mathematical knowledge (e.g., Becker & Park, 2011 ; NAE and NRC, 2014 ; Nugent et al., 2015 ). For example, Huntley ( 1998 ) describes the interdisciplinary approach as having one discipline that is in the foreground with the second discipline in the background simply to provide context. However, most often in science (and more recently in integrated STEM lessons), mathematics is backgrounded as a tool for data measurement and analysis with few or no conceptual learning goals for mathematics (e.g., Baldinger et al., 2021 ; Ring et al., 2017 ; Roehrig et al., 2021 ; Walker, 2017 ). This treatment of mathematics is reinforced by the NGSS through the inclusion of mathematics and computational thinking as one of the eight science and engineering practices (NRC, 2012 ). This practice presents mathematics as a tool that is central to science and engineering (Hoda, Wilkerson, & Fenwick, 2017 ) including “tasks ranging from constructing simulations, to making quantitative predictions, to statistically analyzing data, to recognizing, expressing, and applying quantitative relationships” (Aminger et al., 2021 , p. 190).

While it is difficult to imagine teaching and learning science or engineering without engaging in mathematical practices, the mathematical connections are most often implicit and may not be transparent to students (Roehrig et al., 2021 ). Successful mathematics integration requires that the role of mathematics be made explicit, such as through putting mathematics in the foreground (Silk et al., 2010 ). For example, in a meta-analysis, Hurley ( 2001 ) found the greatest effect sizes for mathematics learning occurred when students learned science and mathematics content in sequence through a multi-disciplinary approach, rather than interdisciplinary approaches. More recently, Baldinger et al. ( 2021 ) argued that science and mathematics learning opportunities need to be strategically positioned and highlighted across a unit. Indeed, as noted previously, conceptual learning of science and mathematics is improved through a multidisciplinary approach that allows mathematics and science concepts to be explicitly and purposefully foregrounded within a unit.

In a rare study of the implementation of mathematical and computational thinking in K-12 science classrooms, Aminger et al. ( 2021 ) found that teachers were able to improve students’ understanding of scientific phenomena only when engaged in high cognitive demand mathematical tasks, such as mathematical modeling. Modeling uses mathematical equations to represent scientific phenomena and communicate scientific ideas (e.g., Bialek & Botstein, 2004 ; Brush, 2015 ; Lazenby & Becker, 2019 ). While students are expected to interpret the mathematical and scientific meaning represented by an equation (e.g., Bialek & Botstein, 2004 ; Sevian & Talanquer, 2014 ), studies at the postsecondary level show that students rely on algorithmic procedures without making connections between the mathematical equation and the scientific phenomenon (e.g., Bing & Redish, 2009 ). Postsecondary researchers advocate for blended sensemaking, where students’ scientific and mathematical knowledge is activated and used to develop understanding of scientific phenomena (Zhao & Schuchardt, 2021 ). When instruction encourages engagement in mathematical modeling through blended sensemaking, students show improved quantitative problem solving (e.g., Becker, Rupp, & Brandriet, 2017 ; Lazenby & Becker, 2019 ; Schuchardt & Schunn, 2016 ).

The case of technology

Technology is rarely explicitly called out within definitions of integrated STEM education (e.g., Ellis et al., 2020 ; Herschbach, 2011 ). Implicit treatments of technology take two primary forms: the integration of educational technology and technology as the production and use of technology within engineering (Ellis et al., 2020 ; Kelley & Knowles, 2016 ). Unquestionably, educational technology plays an increasingly large role in K-12 classrooms and, as is the case for all teachers, science teachers are involved in using digital technology tools to present content and allow students to complete their work, often through one-to-one technology initiatives. Standards guiding the use of technology in K-12 classrooms, such as the International Society for Technology in Education (ISTE) Standards for Educators, which define the technological skills educators need (ISTE, 2000), are content- and grade-level agnostic (Ellis et al., 2020 ). Most often, these digital technologies are used as replacements to traditional paper and text learning. For example, in science classrooms, digital notebooks have been used instead of paper notebooks (Constantine & Jung, 2019 ). While this allows students to include multimedia such as photos and videos and work collaboratively through web-based tools, these uses of technology are not specific to STEM.

Given the focus on engineering within the NGSS , views of technology within integrated STEM education are often connected to how technology is portrayed within engineering curriculum. In a review of K-12 engineering curricula, technology was primarily represented as the product of engineering (NRC, 2009 ). This representation of technology within integrated STEM education is clearly stated within the NGSS where engineering is defined as “a systematic practice for solving problems, and technology as the result of that practice” (NRC, 2012 , p. 103). Similarly, the Framework states that “technologies result when engineers apply their understanding of the natural world and of human behavior to design ways to satisfy human needs and wants” (NRC, 2012 , p. 12). In essence, under this definition of the “T” in STEM, STEM becomes SEM, resulting in technology being subsumed by engineering.

More productive in defining technology specific to integrated STEM education is the view of the “T” in STEM defined as the tools used by practitioners of science, mathematics, and engineering (Ellis et al., 2020 ; NAE and NRC, 2014 ). To support student engagement in the authentic practices of STEM professionals, students should have opportunities to use STEM-specific tools or technologies (e.g., Bell & Bull, 2008 ; Ellis et al., 2020 ; McCrory, 2008 ). A common example in science classrooms is the use of digital probes to collect and analyze data (e.g., Hechter & Vermette, 2014 ). More recently, with the addition of engineering into science classrooms, new technologies such as computer-assisted design (CAD) software and 3-D printers are being introduced (e.g., Wieselmann et al., 2019 ). Critical to integrated STEM education, however, is that these tools should not be limited to data collection devices; rather, they should encourage deeper student engagement with science content (Bull & Bell, 2008 ). Moving beyond basic data practices, technology practices in STEM education can be elevated to incorporate simulation and modeling practices which have been shown to improve students’ conceptual science understanding (Aminger et al., 2021 ).

Summary of content integration

Given the need for disciplinary knowledge to be activated and applied in integrated STEM lessons, there is a strong argument for a multidisciplinary approach where students have opportunities to both learn the content and connect that content to an authentic problem. Implicit connections are not enough; observations of instruction should yield clear and explicit discussion orchestrated by the teacher to facilitate students’ understanding of the connections across the disciplines. The inter-relationships among the disciplines are complex and require teaching STEM content in deliberate and purposeful ways so that students understand how STEM content is conceptually linked. In the case of mathematics and technology, it is critical that these subjects are not limited to tools in the service of data collection and analysis. When appropriate, curriculum developers and teachers should engage students in higher cognitive demand practices and explicit sensemaking through mathematical and technology-assisted modeling. While the literature related to modeling in physics is more robust (e.g., Hestenes, 2010 ), modeling literature also exists in other scientific disciplines that can be used to guide higher quality mathematics integration (e.g., Lazenby & Becker, 2019 ; Schuchardt & Schunn, 2016 ; Zhao & Schuchardt, 2021 ). Engagement in these data and mathematical practices, as practiced by STEM professionals, is a STEM-specific approach to technology integration.

Integration through STEM practices and twenty-first century skills

Also common across definitions of integrated STEM are references to specific disciplinary practices (e.g., inquiry, engineering design), as well as to shared practices and skills (e.g., critical thinking, creativity) (Moore et al., 2020 ). In addressing real-world problems and engineering design challenges, students should engage directly in authentic STEM practices (Characteristic 5) and twenty-first century skills (Characteristic 6) to develop potential solutions (Fig. 1 ) (e.g., Kelley & Knowles, 2016 ; Moore, Stohlmann, et al., 2014). The nature of the engineering design challenge is critical in promoting the desired learning outcomes and should be structured with multiple possible solution pathways. For example, if the task is too constrained, then the design space becomes limited, and students will not have the opportunity to develop important twenty-first century skills, such as critical thinking and creativity.

STEM practices

Engaging students in STEM practices is a common component of definitions of integrated STEM education (e.g., Kelley & Knowles, 2016 ; Moore et al., 2020 ). These practices are “a representation of what practitioners do as they engage in their work and they are a necessary part of what students must do to learn a subject and understand the nature of the field” (Reynante et al., 2020 , p.3). Engaging students in STEM practices is supported broadly by pragmatism, which emphasizes learning by doing (Asunda, 2014 ), and more specifically by social constructivist learning theories that underpin reforms in STEM education that advocate for students’ active construction of knowledge as opposed to transmission of knowledge (e.g., Guzey, Moore, & Harwell, 2016 ; Riskowski et al., 2009 ).

Central to knowledge construction and the work of STEM professionals are data practices (Duschl et al., 2007 ). Data practices include the creation, collection, manipulation, analysis, and visualization of data (Weintrop et al., 2016 ). Given that engineering design challenges afford multiple solution pathways without a single correct solution (Lachapelle & Cunningham, 2014 ) and “data do not come with inherent structure that leads directly to an answer” (Weintrop et al., 2016 , p. 135), it is important that students are actively engaged in data practices and using data to make decisions as they engage in the engineering design process. Within the Framework (NRC, 2012 ), this is called out as the practice of engaging in argument from evidence , which features the use of evidence and scientific and mathematical knowledge to develop explanations in science and justify design decisions in engineering.

Argumentation is a common practice within both science and engineering fields (Couso & Simarro, 2020 ); however, while scientific argumentation is well-supported within the research literature (e.g., Berland & McNeill, 2010 ), the level to which K-12 students use both evidence and STEM content to justify design decisions is in its infancy (e.g., Mathis et al., 2018 ; Purzer et al., 2015 ; Valtorta & Berland, 2015 ). Argumentation and decision-making require considering the advantages and disadvantages of possible design solutions in light of available evidence and any defined criteria and constraints (Wendell et al., 2017 ).

Siverling et al. ( 2017 ) argue that students’ application of scientific and mathematical content is promoted through the explicit use of evidence-based reasoning within integrated STEM lessons. For example, the classroom activities may require students to justify their thinking about why an initial design solution should be pursued during the planning phase and additionally require students to use evidence and STEM content when evaluating a tested design solution and justifying it to the client (Mathis et al., 2016 ; Mathis et al., 2018 ). This formal evidence-based reasoning explicitly asks students to make claims about their designs and design decisions that are supported by both evidence (from iterative testing) and reasoning (using scientific and mathematical content) (Siverling et al., 2019 ). Students do not spontaneously use science and mathematics content to justify and explain their design choices; rather, students focus on cost and material limitations when engaging in engineering design tasks (e.g., English et al., 2013 ; Guzey & Aranda, 2017 ). Thus, explicit inclusion of evidence-based reasoning in K-12 integrated STEM lessons is necessary to scaffold students in connecting science and mathematics content to the engineering design challenge.

STEM content knowledge is not the only consideration in making design decisions. In evaluating a possible design solution, students are expected to prioritize “criteria and trade-offs that account for a range of constraints, including cost, safety, reliability, and aesthetics as well as possible social, cultural, and environmental impacts” (NGSS standard HS-ETS1–3). It is important that the social and cultural aspects of proposed solutions are not ignored, as we truly intend to develop a STEM literate citizenry and develop a future workforce who think more deeply about their work beyond the traditional technocratic focus (Gunckel & Tolbert, 2018 ; Roehrig et al., 2020 ; Zeidler, 2016 ).

Students should have agency in design decisions as they engage in the engineering design process (e.g., Berland & Steingut, 2016 ; Johnson et al., 2016 ; Saito et al., 2015 ). Engineering design challenges should be constructed with multiple solution pathways, allowing students to determine their own solution trajectories and opportunities to build knowledge as possible design solutions develop from students’ questions, ideas, and explorations. Miller et al. ( 2018 ) argue that we must also position students as epistemic agents as opposed to receivers of STEM content, without which the call from the Framework (NRC, 2012 ) for students to engage in STEM practices will not be realized. Miller et al. ( 2018 ) define epistemic agency as “students being positioned with, perceiving, and acting on, opportunities to shape the knowledge building work in their classroom community” (p. 1058). Specifically, students should have opportunities to: (a) build on personal and cultural knowledge as a resource for learning, (b) build knowledge, (c) build a knowledge product that is personally useful, and (d) change structures that constrain and support action. When afforded epistemic agency, students can propose solutions to personally meaningful problems, rather than simply learning the canonical facts of the discipline (Schwarz et al., 2017) and mimicking the proscribed practices. Engaging students in engineering design challenges contextualizes learning around meaningful and authentic problems, providing a sense of agency as students can see the content learning goals as useful and relevant to developing solutions to the problem (e.g., Schwarz et al., 2017). Researchers argue that real-world problems should position students as not only knowledge builders, but also change agents in their community, further promoting epistemic agency and the development of STEM identity (Billington et al., 2013 ; Leammukda & Roehrig, 2020 ; Miller et al., 2018 ).

• Twenty-first century skills

In addition to specific STEM practices, integrated STEM instruction should support the development of twenty-first century skills (e.g., Moore, Glancy, et al., 2014; Sias et al., 2017 ). Broadly, twenty-first century skills include knowledge construction, real-world problem solving, skilled communication, collaboration, use of information and communication technology for learning, creativity, and collaboration (Partnership for twenty-first Century Learning, 2016 ); these are the skills “necessary for a person to adapt and thrive in an ever-changing world” (Stehle & Peters-Burton, 2019 , p.2). A recent trend has been to include the arts, as proponents of STEAM education argue that the integration of the arts will enhance students’ critical thinking and problem-solving skills and cultivate their creativity (Trevallion & Trevallion, 2020 ). However, these arguments are already central to agreed-upon goals of integrated STEM education (NAE and NRC, 2014 ; Moore, Glancy, et al., 2014), and creativity is pivotal within the STEM disciplines without the insertion of the arts. Integrated STEM education provides a rich environment for the development of critical thinking, collaboration, creativity, and communication (Stehle & Peters-Burton, 2019 ).

The ill-defined nature of real-world problems and engineering design challenges requires that students engage in critical thinking, drawing on their STEM content knowledge and lived experiences to propose possible design solutions. Engaging in the engineering design process inherently incorporates creativity and critical thinking as there is no single correct solution, thus promoting the potential of transformative and innovative design solutions (Stretch & Roehrig, 2021 ; Petroski, 2016 ; Simpson et al., 2018 ). As students iteratively test and improve their design solutions, they will experience design failure. As previously noted, failure should be expected if innovation is to occur, and the ability to learn from failure can lead to stronger designs and innovation through the application of creativity and critical thinking (Henry et al., 2021 ; Simpson et al., 2018 ).

Given the highly interdisciplinary and integrative nature of engineering, students should also be provided opportunities to work together in teams to enhance their collaboration skills (Riel et al., 2012; Rinke et al., 2016 ; Thibaut et al., 2018 ), which are necessary to develop negotiated design solutions that synthesize across differing understandings of the same problem space (Wendell et al., 2017 ). Indeed, in the K-12 classroom, small group activities account for approximately half of instructional time in science classrooms with the expectation that small groups co-construct knowledge of STEM content and design solutions to real-world problems (Wieselmann et al., 2020 ; Wendell et al., 2017 ). Sharunova et al. ( 2020 ) used Bloom’s taxonomy (Anderson & Krathwohl, 2005 ) to define a continuum of cognitive engagement that groups engage in during small group engineering design activities. Integrated STEM learning environments involve “new levels of communication, shared vision, collective intelligence, and direct coherent action by students” (Asunda, 2014 , p. 8). Further, researchers call for integrated STEM activities wherein students are expected to collectively apply what they have learned to develop possible design solutions and improve these designs through iterative analysis and evaluation (Asunda et al., 2015; Dolog et al., 2016 ; Sharunova et al., 2020 ).

Promoting STEM careers

The final characteristic, promoting STEM careers (Characteristic 7), is the least common feature of integrated STEM within the literature. As such, it stands somewhat separate from the other characteristics of the integrated STEM framework but undergirds the policy motivation for including integrated STEM education in K-12 classrooms. With the goal of promoting future participation in STEM careers in mind, integrated STEM education should expose students to details about STEM careers (Jahn & Myers, 2014 ; Luo et al., 2021 ). This should include both allowing students to engage in the authentic work of STEM professionals (Kitchen et al., 2018 ; Ryu et al., 2018 ) and critically promoting student development of STEM identities. A growing body of research has shown that STEM interest, attitude, and identity serve as predictors of sustained pursuit in the STEM disciplines rather than academic performance in STEM coursework (Avraamidou, 2020 ; Rodriguez et al., 2017 ; Tai et al., 2006 ). Furthermore, identity research has shown that students who show interest and enjoyment in STEM do not necessarily see themselves pursuing a future STEM career (Carlone et al., 2011 ); this is especially true for students from historically underrepresented groups of people who are less likely to show interest in and identify with the STEM domains (Rodriguez et al., 2017 ). Further, STEM interests and career aspirations are largely developed by eighth grade (Tai et al., 2006 ), suggesting a need to introduce students to STEM careers early in their education. In addition to introducing students to STEM careers, research shows that a focus on connections to personal experience and knowledge can help shape students’ identity within STEM (Ryu et al., 2018 ; Carlone et al., 2014 ; Sias et al., 2017 ).

Although supporting students in developing solutions to real-world problems through engaging in STEM practices and twenty-first century skills may also help to develop positive STEM identities and interest in STEM, these activities do not require any explicit connection to STEM careers. Research exploring the development of students’ understanding of engineering is limited and debate remains about whether implicit modeling of STEM professions by engaging students in hands-on STEM activities leads to durable and robust understandings about the work of engineers and other STEM professionals (e.g., Svihla et al., 2017 ). However, explicit discussion of STEM professions can help students to understand specific career opportunities and align these professions with their interests (Kitchen et al., 2018 ; Ryu et al., 2018 ).

Implications and use of the framework

Each of the seven critical characteristics of integrated STEM education (Table 1 ) has important implications for teachers in their planning and implementation of integrated STEM if integrated STEM in K-12 classrooms is going to be successful in promoting STEM literacy and increasing diversity in the STEM fields. Careful consideration is critical in selecting the context for an integrated STEM lesson, as research shows differences in motivation to engage in STEM for students of color and women who are under-represented in STEM as compared to White males (e.g., Billington et al., 2013 ; Diekman et al., 2010 ; Leammukda & Roehrig, 2020 ). While some science topics lend themselves to simple engineering design activities, such as designing a mousetrap car to travel as far as possible, these activities are not contextualized in a real-world problem. In contrast, students could be asked to design habitats to protect equatorial penguins impacted by climate change, a problem that requires knowledge and application of the scientific concepts of heat transfer (Sheerer & Schnittka, 2012). This engineering design challenge is contextualized by a real-world problem created through human impact on the environment and could easily be adapted to include considerations of human-caused environmental issues and local policies and traditions in developing design solutions. By contextualizing an engineering design challenge in a real-world problem, we ask students not only to understand the technical criteria and constraints of a problem but also to consider the problem within the context of a potentially difficult moral and ethical dilemma. Teachers should seize such opportunities to guide students in sense-making, understanding the authenticity of the context, and approach these problems with a critical perspective. Attention to selecting real-world problems and related engineering design challenges that promote positive STEM identities for students that are under-represented in STEM not only addresses reported workforce needs but brings new perspectives and approaches to how STEM content and practices are applied in the real-world.

Unfortunately, even with a real-world context, engineering design tasks can degenerate into tinkering and iterative improvement of designs through random trial and error (McComas & Burgin, 2020 ; Moore, Glancy, et al., 2014; Roehrig et al., 2021 ) if these integrated STEM lessons are poorly planned. As well as providing a motivating context designed to promote positive STEM identities, the real-world problem and engineering design challenge must provide a context for learning specified STEM content. This could involve the reactivation of prior knowledge or the explicit teaching of STEM content within a unit of instruction. We suggest that a pedagogical approach closer to multidisciplinary integration might better afford students’ recognition of the STEM content inherent within an integrated STEM unit. In other words, quality integrated STEM units (e.g., Bhattacharya et al., 2015 ; Karahan et al., 2014 ; Moore, Guzey, et al., 2014; Moore et al., 2015 ) should include lessons designed to explicitly teach relevant STEM content. Given that students rarely make these connections spontaneously (Tran & Nathan, 2010 ), it is critical that teachers use specific pedagogical approaches, such as evidence-based reasoning (Mathis et al., 2016 ; Mathis et al., 2018 ), to help make these connections explicit. Strong teacher facilitation and questioning is needed to help students recognize the connections across the disciplines and use these connections to develop stronger design solutions through iterative and reflective processes.

Our integrated STEM framework helps to not only provide more specific guidance to educators, but also support for integrated STEM research. Despite the push for integrated STEM in K-12 classrooms, the development of observation protocols that assess STEM-integrated teaching has been slow. Until valid protocols are developed, STEM education researchers continue to rely on existing instruments that predate current STEM education initiatives, such as the Reformed Teaching Observation Protocol (Sawada et al., 2002 ). The lack of a detailed integrated STEM framework thus far has prevented the field from systematically collecting data in classrooms to understand the nature and quality of integrated STEM instruction; this delays research related to the impact on student outcomes, including academic achievement and affect. This framework provides detailed guidance on teacher practices one would expect to observe within an integrated STEM lesson. With this framework, the groundwork is now set for researchers to explore the impact of specific aspects of integrated STEM or the overall quality of integrated STEM instruction on student outcomes as this framework could guide the development of observational protocols for integrated STEM which are currently lacking in the field (e.g., Dare et al., 2021 ).

Conclusions

Our framework addresses a critical need in the field to move beyond simple definitions of integrated STEM to detailed descriptions that operationalize central constructs such as the nature of integration itself. Based on intentions of STEM policy documents and the extant literature, we proposed an integrated STEM framework that includes seven key characteristics of integrated STEM: (a) focus on real-world problems, (b) centrality of engineering, (c) context integration, (d) content integration, (e) STEM practices, (f) twenty-first century skills, and (g) informing students about STEM careers. While these key characteristics include commonly agreed upon components of integrated STEM (e.g., Johnson et al., 2016 ; Kelly & Knowles, 2016; Moore, Stohlmann, et al., 2014), our framework conceptualizes each of the key characteristics in detail, operationalizing integrated STEM for educators, curriculum developers, and researchers. This is critical as statements such as “an effort to combine some or all of the four disciplines of science, technology, engineering, and mathematics into one class, unit, or lesson that is based on connections between the subjects and real-world problems” (Moore, Stohlmann, et al., 2014, p. 38) do not provide enough information about critical issues such as how to integrate any subset of the STEM disciplines or what real-world problems would be appropriate to drive learning in STEM for all students.

Most importantly, our framework directly attends to issues of diversity and equity as current definitions and implementation of integrated STEM are content-focused and consider only the technical aspects of engaging in solving real-world problems and/or engineering design challenges. Our framework specifically addresses issues raised by critics of integrated STEM (e.g., Gunckel & Tolbert, 2018 ; Roehrig et al., 2020 ; Zeidler, 2016 ) to give full consideration to the socio-historical-political context in which the engineering design challenge resides and use this knowledge in making design decisions. The framework also attends to the development of STEM identities for all students through understanding how the nature of the real-world problem and/or engineering design challenge can constrain or afford interest and engagement in STEM for girls and students of color (e.g., Billington et al., 2013 ; Diekman et al., 2010 ; Leammukda & Roehrig, 2020 ). Also important to promoting positive STEM identities for all students is elevating students’ lived experiences and cultural knowledge as valid forms of knowledge to be drawn on as they engage in developing solutions to real-world problems.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

National Academy of Engineering

National Academy of Science

Next Generation Science Standards

National Research Council

President’s Council of Advisors on Science and Technology

Socio-scientific Issues

Science-Technology-Engineering-Mathematics

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Acknowledgements

This research was made possible by the National Science Foundation grants 1854801, 1812794, and 1813342. The findings, conclusions, and opinions herein represent the views of the authors and do not necessarily represent the view of personnel affiliated with the National Science Foundation.

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GR wrote the manuscript based on substantive discussions with ED, JE, and ERW. ED, JE, and ERW provided significant feedback on the manuscript. All authors read and approved the final manuscript.

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Subject integration and theme evolution of STEM education in K-12 and higher education research

• Zehui Zhan   ORCID: orcid.org/0000-0002-6936-1977 1 , 2 &
• Shijing Niu 1  

Humanities and Social Sciences Communications volume  10 , Article number:  781 ( 2023 ) Cite this article

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Over the past two decades, the field of STEM education has produced a wealth of research findings. This study systematically reviewed the published literature from the perspective of subject integration and theme evolution, considering both K-12 and higher education. It was found that STEM education originated from higher education, but the main emphasis is gradually shifting to the K-12 stage. There were mainly sixteen subjects involved in STEM education, showing the gradual in-depth integration of science, engineering, technology, math, humanities, and social sciences, in which humanism is increasingly emphasized. Culture is a new perspective for understanding the diversity of participants, which also gives STEM education a distinctive regional character. In addition, in the K-12 stage, research related to computer science and art stands out alongside the four main subjects, demonstrating relatively even distribution across research themes. Conversely, in higher education, engineering, and chemistry garner considerable attention, with research themes predominantly concentrated on learning outcomes and social relevance. On a holistic scale, researchers exhibit a pronounced interest in learning outcomes, yet relatively less emphasis is placed on pedagogical aspects. Regarding prospective trends, there should be a heightened focus on the cultivation of students’ thinking competencies, students’ career development, and pedagogy.

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Introduction

In response to the global challenges, the promotion of economic development, and the need to meet modern society’s demands for knowledge and skills within the realms of STEM, the emergence of STEM education aimed to develop employment opportunities in STEM fields while bolstering national competitiveness. The acronym STEM education originated from the four subjects (i.e., science, mathematics, engineering, and technology) that were proposed in the report “ Undergraduate Science, Mathematics, and Engineering Education ” (National Science Board 1986 ). Essentially, STEM education stands as an innovation-oriented education that prevailed in Western countries, spearheaded by the United States.

Subsequently, Yakman ( 2008 ) introduced the addition of the “A” element, representing arts, to STEM education, thereby incorporating humanities subjects such as history, philosophy, and religion. The fundamental objective of STEM education is to amalgamate multiple subjects into a cohesive framework (Morrison 2006 ). According to the National Science Foundation ( 2014 ), STEM entails a comprehensive integration of various disciplines, encompassing not only the subjects of natural sciences (e.g., computers and information, engineering, and mathematics), but also the subjects of social sciences (e.g., psychology, economics, sociology, and political science). With an increasing number of disciplines becoming intertwined in STEM education, its interdisciplinary essence has become progressively prominent. As a result, STEM education is increasingly acknowledged as interdisciplinary education with a focus on engineering, where subject integration plays a central role.

In the past two decades, STEM education has witnessed a large number of research achievements, and many scholars have conducted comprehensive reviews on the topic. These studies have focused either on curriculum reform (Uskoković 2023 ), teaching methods (Li and Wong 2023 ), or technology applications (Salas-Pilco et al. 2022 ; Conde et al. 2021 ). At the research level, especially in teaching and learning, many researchers have recognized the interdisciplinary nature of STEM education, but almost no research has focused on the development of STEM education from the perspective of subject integration (Perignat and Katz-Buonincontro 2019 ). The evolution of STEM disciplines and the development of their themes are closely interrelated, but the underlying coupling relationships and reasons for their formation remain unexplored.

Moreover, there exist significant differences in the disciplinary systems of K-12 education and higher education, including teaching objectives, methods, breadth, and depth. As a result, STEM education at different educational levels exhibits distinct characteristics, making it necessary to conduct a segmented analysis. Although some researchers have analyzed the development trends in STEM education from a macro perspective and recognized differences between educational stages, this has not been the primary focus of their work, and there has also been a lack of emphasis on specific disciplines (Zhan et al. 2022a ).

Based on these considerations, this study attempts to examine and explore the developmental trajectories and trends of STEM education at various educational stages from the perspective of disciplinary evolution. Specifically, the following questions will be addressed:

RQ1: How were subjects integrated into STEM education in K-12 and higher education?

RQ2: What is the distribution of the subject themes involved in STEM education at the K-12 and higher education levels?

Keyword search

Papers related to STEM education were searched on 10 July 2023 from the Science Web Core Collection. The query started with the search statement TI = (STEM education) OR TI = (STEAM education) OR AK = (STEM education) OR AK = (STEAM education) OR AK = (STEAM education) OR KP= (STEM education) OR KP = (STEAM education), which yielded a total of 3668 publications. The search results were further refined according to the research area, while duplicates, poorly indexed documents, and documents inconsistent with STEM Education/STEAM Education research were removed, leaving a final total of 2188 publications.

Research process

WOS (Web of Science) was selected as the data source for this study. This database covers a wide range of journals, has a high impact, and can provide a complete sample for this study (Martín-Páez et al. 2019 ). Then, the following steps were used to analyze the data.

Step 1: Data classification (education stage classification). It has been shown that K-12 and higher education systems have different focuses on STEM education (Zhan et al. 2022a ). To clarify the characteristics of the different stages, the data was divided into K-12 and higher education levels based on field information such as title, keywords (including author keywords and keywords plus), journal, and abstract. After discarding the data that could not be categorized, 903 valid data were obtained for the K-12 stage, with the time range from 2009 to 2023, and 873 valid data for the higher education stage, with the time range from 2004 to 2023.

Step 2: Keywords cleaning. In the collected data, some keywords have the same meaning but may be analyzed as different words, such as math, mathematics; model, models, etc., and some words have similar semantics, which may also lead to inaccurate analysis results when analyzed separately, so it was necessary to build a synonym database for synonym replacement so that they could be more accurately counted and visualized.

Step 3: Data classification (time and theme classification). The data at different stages were sub-categorized by time and theme respectively. Time division according to a time slice for a year. The keywords with the top 10 frequency in each subject were screened as alternative theme terms, and the alternative subject terms of each subject were integrated, then the remaining keywords were used as subject terms to participate in the final statistics.

Step 4: Data statistics and visualization. The categorized subject time and subject themes of different sections were counted separately, and the statistical results were visualized and described using heat maps. The heat map used in this study is a kind of statistical chart that shows the frequency of a certain word by the relative shades of color blocks, with dark colors representing the high frequency of occurrence and light colors representing the low frequency of occurrence. Finally, four maps were created to depict the time distribution and theme distribution at the K-12 and higher education levels. The research process is shown in Fig. 1 .

The entire research process went through five stages: data acquisition, data classification (education stage classification), keyword cleaning, data classification (time and theme classification), and data statistics and visualization.

Research findings

Analysis of the temporal evolution of the subject.

STEM education originated from higher education, but in recent years, there has been rapid development in the K-12 stage. Both levels show a similar trend of overall integration, starting with a focus on science, technology, engineering, and mathematics, and later, an increasing involvement of humanities and social sciences. Interdisciplinary integration has become prominent, particularly in higher education. As shown in Fig. 2 .

The first column displays the subjects involved in STEM education, and the first row is the timeline. This figure illustrates the time and subject distribution of STEM-related literature. Darker colors indicate a greater number of documents related to the corresponding time node and subject.

Subject integration of STEM in K-12 Education

Subject integration refers to the methods and processes of cross-fertilization of different subjects, which is specifically expressed as the mutual integration of a subject with one or more subjects through knowledge, concepts, skills, methods, etc. at a certain time node, so time node is one important element of subject integration path analysis. Figure 2(1) illustrates the integration of different subjects at different time points at the K12 level. In the early stages (2009 to 2014), the subjects of science, technology, engineering, and mathematics played a dominant role, and these subjects were considered to be the core of STEM education. Over time, science subjects such as computer science, arts, physics, and environmental science were gradually incorporated into the STEM education integration pathway. In the post-2019 period, more and more research has emerged in the humanities and social sciences.

Different subjects played different roles in STEM education at the K-12 level. Science and technology provided a rich foundation of knowledge and practice for students involved in STEM education. Engineering developed students’ design thinking and problem-solving skills, while mathematics provided the foundation for quantitative and logical thinking. Early STEM education has not yet shown a clear trend of cross-fertilization of disciplines. Science courses, such as physics, chemistry, and biology, were considered the main foci of STEM education, with students exploring basic science concepts through participation in experiments and educational games.

As time went on, computer science and environmental science became important subjects for STEM education, and they facilitated the development of computational thinking and environmental awareness in students at the basic education level (Zhan et al. 2022b ). In 2013, Grover and Pea ( 2013 ) published a study entitled “Computational Thinking in K-12: A Review of the State of the Field”, which explored the importance of including computational thinking as a content and goal of STEM education and had a profound impact on subsequent research regarding the integration of computing into STEM education. In 2022, the U.S. Department of Education proposed “ Science, Technology, Engineering, and Math, including Computer Science ”, also hinting at the importance of computer science in STEM (Department of Education, 2022 ).

Environmental issues have always been important social topics and are closely related to the development of engineering and technology. The integration of environmental science emphasized the importance of environmental awareness and sustainable development, making students conscious of environmental problems and proposing solutions through scientific and technological means. At the K-12 level, researchers have focused on green skills elements in STEM curricula and the integration of STEM educational approaches in environmental curricula (Sümen and Çalisici 2016 ).

After 2019, the integration of humanities and social sciences brought more dimensions and diversity to STEM education. At this stage, STEM education showed a clear interdisciplinary character. Compared to science courses that are involved in STEM education in the form of teaching content, humanities, and social sciences are integrated in a way that is more on the level of research methods and educational philosophy.

Psychological research explored the impact of spatial thinking, spatial skills, and spatial abilities on STEM learning, recognizing the importance of students’ mental states and cognitive abilities for learning (Buckley et al. 2018 ; Gilligan et al. 2017 ; Taylor and Hutton 2013 ). The inclusion of arts enhanced students’ understanding of creativity and encouraged them to use their imagination and creative abilities in the practice of science and engineering (Yakman 2010 ). The inclusion of political science primarily conducted a comparative study of STEM education across different regions from the perspective of policies (Sharma and Yarlagadda 2018 ).

Philosophy created a framework for analyzing and synthesizing STEM education goals and discourses, encouraging students to think deeply about the value and impact of science and technology (Ortiz-Revilla et al. 2020 ). The incorporation of history offered students diverse learning objectives that enabled them to understand the context and social impact of the development of science and technology (Park and Cho 2022 ). The inclusion of linguistics promoted the engagement of culturally and linguistically diverse students in STEM education, encouraging cross-cultural communication and collaboration across linguistic and cultural boundaries (Mallinson and Hudley 2018 ).

In the K-12 stage, there is a significant concentration of disciplines in STEM education, with computer science and arts receiving the most attention alongside the four main subjects. Additionally, interdisciplinary teaching in this stage is guided by conceptual instruction. In 2013, the United States released the milestone document “ K-12 Science Education Framework ”, initiating a major reform in science education. This document became the blueprint for the formal launch of the new era of science education reform known as the “ Next Generation Science Standards ” (NGSS). NGSS proposed a paradigm for science education in the U.S., integrating three dimensions: practices, cross-cutting concepts, and disciplinary core ideas. Seven powerful cross-cutting concepts were selected from these dimensions to bridge the boundaries between different subjects. These concepts include patterns, cause and effect relationships, systems and system models, matter and energy, structure and function, stability and change, and scale, proportion, and quantity (National Research Council 2013 ). The document brought new guidance and direction to STEM education in the United States, emphasizing comprehensive and interdisciplinary educational principles.

Subject integration of STEM in higher education

Figure 2(2) illustrates the time distribution of subjects at the higher education level. Since 2004, a total of 16 subjects have been involved in STEM studies at the higher education level. Similar to the K-12 level, the integration in higher education also shows an intersection of science, technology, engineering, humanities, and social sciences.

STEM subjects (science, technology, engineering, and mathematics) continued to play an important role at the higher education level, covering a wide range of fields of study. Unlike at the K-12 level, STEM education in higher education has exhibited a blend of disciplines at the beginning because of the strong interdisciplinary nature of the courses offered at universities themselves (for example, biochemistry). Students were exposed to more specialized and in-depth knowledge of science, technology, engineering, and mathematics disciplines in their areas of specialization. The focus of disciplinary integration was on combining theories and methods from different disciplines for cross-disciplinary research and innovation. For example, researchers in the multidisciplinary education (ME) course selected undergraduate students in engineering, pre-nursing, and pre-occupational health to collaborate in a maker space to solve health problems and create practical solutions to health-related problems facing the community through their backgrounds and competencies (Ludwig et al. 2017 ).

The development and disciplinary integration of STEM education was influenced by educational reform and societal needs. With the continuous advancement of technology and globalization, there was an increasing demand for comprehensive ability and interdisciplinary thinking. Traditional science and engineering education could no longer meet the current social and professional needs. Therefore, the integration of humanities and social science disciplines has become an important trend in the development of STEM education. For example, art subjects have promoted the integration of innovation and esthetics by providing creative expression and the development of design thinking. The prominence of gender, race, and economic issues, cultural background conflict in higher education has called for the inclusion of social science disciplines such as psychology, economics, and philosophy, linguistics, political science.

In higher education, the distribution of subjects was relatively diverse, with engineering receiving significant attention among the four main subjects. Additionally, chemistry has also been highly regarded, while comparatively, computer science’s involvement is not as prominent.

Comparing the temporal evolution of subjects at different educational levels

In summary, the concept of STEM education was gradually evolving from an initial bias toward engineering education to a more integrated and diverse educational paradigm. Since 2004, there have been 16 subjects involved in STEM (i.e., science, technology, engineering, mathematics, art, physics, chemistry, biology, psychology, computer science, environmental science, linguistics, economics, political science, philosophy, and history). In the analysis of subject integration, the overall integration trend was similar between the K-12 stage and the higher education stage. However, there were still some differences between K-12 education and higher education.

First, STEM education arose in higher education, but there seems to be a trend of research focus shifting from higher education to K-12 education. From 2004 to 2009, STEM research was focused on higher education, and after 2016, the number of papers in K-12 surpassed higher education. The reason for this phenomenon may be that the rise of STEM education sprung from the lack of talent in STEM careers, and higher education was directly oriented to society, so it was reasonable for research and reform to start from higher education, while government policies lead and funding investment largely promoted the rapid development of STEM education in K-12 education stage. Higher education points to the current talent needs of society, while K-12 education points to the future talent needs of society. The inclusion of STEM education in the education strategy of several countries also indicates that STEM talents are an important component of future national competitiveness, so it is very necessary to emphasize the K-12 stage.

Second, at the level of pedagogy and practice, disciplinary integration in STEM education at the K-12 level was often achieved through interdisciplinary projects and activities, such as engineering design challenges, science experiments, and mathematical modeling. These activities were usually classroom-cantered, with teachers guiding students through practice and inquiry. In contrast, in STEM education at the higher education level, disciplinary integration was focused more on the integration of research and practice. Students explored and applied integrated disciplinary knowledge in depth through participation in research projects, hands-on internships, and interdisciplinary courses.

In addition, the concept of STEAM education was more popular at the K-12 stage. “STEAM” was more frequently used in the K-12 stage, which could be said to a certain extent that the STEAM education concept was more popular in the K-12 stage, but may not necessarily indicate a deeper level of interdisciplinary integration in this stage.

Analysis of the evolution of subject themes

Research hotspots are reflected, to some extent, by the frequency of scientific theme terms. In this study, 32 keywords were selected as subject themes at the K-12 level and 33 keywords were selected at the higher education level. To facilitate the analysis, these keywords were grouped into “learning outcomes”, “teachers’ professional development”, “technology empowerment”, “social relevance”, and “pedagogy”. As shown in Fig. 3 .

The first column represents topic categories, the second column contains relevant keywords, and the third row displays the subjects involved in STEM education. This figure illustrates the theme and subject distribution of STEM-related literature. Darker colors indicate a greater number of documents related to the corresponding subject and theme.

Subject theme evolution in K-12 education

Overall, STEM research topics at the K-12 level predominantly emphasize “learning outcomes”, while maintaining a relatively balanced distribution across “teachers’ professional development”, “technology empowerment”, “social relevance”, and “pedagogy”. The dimension of “learning outcomes” primarily encompassed keywords such as students’ academic performance, thinking skills, and associated influencing factors. “Teachers’ professional development” involved aspects related to teachers’ preparedness for STEM education and collaborative efforts among educators. “Technology empowerment” focused on the impact of various technologies such as modeling, robotics, programming, and augmented reality on both the teaching environment and instructional content. “Pedagogy” primarily revolved around inquiry based and game based learning. Furthermore, research related to social themes primarily aimed to foster educational equity from multiple dimensions, including aspects like gender, culture, and policy.

At the K-12 level, the theme of “learning outcomes” account for the largest proportion with 37.69%, under which the theme words included “achievement”, “self-efficacy”, “performance”, “attitudes”, “computational thinking”, “knowledge”, “creativity”, “beliefs”, “design thinking” and “cognitive-load”. In 2009, Obama proposed the “ Competing for Excellence ” initiative, which aimed to improve students’ achievement in STEM. This initiative has led to more researchers exploring different teaching models, activities, and tools to improve student achievement and performance. Also, students’ attitudes, knowledge, beliefs, self-efficacy, and cognitive-load were important factors influencing STEM performance and interest and have received close attention from researchers. Self-efficacy refers to one’s perceived ability to perform specific behaviors that may contain difficulties and stress (Bandura et al. 1999 ). Cognitive load is a multidimensional structure that represents the burden placed on a learner’s cognitive system when processing specific tasks, often appearing alongside keywords like motivation, performance, etc., in educational research with technical support (Kao and Ruan 2022 ).

Computational thinking, creativity (Zhan et al. 2023 ), and design thinking were goals of STEM education and were closely related to the disciplines. Computational thinking (CT) could be seen as a thinking pattern for solving problems with computational tools, and it is a fundamental skill required in everyday life (Wing 2006 ). It has the most direct relationship with computers, and the “ Next Generation Science Standards ” emphasized its significance by considering computational thinking as a core scientific practice. In China, computational thinking is recognized as a core competency in the curriculum standards for information technology. In addition, there is also increasing research focusing on the connection between CT and mathematics (Lv et al. 2023 ). Weintrop et al. defined computational thinking in mathematical and scientific practices using a taxonomy that includes four main categories: data practices, modeling and simulation practices, computational problem-solving practices, and systems thinking practices, which had a broad impact on K-12 education (Weintrop et al. 2016 ).

Furthermore, there was a clear association between creativity and the arts, as well as between design thinking and engineering disciplines. Some scholars argued that creativity plays one of three roles that arts assume in STEM education, with the other two being arts/esthetic learning and contextual understanding (Liu et al. 2021 ). Design is a prerequisite for making and the first step in the formation of STEM work, often found in studies of engineering subjects (Hernandez et al. 2014 ), and design thinking also plays an important role in engineering education, especially in high school (Li and Zhan 2022 ).

“Technology empowerment” (18.91%) was the second most popular theme, with the following themes: “modeling”, “robotics”, “programming”, “augmented reality”, and “scratch”. “Technology empowerment” emphasized the development of student literacy such as information awareness and computational thinking on the one hand, and laid the foundation for students’ STEM education practices on the other. Researchers have explored that robotics education has the potential to cultivate transferable skills in the STEM field (Nelson 2014 ) and narrow the gender gap in STEM, particularly by promoting girls’ learning (Zhong et al. 2023 ). The use of modeling tools can help students visualize abstract scientific and mathematical concepts or objects, which has a positive impact on learners’ academic and personal growth.

In addition, programming is a fundamental requirement for learning computer subjects, and the development of skills related to computer programming and robotics, as well as the introduction of computational thinking principles in STEM education, were considered by researchers as trends in today’s world (Bermúdez et al. 2019 ). AR (Augmented Reality) is the technology that allows virtual objects to be overlaid on real images, enriching students’ learning experiences. AR-STEM research was primarily conducted among K-12 students and typically relies on marker-based AR. However, location-based AR has significant advantages in supporting student learning beyond the classroom and facilitating scientific inquiry-based learning (Sırakaya and Alsancak Sırakaya 2022 ). Scratch is a graphical programming tool. In the K-12 stage, the abstract nature of programming concepts and languages makes it challenging for students to grasp them directly. Graphical programming significantly reduces the complexity of programming, making Scratch widely adopted (Kao and Ruan 2022 ).

The theme of “social relevance” ranked third with 17.53%, with the main themes related to “gender”, “equity”, “culture”, “policy”, “justice” and “patriotism”. Equality has always been an important topic in education, ensuring that individuals of different genders and races can participate in STEM education without discrimination. The Obama administration launched “ the Teach for Innovation program ” in 2009, which aimed to increase access to STEM education and employment opportunities for disadvantaged groups, and has contributed in part to researchers’ attention to gender. The topic of justice was multifaceted, with environmental justice being particularly prominent. Its purpose was to encourage readers to reframe societal and environmental issues as an ethical responsibility, fostering the construction of this responsibility through care, recognition, openness, and responsiveness to both human and non-human vitality (Kayumova et al. 2019 ).

Furthermore, since STEM education was a national priority, many researchers have analyzed the development of STEM education through policy analysis (Zhong et al. 2022 ), particularly focusing on different countries and regions such as South Korea (Park et al. 2016 ), the United States, Europe (Subotnik et al. 2017 ), India, Australia (Sharma and Yarlagadda 2018 ), etc. In South Korea, researchers have combined history education with traditional STEM education to inspire students’ patriotism (Park and Cho, 2022 ).

STEM education originated in the United States, and its evolution is determined by a variety of factors, including national economy, politics, and culture (Zhong et al. 2022 ). As STEM education was increasingly promoted worldwide, it faced challenges of cultural conflicts and international exchanges. “Culture” was a broadly encompassing term, and research about culture could be divided into two categories. First, it served as a research methodology, such as sociocultural theory, exploring social issues like gender and race and aiming at promoting educational equity for students of diverse cultures and languages (Eisenhart and Allen 2020 ).

Second, culture served as the background and content carrier for STEM activities. In China, researchers have developed C-STEAM, or culturally oriented disciplinary integration education, based on STEM education and considering the reality and needs of China’s development. This concept emphasized exploring and creating cultural concepts using related disciplines in the context of traditional Chinese culture, cultivating students’ humanistic spirit, and enhancing their cultural identity and understanding. At the same time, C-STEAM embodied the nurturing value of cultivating students’ core literacy, the carrying value of passing on excellent traditional culture, and the social value of creating a culture with regional characteristics. On this basis, the researcher proposed the ETIC curriculum classification framework and 6 C implementation model, which provided a reference for promoting the construction and development of the regional C-STEAM curriculum. (Zhan et al. 2020 , 2021 ; Huo et al. 2020 ).

“Professional development” ranked fourth with 15.62%. The theme words related were “knowledge”, “professional development”, “attitudes”, “conceptions”, “beliefs”, “teacher preparation”, and “teacher collaboration”. Researchers have indicated that changing teachers to interdisciplinary teaching requires first developing the skills and attitudes of interdisciplinary teaching, and professional development (PD) was considered a key component to helping teachers through this transition process (Al Salami et al. 2017 ). The link between teacher preparation to teach STEM and student STEM achievement has motivated researchers to develop professional development programs to address teacher confidence, attitudes, knowledge, pedagogy, and other preparation issues (Nadelson et al. 2013 ). Understanding the beliefs held by educators was central to influencing change and improving instruction, so researchers needed to be able to design educational programs that address teachers’ beliefs and work to change them when appropriate (Nathan et al. 2010 ; Vossen et al. 2020 ).

Furthermore, there was still considerable uncertainty about “what STEM education is” and “what it means” in terms of curriculum and student achievement, research and discussion on the concept of STEM aimed to create a shared concept of STEM education to facilitate dialog between different stakeholders (Dare et al. 2019 ; Holmes et al. 2018 ). The above topics can all be categorized as preparations for STEM education, primarily referring to pre-service and in-service STEM teacher training. In addition to the mentioned content, this also included language training, relevant technical learning, and teaching methods. Furthermore, due to the interdisciplinary nature of STEM education, collaboration among teachers from multiple disciplines was necessary, especially when humanities and social sciences were involved (Park and Cho 2022 ). Therefore, teacher cooperation was also an important way for teachers’ professional development.

“Pedagogy” received the least attention (10.25%). The theme words related were “inquiry based learning”, “game based learning”, “project based learning”, and “self-regulated learning”. Game based learning demonstrated a close association with technology and computers. Nowadays, students are generally passionate about electronic games, however, they often lack sufficient computer programming knowledge and skills, which limits their development in the computer and technology fields. To address this issue, game based learning has received significant attention in the K-12 stage. The purpose of inquiry based learning was to cultivate students’ inquiry skills, which was also at the core of the science curriculum. In STEM education, this method was considered to have three components: data analysis, interpretive reflection, and critical reflection. Using inquiry based learning could integrate various disciplines, enhance educators’ attitudes, and it’s also suitable for the special needs of gifted students (Abdurrahman et al. 2019 ).

STEM PBL (STEM Project-Based Learning) is a student-centered teaching approach based on constructivism, characterized by clear outcomes and vaguely defined tasks (Capraro and Slough 2013 ). STEM PBL activities are fundamentally interdisciplinary, encouraging students to construct knowledge, identify problems independently, and collaborate to solve them (Han et al. 2015 ). Self-regulated learning (SRL) refers to an active, iterative process in which learners achieve their goals by controlling, monitoring, and adjusting their cognitive/metacognitive processes and learning behaviors. This approach was effective in activating and monitoring learners’ behaviors, cognitions, and emotions, which is crucial for task performance in the STEM field (Li et al. 2020 ).

Through the above analysis, it is evident that research topics in different disciplines have varying emphases. “Achievement” and “gender” were highly popular topics in the scientific community. Additionally, in the fields of math, physics, chemistry, and biology, there was a greater emphasis on “technological empowerment” and “pedagogy”. Technology placed the most emphasis on “modeling”, while computer science was concerned with “computational thinking”. Engineering exhibited a relatively even distribution of research topics. In contrast, the focus areas within humanities and social sciences were relatively scattered.

Subject theme evolution in higher education

In comparison to the K-12 level, research theme distribution in higher education appeared to be more concentrated. This was primarily manifested in the prevalence of research related to “learning outcomes” and “social relevance”, which collectively account for over three-quarters of the total research. Conversely, research areas focusing on “teachers’ professional development”, “technology empowerment”, and “pedagogy” were relatively scarce. However, from a disciplinary perspective, research topics in the humanities and social sciences at the higher education level exhibited greater diversity and richness.

“Social relevance” was the most popular theme in higher education research (47.31%). The research content could be broadly categorized into three types. The first category was educational equity and justice, including keywords “gender”, “identity”, “stereotype threat”, “race”, “equity”, “minority”, and “marginalized populations”. STEM identity is an expressed connection between one’s self and STEM, which depends on the individual’s beliefs about their abilities and their conceptual and practical knowledge of their particular STEM subject (Charleston et al. 2014 ). Enhancing the self-identity of minority groups and optimizing the experience of marginalized populations, especially females, contributed to their more active participation in STEM education. Stereotype threat is a risk experienced by individuals in which individuals fear that they will validate negative stereotypes of the group to which they belong (Spencer et al. 1999 ). Stereotype threat has been shown to have a significant impact on the likelihood of women, minorities, and white men leaving STEM professions (Beasley and Fischer 2012 ).

The second category was students’ career development, including the keywords “career” and “choice”. Career orientation was more prominent at the higher education level than at the K-12 level, with researchers focusing on career goals, career preparation, the position of STEM talent in the labor market, major selection, and attrition.

The third category was culture-related research, which, in higher education, connected with various humanities and social sciences disciplines such as psychology, philosophy, history, linguistics, and more. Research in this category focused on promoting educational equity and students’ full participation in STEM education by addressing the fair treatment of students from different sociocultural backgrounds and using “culturally responsive pedagogy”. This approach involved leveraging the cultural characteristics, experiences, and perspectives of ethnically diverse students to teach them more effectively, fostering educational equity and comprehensive engagement in STEM education (Gay 2003 ).

“Learning outcomes” was also a theme that received a lot of attention in higher education, with 30.47%. The related themes included “achievement”, “performance”, “self-efficacy”, “motivation”, “persistence”, “innovation”, “critical thinking”, “computational thinking”, “creativity”, and “digital skills”. It was evident from this that higher education was not only concerned with issues such as students’ achievement, performance, and computational thinking but also paid attention to influencing factors such as students’ self-efficacy and motivation. How to sustain students in STEM majors and reduce attrition of STEM majors, especially among minority and female populations, was a concern in studies related to “persistence” (Burt et al. 2019 ; Ong et al. 2018 ).

Compared to the K-12 stage, higher education placed less emphasis on computational thinking and creativity but focused more on innovation and critical thinking. Creativity refers to “the generation of novel and useful ideas by an individual or a small group of individuals” while innovation is “the successful implementation of creative ideas within an organization” (Amabile 1988 ). The distinction between creativity and innovation lies in the emphasis on products and outcomes in innovation. Higher education demands that students not only have creative ideas but also successfully transform these ideas into scalable products. In contrast, K-12 education placed more emphasis on encouraging students to generate new ideas. Besides, Critical thinking was another important developmental goal at the higher education level. It served as a method and tool for problem-solving, conceptualized as purposeful, self-regulated judgment involving various thinking skills such as analysis, evaluation, and reasoning (Gadot and Tsybulsky 2023 ).

Digital skill is a concept encompassing skills and specific techniques that are necessary for the use of effective digital technology (van Laar et al. 2019 ). In research, various terms were used to describe the ability to use digital technology effectively in learning activities, such as digital skills, technical skills, digital literacy, digital competence, digital tools, 21st-century skills, ICT literacy, and ICT skills. Studies have shown a positive correlation between students’ digital skills and their creative self-efficacy, and higher levels of digital skills were often predictive of higher levels of actual performance (Chonsalasin and Khampirat 2022 ).

“Technology empowerment” was ranked third with 11.53%, and the related themes were “modeling”, “robotics”, “programming”, “augmented reality” and “virtual reality”. Modeling is a useful tool to identify current problem situations, predict future societal changes, and identify possible solutions (Suh and Han 2019 ). Programming was considered to be related to problem-solving and the main pedagogical challenge was the lack of appropriate methods and tools as well as scaled and personalized instruction (Medeiros et al. 2019 ). Robots were often used in the classroom to develop students’ human-machine collaboration skills (Mathers et al. 2012 ).

Augmented Reality (AR) refers to the technology that enhances virtual information in the real environment through ongoing activities and user input, while “Virtual Reality (VR)” is the technology that immerses users in a purely virtual environment. The learning environments created by VR and AR technologies contributed to the formation of collaborative, interactive, and highly immersive learning experiences, thereby enhancing the efficiency of learning for learners (Zhong et al. 2021 ). Additionally, they demonstrated the potential to help students improve their cross-cultural communication skills (Akdere et al. 2021 ).

“Teachers’ Professional Development” was ranked fourth with 6.11% of the total, and related terms were “faculty training”, “professional development”, and “educational innovation”. Faculty training and professional development were broadly defined terms, and there was a significant degree of overlap in their research content. They encompassed research related to teacher development (such as teacher reflection and active learning), diversity and equity issues among the teaching staff, curriculum design, teaching methodologies, and pedagogical knowledge. Research related to educational innovation encompassed the introduction of new educational technologies, teaching methods, curriculum designs, and assessment approaches to address evolving learning needs and societal challenges.

“Pedagogy” was the least studied topic (4.58%), with related themes including “collaborative learning”, “active learning”, “experiential learning”, “game based learning”, and “positive learning”. Collaborative learning played a significant role in enhancing the likelihood of successful problem-solving. Additionally, collaborative skills are crucial for individuals pursuing STEM careers. Active learning is a method characterized by students taking control of their learning to some extent through metacognition, self-assessment, and reflection, within student-centered and inquiry based learning approaches (National Research Council et al. 2000 ; Kuh 2008 ). The American Association for the Advancement of Science encouraged university science educators to shift their teaching from traditional lectures to active learning (American Association for the Advancement of Science 2011 ).

Experiential Learning is an educational approach that emphasizes acquiring knowledge and skills through first-hand experiences, practice, and reflection, often in forms such as teaching, research, and internships. Experiential learning can facilitate the transfer of classroom learning to real-world practice and has the potential to enhance students’ learning, motivation, skill development, and graduation rates (Gong et al. 2022 ). Game based learning was not very common in higher education, and research in this area was quite scattered, covering topics such as computer-based learning and the creation of diverse and inclusive learning environments. The origins of positive learning can be traced back to the early days of the positive psychology movement, to promote students’ overall well-being, not just the imparting of knowledge and skills, but also the cultivation of their positive psychological traits and qualities (White 2016 ).

Undoubtedly, in higher education, almost all disciplines focused their research on “learning outcomes” and “social relevance”. Among these, the most emphasized areas included students’ performance, diversity, equity, and career development. Furthermore, engineering placed a significant emphasis on programming and robotics technology; mathematics and technology prioritized students’ self-efficacy, motivation, persistence, and programming skills. Chemistry, on the other hand, exhibited a unique pattern by showing less focus on learning outcomes but a greater emphasis on technology integration and pedagogy. The arts concentrated more on technology integration and social relevance. However, many other disciplines lacked a substantial focus on teacher professional development.

Comparing the evolution of subject themes at different educational levels

From the above analysis, it can be found that the distribution of research topics in K-12 education was relatively balanced, while in higher education, it was more concentrated. However, in higher education, research in the humanities and social sciences was more in-depth, and the distribution of themes was more extensive. The research hotspots at the two levels have shown the following differences.

Overall, in the K-12 stage, “learning outcomes” received the most attention, while career education for students was lacking. In higher education, “learning outcomes” and “social relevance” were the most emphasized aspects, while “teachers’ professional development” and “pedagogy” were relatively neglected.

Specifically, concerning “learning outcomes”, achievement, performance, and self-efficacy were common topics across different educational levels. K-12 education placed more emphasis on computational thinking, creativity, and design thinking, while higher education focused more on innovation and critical thinking. Regarding “teachers’ professional development”, higher education paid relatively less attention to teachers and their development, lacking a systematic body of research. In “technology empowerment”, technologies in the research were highly similar, but there was a greater volume of publications in K-12 education. The knowledge or tools learned were also more foundational and straightforward at this level. In the realm of “social relevance” research, gender, equity, and culture were common topics of interest, but higher education delved into students’ career choices and development, an area that lacked emphasis in K-12 education. In terms of “pedagogy” research, K-12 education primarily focused on inquiry based learning and game based learning, while higher education emphasized collaborative learning and active learning.

This study analyzed and compared the development of the STEM research field in two aspects: subject integration and subject themes distribution, to clarify the STEM subject orientation and the ecological map of subject integration in the STEM field.

Referring to RQ1, the subject time distribution maps were used to find out how subjects integrated into STEM education at the K-12 and higher education levels. From the above analysis, it is clear that subject integration followed the evolutionary path of science, technology, engineering, and mathematics to the addition of social sciences and humanities. The addition of the latter has qualitatively improved the connotation of STEM education and fundamentally changed the subject integration path. In other words, the field of STEM studies has expanded from science education to the whole education field, and the cross-fertilization of subjects has become its most fundamental feature. This conclusion has been corroborated by existing research and policies (Perignat and Katz-Buonincontro 2019 ; Zhan et al. 2022a ).

Referring to RQ2, the subject themes distribution maps at the K-12 and higher education levels reflected the main research content of STEM education. Research themes were not evenly distributed, especially since the research on “learning outcomes” was much more than the research on “teachers’ professional development” and “pedagogy”, which implied that the current attention to STEM teachers was insufficient. Previous research indicated that teacher education programs lack content related to interdisciplinary integration across different subject areas and do not provide suitable activities for integrating STEM education (Türk et al. 2018 ). In addition, although K-12 education started late, it has developed rapidly due to the promotion of policies and the future needs of society, but there is still much room for expansion of its research scope, especially career issues. In recent years, with the further development of globalization, student diversity has become evident not only in higher education but also in K-12 education. Research has shown that multicultural education and culturally supportive teaching contribute to addressing the persistent inequalities in the field of STEM education (Charity Hudley and Mallinson 2017 ).

STEM education has obvious interdisciplinary characteristics, in which different subjects play different roles, as shown in Table 1 . The essence of science subjects is to understand the objective laws of the world, and science education aims to help students understand the world through inquiry methods, knowledge is the key to its teaching. The essence of technology is the application of knowledge scenarios, and technology achieves the purpose of transforming the world by manipulating and optimizing the variables that affect the results (products), the key to its teaching is the acquisition of skills. Engineering is the integrated application of technology, and its purpose is also to transform the world, but unlike technology, engineering places more emphasis on the coordination of all elements within the system to find the optimal solution to the problem, and engineering operates and optimizes the variables that affect the system to achieve the purpose of system optimization. The essence of mathematics is measurement and calculation, which develops itself through abstract, non-empirical mathematical operations and heuristic logical deduction, and can provide the logical and calculative basis for other subjects, and the key to its teaching is calculation, measurement, and logical deduction.

Unlike the above subjects, the essence of humanities and social sciences is to feel, interpret, and create the man-made world. It contributes to the all-around development of human beings, the enhancement of moral values and cultural identity, and the development of creative and innovative thinking through the unity of awareness, expression, values, and emotions, the key to teaching is tasting, designing, and creating. In addition, there is a slight difference between the humanities and social sciences. The social sciences involved in STEM fields mainly reflect on the social issues that exist or are raised in STEM education from the perspective of research, but are less reflected in the teaching of the subjects, such as psychology. The involvement of the humanities is mainly reflected in the teaching of the subjects, and the educational goals are achieved through teaching students to appreciate the appeal and value of the arts.

The STEM education research ecosystem comprises two parts. The upper elliptical portion reveals the distribution of disciplines and research topics, while the lower timeline illustrates the timeline of interdisciplinary integration. The central part of the ellipse indicates the disciplinary composition of STEM education. Science, oriented towards exploration, forms the foundation of STEM education. Engineering, driven by creativity and innovation, plays a crucial role in fostering students’ creativity and innovation. Science and engineering mutually reinforce each other and progress together. Technology provides the tools and support for STEM education, while mathematics serves as the computational foundation, collectively facilitating STEM education activities.

STEM education, through interdisciplinary teaching, emphasizes the cultivation of students’ higher-order thinking skills, such as scientific thinking, design thinking, engineering thinking, and computational thinking. The outermost circle includes other disciplines involved in STEM education, such as arts, economics, history, political science, linguistics, psychology, philosophy, physics, biology, computer science, environmental studies, chemistry, and more. This demonstrates the trend in STEM education shifting from STEM to STEAM (Science, Technology, Engineering, Arts, and Mathematics) and the integration of science, technology, engineering, mathematics, and social sciences in education. The pink and blue sections represent the distribution of research topics in the K-12 and higher education stages.

From the above analysis, we could outline the ecological map of STEM subject integration in terms of subject integration and subject themes distribution, as shown in Fig. 4 , which demonstrates the subject integration and main research contents of STEM education.

This figure is composed of two parts, with the upper part representing the content dimension, and the lower part representing the time dimension. The pink area within the ellipse illustrates the most prominent research themes in the K-12 stage, while the blue area illustrates the most prominent research themes in higher education.

Conclusion and future research

Based on the literature related to STEM education in the WOS database from 2004 to 2023, covering 903 papers at the K-12 level and 873 papers at the higher education level, this study conducted a bibliometric analysis from the perspective of subject evolution, including subject timeline evolution analysis and subject theme evolution analysis, to reveal the subject evolution trends and research hotspots in STEM education. The following conclusions were reached.

First, regarding subject integration, the interdisciplinary and cross-subject collaboration in STEM education was constantly expanding and deepening, forming a new situation in which science, engineering, humanities, and social sciences are integrated. Since 2004, a total of 16 subjects have been involved, among them, arts, physics, chemistry, biology, computer science, and environmental science were the main integrated subjects. Interdisciplinary integration promoted the innovation and development of STEM education research.

Second, regarding the research themes, humanism was more and more emphasized in STEM education. In the temporal evolution of subjects in STEM education, it was found that the research outputs of humanities and social science subjects such as arts, psychology, and philosophy kept increasing. The cultural themes have enriched the diversity of participants and the uniqueness of regions in STEM education research, viewed from perspectives such as theory, teaching methods, and regional development. “Social relevance” has garnered significant attention across different educational levels. In K-12 education, research topics were relatively balanced, but there was a lack of research on students’ career choices and development. In higher education, research topics in the humanities and social sciences were more diverse in their distribution.

To sum up, this study analyzed the developmental lineage of STEM education, focusing on the subject roles, and hot topics of research, and summing up potential guidance for subsequent subject integration research. Future work should prioritize the articulation of STEM subject integration between K-12 education and higher education. At the K-12 level, it is necessary to enhance vocational education appropriately, while in higher education, reducing the attrition rate of STEM majors may become a crucial issue. Additionally, attention to multi-discipline teacher collaboration and professional development, high-quality curricula design, and regional policy support should continue to be emphasized. Moreover, different countries present different characteristics in the development of STEM education due to their different cultural, political, and economic backgrounds. In future studies, we aim to conduct a comparative study on the development of STEM education on a country-by-country basis.

Data availability

The datasets generated during and/or analyzed during the current study are available in the supplementary file.

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Acknowledgements

This research was financially supported by the National Natural Science Foundation in China (62277018; 62237001), Ministry of Education in China Project of Humanities and Social Sciences (22YJC880106), the Major Project of Social Science in South China Normal University (ZDPY2208), the Degree and graduate education Reform research project in Guangdong (2023JGXM046).

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Zhan, Z., Niu, S. Subject integration and theme evolution of STEM education in K-12 and higher education research. Humanit Soc Sci Commun 10 , 781 (2023). https://doi.org/10.1057/s41599-023-02303-8

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Women experiencing fear tended to prioritize smaller short-term gain compared to men.

Fear may affect women's decisions in choosing immediate rewards versus larger delayed ones, while men's decisions appear unaffected by emotion, according to a study published March 20, 2024 in the open-access journal PLOS ONE by Eleonora Fiorenzato, Patrizia Bisiacchi, and Giorgia Cona from the University of Padua, Italy.

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Integrating artificial intelligence into science lessons: teachers’ experiences and views

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In the midst of digital transformation, schools are transforming their classrooms as they prepare students for a world increasingly automated by new technologies, including artificial intelligence (AI). During curricular implementation, it has not made sense to teachers to teach AI as a stand-alone subject as it is not a traditional discipline in schools. As such, subject matter teachers may need to take on the responsibility of integrating AI content into discipline-based lessons to help students make connections and see its relevance rather than present AI as separate content. This paper reports on a study that piloted a new lesson package in science classrooms to introduce students to the idea of AI. Specifically, the AI-integrated science lesson package, designed by the research team, provided an extended activity that used the same context as an existing lesson activity. Three science teachers from different schools piloted the lesson package with small groups of students and provided feedback on the materials and implementation.

The findings revealed the teachers’ perceptions of integrating AI into science lessons in terms of the connection between AI and science, challenges when implementing the AI lesson package and recommendations on improvements. First, the teachers perceived that AI and science have similarities in developing accurate models with quality data and using simplified reasoning, while they thought that AI and science play complementary roles when solving scientific problems. Second, the teachers thought that the biggest challenge in implementing the lesson package was a lack of confidence in content mastery, while the package would be challenging to get buy-in from teachers regarding curriculum adaptation and targeting the appropriate audience. Considering these challenges, they recommended that comprehensive AI resources be provided to teachers, while this package can be employed for science enrichment programs after-school.

Conclusions

The study has implications for curriculum writers who design lesson packages that introduce AI in science classrooms and for science teachers who wish to contribute to the development of AI literacy for teachers and the extension of the range of school science and STEM to students.

Introduction

The world is currently undergoing what Schwab ( 2017 ) has called the Fourth Industrial Revolution, which has been characterized by increased connectivity and automation propagated by technologies including artificial intelligence (AI), machine learning (ML), and digital fabrication. In hidden or explicit forms, many lives are now shaped by AI. For instance, AI has been embedded in search engines of online consumer platforms and email (e.g., Google and Yahoo) to market items and promote consumerism (Verma et al., 2021 ). AI has also been applied to agriculture, education, finance, security, science, healthcare, traffic control, crime control, and so on (OECD, 2019 ). While we have become aware of the pervasiveness of AI in shaping human lives, we asked ourselves as STEM educators and teacher educators about our role in empowering learners with the relevant knowledge and skills about AI to thrive in society as literate citizens.

Many scholars and policymakers have argued for schools and societies to place greater emphasis on developing the AI literacies of students. The report Talent for the Future: AI Education for K-12 in Canada and South Korea (Asia Pacific Foundation of Canada et al., 2021 ) has called for the government to make tangible investments in AI education for K-12 students. Such education opportunities should be made equal and be of good quality. Further, AI ethics should form the core and centerpiece of the curriculum (Akgun & Greenhow, 2021 ). For such implementation to be successful, teachers must be enrolled to assist in the materialization of such an endeavor. In South Korea, the Ministry of Education has announced a plan to train 5000 AI-literate teachers through professional development (PD) by 2024 and also promised to develop accredited AI textbooks for elementary schools.

As the implementing agents in direct interaction with students, teachers hold a critical role in achieving the goal of fostering AI literacy among students within their teaching subjects (Casal-Otero et al., 2023 ). However, it seems a tall order for teachers to be able to become AI-literate educators especially when most of them are not trained in AI (Sanusi et al., 2022 ) and their teaching duties are already very intense. This may be because it necessitates significant effort for them to integrate AI instruction into their subjects (Lin & Van Brummelen, 2021 ). History in education reform has informed us about the challenges in onboarding teachers in the implementation of any new curriculum that has occurred for many reasons including time, assessment stress, lack of knowledge in the new topic, and so on (Teo, 2019 ). It implies that a substantial amount of systemic effort would have to be provided to teachers to support their PD in AI literacy. This entails working within existing structures through adjustments rather than revamps that are disruptive to teachers’ work. With this in mind, we embarked on a study to support Singapore science teachers in enacting a lesson package that introduces the idea of AI to students so that they can become more aware of it. This paper reports on the teachers’ experiences with and perspectives on the student learning that took place during the implementation of a curriculum that introduced AI with science. As science teachers, an emergent conversation that came up during the post-implementation dialogue was the relationship between AI and science. Recommendations were provided by the teachers to improve the lesson package for adoption or adaptation by other teachers.

The context of this study

Unlike other education systems such as China, Canada, and South Korea, Singapore has only begun to take small steps in introducing AI to students. Such efforts are typically undertaken by external organizations and industries such as AI Singapore (AISG)®, Google and Amazon web services. For instance, the AI4K® program was developed by AISG to introduce AI literacy to upper primary school children aged 10 to 12. AISG also offers student outreach programs for students in secondary schools and post-secondary institutions. At the time of this study, there were very few existing curricula led by school teachers in regular discipline lessons in Singapore. Hence, AI has often been perceived as an isolated topic pursued by students with special interest in the field. The implication of this is that AI will be taken up by specific groups of students rather than all students. This could potentially go against the grain of efforts that call for equal accessibility to AI literacy for all students. This study recognizes the limitations of such efforts and purposefully identifies a space within the regular school curriculum to implement the AI-integrated science lesson package that we have developed.

The AI-integrated science lesson package was piloted by three science teachers who taught Grade 7 (aged 12 and 13) students in 2022. In Grades 7 and 8 (lower secondary levels) in Singapore, students in the Express and Normal Academic streams (Tan et al., 2016 ) experience the same lower secondary science (LSS) curriculum while students in the Normal Technical stream will experience a different LSS curriculum. The LSS curriculum at this time was aligned to the revised Singapore science curriculum framework (Ministry of Education, 2020 ), which underscores the importance of the practices of science in the teaching and learning of the discipline. Scientific practices encapsulate understanding the nature of scientific knowledge, demonstrating ways of thinking of doing science, and relating science, technology, society, and the environment.

The LSS curriculum is divided into four themes: diversity, models, interactions, and systems (Ministry of Education, 2020 ). Each theme is accompanied with a set of textbooks and activity books covering a few science topics. The activity books comprise practice questions related to the topic units and the key essential takeaways for each theme. The last unit of an activity book is an integrative activity that integrates all the units and the key essential takeaways of the theme.

In particular, in this study we have decided to weave in content about AI in the integrative activity of the activity book on models among four themes. The four topics taught under the theme of models are the particulate nature of matter, atomic structure, the ray model of light, and cells as the basic units of life. The three essential takeaways about models are (1) models are simplified representations of phenomena that provide a physical, conceptual, or mathematical perception of reality; (2) models are constructed to explain phenomena; and (3) models can be used to make predictions.

We approached AISG to collaborate on this study. Specifically, we adapted one set of resources that they had developed to introduce AI using the context of space data. This topic is related to an integrative activity that explores the idea of habitation on Mars. Hence, the AI resource serves as an extension to the existing integrative activity (see Fig.  1 ). The research team developed the lesson package—the structure and details is shown in Table 2 in the Methodology section—and shared it with three science teachers from different schools who participated as research subjects.

AI-integrated science lesson

Research questions

The overarching research question and specific research questions addressed in this study are: What were the science teachers’ experiences and views in integrating AI content into their lessons?

How did the teachers perceive the relationship between AI and science in the AI lesson package?

What did the teachers identify as challenges in implementing AI-integrated science lessons?

What did the teachers recommend for the improvement of the AI-integrated science lesson package and why?

This study aims to examine the three science teachers’ views and experiences in piloting the AI lesson package that we had developed focused on ML, a part of AI. The curriculum was an extension to a unit in the science activity book used by the teachers in their Grade 7 lower secondary science lessons, with the theme focused on scientific models and modeling practices. We were interested in investigating the relationship that teachers perceive between AI and science while enacting the curriculum. When science teachers could make connections between AI and science, they would be more willing to integrate AI into their science curriculum (Kim, 2022 ). However, teacher buy-in to curriculum change could be facilitated if they know in advance challenges to expect and actively engage them in the design and development of the curriculum. Hence, we elicited responses from teachers about the challenges they have faced in implementing the AI curriculum and sought their views on improvements. These comments are incorporated into the revision of the curriculum for future implementation.

Theoretical background

How to integrate ai into the curriculum: designing an ai-integrated curriculum.

Integrating AI into a curriculum involves curriculum designers deciding what and how to teach it in the curriculum (Akram et al., 2022 ; Yang, 2022 ). We have categorized two design consideration aspects for AI-integration curricula derived from prior literature: orientation, which relates to the selection of teaching content, and pedagogy, which involves factors such as teaching strategies and materials. Table 1 illustrates the orientations and pedagogies used for integrating AI into a curriculum in this study.

AI-integrated curricula in previous literature can be categorized into three orientations: AI-focused, discipline-focused, or a combination of both AI and a specific discipline. First, AI-focused orientation aims to foster students’ AI literacy. In schools, it can be a stand-alone subject—such as an elective course—or a separate part of existing disciplines, such as information technology, implying that there is no integration with other subjects. AI literacy is defined as "a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace" (Long & Magerko, 2020 , p. 598). The AI-focused orientation is aimed at designing a curriculum that focuses on educating students to enhance their AI literacy, which includes aspects such as what AI is, how AI works, AI applications, AI tools, and the social impact of AI (Kim et al., 2021 ; Touretzky et al., 2019 ).

The second orientation is discipline-focused. This orientation involves teaching AI within the context of existing disciplines such as mathematics and science. Components of AI taught in this discipline are chosen and arranged based on the themes or core ideas of these disciplines. According to the framework of discipline integration levels by Vasquez et al. ( 2013 ), this orientation corresponds to multidisciplinary integration, which means that students learn the concepts and skills of each discipline separately but within a relevant theme, or interdisciplinary integration, where students can deepen knowledge and skills from two or more disciplines (Casal-Otero et al., 2023 ). This approach could help students understand the connections between the two disciplines while also fostering AI literacy. For instance, Shin and Shin ( 2021 ) created an AI-integrated program for fourth-grade students using Google's Teachable Machine as an AI tool in a plant-classification inquiry project. In this program, students learned both how the Teachable Machine works with the data they input, which relates to AI components, and plant-classification and criteria for the classification activities, which are components of science. Considering that classification is a shared theme of AI and science in this program, students learned about the AI tool in the context of science education. In this orientation, students could learn relevant ideas, skills, and attitudes related to AI that helped enhance their understanding of existing disciplines.

The last orientation focuses on designing curricula that apply AI knowledge and skills from existing disciplines to solving real-life problems. This can be considered a transdisciplinary integration, which is the highest level of integration (Vasquez et al, 2013 ), and involves a genuine context for solving problems relevant to our daily lives. Through these integrated projects, students can apply their knowledge and skills from more than two disciplines in a contextual manner, thereby refining their learning experiences (English, 2016 ). For instance, one of the projects in Akram et al. ( 2022 ) involved developing a contact tracing application using a breadth-first search algorithm, which is a tree data structure for exploring a graph level by level beginning with an initial point and checking all connections (see Beamer et al., 2012 ), to facilitate decision-making around self-quarantine during the COVID-19 pandemic. This project set integrated goals of competency, interest, and career aspirations to be achieved through an AI-infused science problem-solving activity. The development of a recognition model using Google's Teachable Machine to classify recycling materials (Martins et al., 2023 ) also exemplifies this orientation.

In terms of pedagogy, one of the main issues in discussing instructional approaches for AI education is how to deal with the complex and abstract nature of AI knowledge and concepts (Zhou et al., 2020 ). Due to the nature of AI content, this issue has been significantly discussed with reference to various instructional approaches such as collaborative learning, hands-on activity, and inquiry-based learning (Ng et al., 2021 ; Sakulkueakulsuk et al., 2018 ; Sintov et al., 2017 ), which can share similar pedagogical approaches to subject education. For example, social interaction and collaborative group activities have been emphasized to engage students in ML activities (Sperling & Lickerman, 2012 ; Vartiainen et al., 2020 ) and science inquiry activities (Wan et al., 2020 ). Hands-on activities have been reported to be effective in teaching abstract AI concepts to promote active learners when they consider the stages of students’ development of concrete thinking (Williams et al., 2019 ). Similarly, to foster students’ engagement, it has been shown to be effective for students to deal with concrete or relevant data created in their sociocultural context (Sakulkueakulsuk et al., 2018 ; Van Brummelen et al., 2021 ).

Meanwhile, determining which tools provide an appropriate degree of student involvement in managing AI processes is another critical issue of pedagogy in AI education (Zhou et al., 2020 ). Two approaches to address this issue have been considered so far that are contingent upon instructional objectives and students’ developmental stages. The first approach focuses mainly on a problem-solving process based on AI-based solutions (Akram et al., 2022 ; Van Brummelen et al., 2021 ). This approach involves the technical use of AI skills in an AI-integrated activity using simplified programming or block coding programs that allow young students to experience and use data modeling easily (Charters, 2003 ; Lane, 2021 ). The second approach concentrates on teaching AI concepts and knowledge themselves (Hitron et al., 2019 ). Specific pedagogical strategies have been developed to unpack the complex process, akin to a black box, to help learners understand how ML works (Wan et al., 2020 ; Williams et al., 2019 ). There has been no definitive answer as to which tool and approach are better, and they should be determined depending on the objectives, target students, and curricular context.

Given that the curriculum orientations established by teachers are a hidden force that determines curriculum content and teaching method (Cheung, 2000 ), an orientation to form an AI-integrated curriculum can be an essential issue in determining a curriculum’s detailed directions, including curriculum content and pedagogy. In the context of this study, it is essential to consistently establish and clarify the orientation and pedagogy of the AI-integrated curriculum. The AI-integrated lessons in this study focused on a discipline-focused orientation to teach AI in the context of science lessons with the intent of affording students’ opportunities to learn and practice the concept of a model, which overlaps the two fields of AI and science. Concretely, in the aspect of curriculum content, students learned the similarities and differences between AI and science in developing a more accurate model. In the aspect of pedagogy, students were guided to use AI skills technically in their scientific problem-solving processes. This research context was intended to improve understanding of the decisive impact of lesson implementation on the formation of teachers’ perceptions of AI-integrated science classes, which has rarely been examined, as we seek to understand from their perspective and make curricular adaptation successful.

Implementing an AI-integrated curriculum: considerations and challenges

As the uses of AI in education increase, various challenges that need to be considered for AI lesson implementation have been reported. The practical considerations and challenges reported in previous literature can be summarized in terms of teachers’ instructional strategies, professionalism, and support for AI-integrated lessons.

The first challenge is selecting a suitable learning program or activity as one of the key instructional strategies for AI programming or developing AI models appropriate to students’ levels (Van Brummelen et al., 2021 ). Researchers have developed and applied various AI activities, including physical, web-based, and unplugged activities (Zhou et al., 2020 ). In web-based activities, widely used AI platforms, such as Teachable machine, ML for Kids (Lane, 2021 ), and AI Programming with eCraft2Learn (Kahn & Winters, 2018 ), have been generally adopted in AI lessons considering target students’ ages, developmental stages and lesson goals (Williams et al., 2019 ).

Another instructional consideration is how to evaluate and measure student learning outcomes in AI-integrated lessons. Teachers can create assessment standards and evaluate the overall process of problem-solving with AI activities, such as using a checklist and teacher observation of peer interaction, presentation, and discussion (Kim et al., 2021 ). Another way to efficiently measure student outcomes in AI-integrated activities that have been discussed is to systematically create a measurement mechanism to directly calculate the similarity or cohesion of data modeling created by students and offer automatic feedback for students (Wan et al., 2020 ). Either way, teachers need to evaluate students’ processes and outcomes in connection with the goals of AI-integrated lessons.

The more fundamental challenge in dealing with the above considerations is fostering teachers’ professionalism and self-efficacy for AI-integrated lessons. Teachers need to have sufficient knowledge related to AI tools and technologies so that they can understand and effectively use the educational roles of AI (Celik, 2023 ). However, if teachers’ preconceptions about AI remain on an abstract and rudimentary level and they have difficulties using technical terms, they will not have skills sufficient for teaching AI (Lindner & Berges, 2020 ). Teachers themselves have also been reported to perceive that they did not have enough knowledge about AI in designing and implementing AI-integrated lessons (Chounta et al., 2022 ; Sakulkueakulsuk et al., 2018 ). Therefore, increasing teachers’ professionalism and efficacy in AI knowledge, skills, and tools is the most urgent and fundamental task.

Dealing with this urgent task, a new framework of AI-technological pedagogical and content knowledge (AI-TPACK) has been suggested to re-explore the relationship of teachers’ professionalism in technology, teaching methods, and subject content in the AI context (Zhang, 2021 ). Zhang ( 2021 ) argued that in isolation, AI-technological knowledge (TK), AI-pedagogical knowledge (PK), and AI-content knowledge (CK) are all insufficient to help teachers apply AI technologies effectively in their lessons. AI-TPACK knowledge, however, as the combination of the three elements, may be the most practical knowledge base for teachers to integrate AI into classroom teaching. The various perceptions and concrete teaching cases of teachers who integrated AI into science classes in this study can be valuable as basic data for exploring the development paths of teachers’ AI-TPACK.

Even with expertise, however, teachers face several practical difficulties in designing, managing, and implementing AI-integrated lessons. Teachers have been reported to perceive that they needed to be supported with more time and appropriate resources or activities for AI-integrated lessons, even if they were eager to implement AI lessons (Sakulkueakulsuk et al, 2018 ). As one of the ways to support teachers, a guidance chart has been developed and provided to help teachers efficiently find suitable resources for their students (Zhou et al., 2020 ). More supportive ways and practical resources like this need to be considered and provided for teachers.

Given the considerations and challenges described above, we developed and provided an instructional package as a structured AI-integrated science lesson that can be adopted for an enrichment program after-school. The target lessons developed in this study were AI-integrated science lessons for Grade 7 students. The various perceptions and concrete teaching cases of the participant teachers who integrated AI into science classes in this study were then holistically investigated with a focus on the relationships among science content, science teaching, and AI technologies. The practical cases in this study can be valuable as basic data for exploring the concrete ways of teaching AI-integrated lessons and the development paths of teachers’ professionalism in AI-TPACK. We will illustrate more concrete considerations in the Methodology section.

Methodology

This study adopted an instrumental case study design (Stake, 2000 ) to address the overarching research question: What were the science teachers’ experiences and views in integrating AI content into their lessons? Considering the contextual nature of the case study, this study aimed to draw insights into approaches to integrating AI into science lessons based on examining a case of teachers’ teaching practices and their reflections on their practices rather than making a universal claim. For this case study, we provided a “thick description” (Denzin, 2002 ) to illustrate how the teachers perceived the relationship between AI and science, the challenges of implementing AI-integrated science lessons, and recommendations on improvements based on qualitative analysis.

Research participants and data analytic processing

The three science teachers, all in different secondary schools, who will be referred to by their pseudonyms, Tom, Jennifer, and Chris, and their 37 students (10, 21, and 6 students, respectively) participated in the project. The teachers were recruited as personal contacts of the authors. Although the science teachers each had less than five years of experience teaching science in schools and had not been trained in teaching AI, they were interested in this project to learn more about AI-integrated science lessons. All of them taught lower secondary science for grade 7. Additionally, Tom and Jennifer instructed chemistry for grades 9 and 10, while Chris covered physics for those grades. The students joined voluntarily, with parental agreement, after receiving the recruitment advertisement for the project.

The involvement of the research participants was carried out in three stages: (1) an introduction session for the teachers as a PD session; (2) the implementation of the AI-integrated science lesson package; and (3) a reflection session with the three teachers. In the introduction session, the research team provided a detailed explanation of the developed lesson package to the teachers. The introduction session focused on the structure of the lesson package: an overview of the concepts involved in the lesson was given, including the key ideas of ML and a brief introduction on how to use the ML program that will be used in the lessons rather than a systematic elaboration to provide a more detailed understanding of ML concepts. Overall, this PD was conducted with TK, PK, and CK within the context of the lesson structure. After the introduction session, the 3-h AI-integrated science lesson, excluding recess, was implemented after school in the three different schools. The implementations of the three lessons were observed by the authors and audio- and video-recorded and transcribed. The researchers’ field notes and students’ worksheets were also collected as secondary resources to enrich the contextual understanding of what happened in the classrooms (Merriam, 1998 ). After the lesson implementations, the research team held separate reflection sessions with two teachers together (Jennifer and Chris) first and then with the remaining teacher (Tom). The initial plan was for all three teachers to be guided together using the focus-group discussion method (Nyumba et al., 2018 ); however, because Tom was unable to join the session, he had a separate session with the research team with a focus on the raised issues in the first reflection session. The reflection sessions were conducted following semi-structured questions, which were given to the teachers in advance: What do you think are the similarities and differences between AI and science? What do you think is the relationship between AI and science? What were the most challenging components to teach? What parts were difficult to make sense of? What are your comments on improving this package? The sessions were audio-recorded and transcribed.

The corpus of the qualitative data was analyzed using Margot and Kettler’s ( 2019 ) literature review for teachers’ perceptions of STEM education as a framework, enabling us to purposefully investigate the teachers’ perception of the AI-integrated science lesson. The framework comprised three parts: (1) teachers’ views about the connections between science and AI; (2) the challenges of the AI-integrated science lesson package; and (3) recommendations on improvements for future curriculum adoption and implementation. These were then transformed into the three research questions. With these three targeted objects, the data were analyzed using the constant comparison method (Merriam, 1998 ). We began by first analyzing the reflection session data to find emerging preliminary patterns of the teachers’ perceptions of the three focuses. We also incorporated the other data, including lesson recordings, lesson observations and the students’ worksheets so that we could compare and adjust continuously to accommodate new insights. The research team iteratively performed the analysis until an agreement was reached.

Structure of the AI-integrated science lesson package and developmental considerations

Given the discipline-focused orientation of this study and its focus on pedagogies for AI integration, this AI-integrated science lesson package (see Table 2 ) was developed with four segments that were designed with several key considerations to ensure its effectiveness and accessibility to the targeted research participants: the three science teachers and their Grade 7 students. (1) In the first segment, students are expected to be able to come into the lesson with a more fundamental understanding of AI, which would help with the development of lesson ideas during the lesson proper. To accomplish this, two videos created by AISG were selected: one provides a brief introduction to what AI is and a showcase of some common examples of AI, while the other gives an overview of the ML cycle with reference to an analogy of cooking (see Fig.  2 ). (2) The second segment of the lesson package is a set of slides aimed at activating students’ prior knowledge about science ideas and themes that they have learnt in LSS. This segment aims to elicit three key ideas from students: the appreciation of diversity through categorization, the concept of models as representations that can be improved upon, and an understanding of science as a systematic endeavor that occurs via scientific methods. (3) In the third segment, students are introduced to ML through “Quick, Draw!”, an online game developed by Google that showcases the predictive power of ML. Students draw an assigned object while the program guesses what is being drawn, exposing them to the predictive abilities of ML. Afterward, students engage in an activity that helps them understand how they learn, which connects to the idea of how machines learn. In this activity, the teacher guides students to draw a square, prompting the students to reflect on how humans recognize the features of squares and on how the evolution of their thinking is analogous to the ML cycle. (4) In the final segment, students are presented with a scenario where the Mars Rover requires electrical energy. They are tasked with developing a model that can identify how given values of variables can predict either high or low solar radiation so that energy can be harnessed through deploying solar panels. There are various variables that may or may not have an impact on the energy collection efficiency on Mars, with 32,000 data items constituting big data sets. To solve this scientific problem, students build their own predictive model using the software Orange (Demšar et al., 2013 ) after being introduced to the ML cycle and drawing links to scientific methods. This Orange program provides a technological platform that facilitates the generation of predictive models using big data. This program was chosen by AISG to provide general education in AI for students, and we also considered the Orange program to have a level appropriate to Grade 7 students. The hands-on activity is an adaptation of an existing exercise that showcases the different stages of the ML cycle. Upon completion, students reflect on the lesson’s content by articulating their perceptions of how using AI can benefit the study of science and how the study of science can help improve AI.

Analogy of cooking and ML model generated by AISG

We paid close attention to the design of the lesson package for this AI-integrated science lesson in terms of both the design consideration framework (see Table 1 ) and the AI-TPACK framework. First, the package aimed to be simple yet effective and accessible to Grade 7 students while also developing a relational understanding of AI processes. For example, it used a simple definition of AI, referring to AI as having the ability to sense, reason, act, and adapt. Second, the lesson package was built on existing AISG resources but needed to be adapted to better fit the LSS context, including modifying the Mars Rover activity to include more scientific reasoning and discussion. For example, an existing AISG activity involves a data set with the five variables of humidity, pressure, temperature, wind direction, and wind speed on Mars. However, we deliberately split this data set into five sets, with each set missing a different one of the variables but containing the other four, for students to have an opportunity to determine which data set would be most appropriate for training the machine to develop the most accurate predictive model. Considering that most students did not know how to develop a model using the Orange software, we provided steps for them to follow and the rationale behind the steps. We hope that this would allow students to be able to evaluate the best predictive efficacy with a showcase of the predictive ability on an unknown data set in the package. Third, a hands-on activity utilizing the Orange software, designed to solve a scientific problem using an inquiry-based approach, was conducted in a group setting to promote collaborative learning. Last, the lesson package was explicitly integrated with the LSS syllabus and curriculum framework to gain teacher buy-in, with the second and third segments critical in helping teachers draw links between what was taught in LSS and what would be taught in this lesson package.

This study was conducted with the intention of identifying the teachers’ experiences and views based on the introduction session, implementation of the developed AI lesson package, and reflection session. The findings will be illustrated with the teachers’ reflections and the relevant excerpts from their lesson implementations in responding to the research questions accordingly, focusing on (1) the teachers’ perceptions about the relationship (i.e., similarities and differences) between AI and science; (2) the challenges they faced in executing the lesson package; and (3) their recommendations for the improvement of the package.

RQ1: How did the teachers perceive the relationship between AI and science in the AI lesson package?

Similarities between ai and science as a starting point to teach.

The teachers thought that the AI lesson package was a reasonable approach that allowed the students to learn about AI as a tool for doing science based on the similarities between AI and science, such as developing a more accurate model with relevant data and using simplified reasoning. This can be a starting point to teach AI and science together as a discipline-focused approach.

Developing a more accurate model through relevant data

The teachers viewed developing a model based on the relevant data as a similarity between AI and science, although AI and science had different aspects and purposes. The differences will be illustrated in the next section. In particular, they also thought that both AI and science aim to refine a model to be more accurate by adding further quality relevant data. This perception came as a result of the reflection and was not the initial idea of all teachers. In the beginning, Jennifer and Tom were not convinced of the similarity between AI and science of the development of models. Jennifer noted that she did not emphasize this aspect of the nature of science in her previous teaching but rather taught how to build models in science using scientific methods as she answered the question about the relationship between AI and science in the reflection below.

R1: What do you think is the relationship between AI and science? Jennifer: Actually, I thought that it was a bit difficult to find the link between AI and science. Yeah, personally I was not very convinced because I think. … like for example, the iterative approach kind of modifies models as they come along, but. As a teacher of the LSS syllabus, I do not find myself emphasizing all these points.

(Excerpt 1 from the Reflection, Jennifer)

However, she ended up incorporating the idea of the iterative process for refining models more accurately with data as the connection between AI and science in the reflection.

R2: So just to clarify, you are saying that AI involves the building of models and science also Involves it as well? Jennifer: … When we collect more and more data, we refine the model and make the prediction much more accurate than it originally was , and that’s such a more direct way of, you know, just showing to the students that. When we collect more data, we collect more accurate data with the development technology. It helps scientists also refine their hypothesis, refine their models of the real world . R2: I think that is a good point. So, in other words, the kids will also get to see that whether you’re doing the AI work to make a prediction or you’re doing scientific work, both involve working with data and the sample size of the data matters. Jennifer: And the quality of the data matters.

(Excerpt 2 from the Reflection, Jennifer)

She considered both the data and the quality of data, which is crucially important in developing models. She taught this refining model process by providing further data to her students. Excerpt 3 below was what she mentioned in the closing phase of her lesson.

Jennifer: You see whether it’s correct or not, then after that you will change update your model, improve your model so we learn about AI . … Then after that we thought about OK, how do we build an AI system through the machine learning cycle? We identify the problem, we collect data in this case in this lesson. Because of limited time, I gave you the problem and the data.

(Excerpt 3 from Jennifer’s lesson from 2:41:53 to 2:42:22)

Similar to Jennifer, after his involvement in this project, Tom found that the link between AI and science requires a basic understanding of AI to prepare and execute the lesson package. He was able to extend his understanding of AI through the lesson plan, the videos provided for the lesson, and the readings, although this understanding would not be sufficient to use the larger range and types of AI. Excerpt 4 below shows his thought changes on the relationship between AI and science.

R1: What do you think is the relationship between AI and science? Tom: Actually, I viewed AI as quite separate from AI and science. Yeah, but it’s only after I got involved and then we had the Zoom call the other time and then I went to read up more based on the lesson plans and the attached few videos and websites for us to go and visit. And then it’s only after I did all that reading up, then I realized. Actually, there’s the link, and for us as a teacher, the most obvious link is to the team of models. ... You put some data in. It creates a model, and that model can be used to solve problems .

(Excerpt 4 from the Reflection, Tom)

The cases of Jennifer and Tom indicate that we could draw the two possible conditions for recognizing the development of models as a link between AI and science: (1) model-building as a scientific enterprise and (2) a basic understanding of AI, in particular the mechanism of ML. Awareness of these two conditions will enable teachers to be at a starting point for teaching integration of AI and science as a science-focused orientation approach. Since science has various forms of practice, it is necessary to explicitly indicate what aspects of science can be linked to involve AI components as an intersectional area of AI and science. Kim ( 2022 ) discussed the similarities between AI and science in terms of their nature of epistemic processes. Although there are different epistemic aims—science traditionally focuses on knowledge claims while AI focuses on generating solutions and predictions—scientific methods allow for validation and improvement of the intellectual outcomes, such as models, in both AI and science. On the other hand, understanding the basics of AI is another essential prerequisite for teachers who want to employ this approach. Similar to Tom, science teachers generally tend not to have been trained to teach AI in science lessons. It may sound paradoxical since AI and science have different natures, but science teachers can start to teach AI in their lessons when they have an understanding of the similarity between AI and science from a science-focused orientation.

Using simplified reasoning purposefully (or necessarily)

Practically and realistically, it was extremely difficult to teach the overall idea and complex mechanisms of AI to Grade 7 students, considering their expected understanding level of AI and the teachers’ comprehension of the ML mechanism used in the lesson. Finding no other alternative, a simplified explanation of the reasoning of AI, or a “black box”, was used in the implementation of the lesson that involved practicing ML using the Orange program. The statistical method for ML used in the lesson was logistic regression, which is generally not taught until high school. Although the lesson was developed this way, all the teachers seemed to be in agreement on this approach, which means that the idea of using simplified reasoning can be regarded as a commonality between AI and science that teachers can use in progressing the lesson purposefully. Excerpt 5 below shows Chris’s awareness of teaching ML algorithms simply to the students, Jennifer’s response to this matter in science classes, and R2’s aligned example.

Chris: We don’t actually teach them about how the algorithms work and what they do . … Quite honestly, I don’t think students have the prerequisite knowledge—really have a deep understanding of what logistic regression is … as a matter of fact, they just have to do it, except that this AI program or this model that they’re creating is learning , and that’s the word that I use with the, like, you’re feeding it more data, which is learning. Then at the end of it, it makes a prediction. … Jennifer: It’s like a property of sciences. In science, we use a lot of maths, but we only use the results. … It’s so there are a lot of black boxes, I read. I think in science, we do often adopt many tools that were developed by other branches of maths or physics, and we just simply take the solution, and we apply it. … I’m OK with that black box idea . … We don’t really need to because it’s just a tool, and I see the crux of the matter as this tool helps you to answer scientific problems. … we will just have to have faith right in the program that you know is actually able to deliver us support. R2: … We have many black boxes in our learning, right? For example, we bring students to the lab and ask them to use the readings. Do they even know how the biuret is calibrated? Why is it that we should write to two decimal places? … So, there’s a black box in the design of operators as well, right?

(Excerpt 5 from the Reflection, Chris and Jennifer)

As Jenifer and R2 mentioned, using simplified reasoning is a prevailing phenomenon in science classrooms. Science educators are used to focusing on particularly targeted concepts (Wittwer & Renkl, 2008 ) and may marginalize peripheral parts to simplify to help students solve problems purposefully. It may be related to reducing extraneous cognitive load in designing instructions (Sweller, 1994 ). According to the cognitive load theory, content, which is less relevant to the targeted concepts, can be learned with complex information when the need is raised (Pollock et al., 2002 ). This teaching practice also happened in the AI-integrated lesson of this study. Simplified reasoning was used in progressing through the steps of training the program as a scientific modeling method with a large amount of data to focus on developing a predictive model. The simplification of reasoning emerged similarly in the three lessons. Jennifer explained how the data were divided and used in creating a model and testing the model, referring to the guide instruction shown in Fig.  3 . It was a brief idea of an ML algorithm, which was enough for the Grade 7 students. As shown in Fig.  4 and Excerpt 6, the students understood that 70% of the data were used to create a model, while the remaining 30% was used to test the trained model.

Teacher: What you guys just did with the computer program was … the computer program randomly selected 70% of the data in your Excel file and you used it to train. So, 70% of the data was used to create a model. Okay? Teacher: What’s this 30% used for? S1: Prediction. Teacher: What prediction? Why is it that we cannot use all the data? Surely, if we use 100% of the data, it’s better, right? Teacher: Because we just say the more data you give the program, then the more accurate the program will be. Why can’t we use 100%? S2: Accuracy. Teacher: How do we know that the computer program is good? S3: Need a test? Teacher: Need a test? How are you going to test? S3: Use the 30% to test. Teacher: Use the 30% to test, correct? Ss: Yes. Teacher: Ah, OK, so let me summarise.

A screenshot from the AI guide which is a material for the students. The middle column is the description of how to perform the step and the right-hand side column is for the reason for the steps

A screenshot from the lesson when Jennifer was explaining the use of 70% of the data for training a model and the use of 30% of the data for testing the model

(Excerpt 6 from Jennifer’s lesson from 1:50:23 to 1:51:50, Note: S1, S2 and S3 indicate single student’s responses while Ss means many students’ responses.)

This pedagogical approach, which uses simplified reasoning, has been adopted to help younger students learn how AI works (e.g., Wan et al., 2020 ; Williams et al., 2019 ) and was also utilized in the lesson of this study. Through this lesson implementation, the teachers were given an opportunity to think about how science lessons also use simplified reasoning purposefully and necessarily. This does not mean that we should not teach more sophisticated ideas or have a discussion to help students to recognize the simplified reasoning in their learning activities. The important point is that simplified reasoning can be utilized in a particular context, as in the case of this study. If the students have more opportunities to discuss AI algorithms, they will be allowed to refine the simplified reasoning across different contexts. Science teachers’ recognition of this similarity between AI and science can also be a starting point for integrating AI into science lessons.

Differences as a complementary role when teaching the AI-integrated science lesson

The three teachers also perceived that, based on their different natures within the lesson package, AI and science played complementary roles in providing both learning content and a context for that content. Science provided a context for the problem, which in this case was determining the proper conditions to obtain solar energy through the solar panels of the Mars Rover, while AI played a functional role in contributing to drawing a solution to solve the problem. Their thoughts on the different roles of AI and science were important in concretizing how to integrate AI into science lessons.

Science as a contextual understanding that enables judgment of data . The teachers thought that science could help them judge whether data or variables are useful and relevant or not, while AI cannot provide this function. This means that science still provides the focus for what is to be achieved, while AI serves as a supplementary tool in that endeavor. This was a major difference between AI and science in the lesson package. In the reflection, Jennifer and Chris shared their thoughts about science providing a contextual understanding that enables judgment of the usefulness of the data (Excerpt 7).

Jennifer: I wanted to spend more time discussing how the data set produced the best, the most accurate model and it’s going to do scientific variables. So, I guess machine learning or AI, from my current understanding, is that there is so much data. … If actually, we can train a computer to do everything right. So why do we still need scientists? Scientists’ jobs are really to help us to see the data that’s useful and the data that are not . … You know, which are the causes that will lead to this final effect, because we study physics and other sciences. I think that’s the value of the scientific model. … and AI is a tool, right, to help us to supplement that , so, without this kind of basic understanding [of science], it can be not sophisticated and lacks cause-and-effect understanding. Chris: … Scientists actually make decisions about, you know, what is important, what is relevant . For example, I could bring in a variable that has nothing to do with. It’s not going to help the model become better. It might actually make it worse, right? Yeah, so if you want to go in that direction then it might be useful to introduce something like another data set with data that’s actually variables that are not important or variables that don’t actually help the model make better predictions. And then get students to think about why that’s the case.

(Excerpt 7 from the Reflection, Chris and Jennifer)

The teachers’ perception of science’s role can be interpreted as providing the meaning of the variables, which in this study was a contextual understanding of the proper conditions for unfolding the solar panels to get solar energy. An example image of Mars Rover, which has foldable solar panels, was shown in Jennifer’s lesson (see Fig.  5 ). For example, the data of the lesson included many variables such as humidity, wind direction, wind speed, air pressure, and temperature that were provided for creating a predictive model. However, if they did not have a relevant scientific understanding, the numbers in the data set did not provide any meaningful information even when the students trained the program using the data set. The meanings of the numbers were only able to be understood by making links between the numbers in the variables and relevant scientific knowledge guided by the teachers. Based on this contextual understanding, the students could roughly estimate the usefulness of each variable, referring to the result of the model test to solve the scientific problem they were given. This is an important consideration in terms of the roles of AI and science when integrating AI into science lessons.

An example of the image of Mars Rover which has foldable solar panels shown in Jennifer’s lesson

AI as a platform that makes a complex flow simpler with visualizations. AI, in particular ML, focuses on developing algorithms or predictive models through identifying patterns by input data (Jordan & Mitchell, 2015 ). Thus, the meaning of AI in the lesson package should be related to the use of the program that enables the students to develop a predictive model. The Orange program used in the package provided the technological platform used in the lesson for creating a model and testing it with a data set consisting of 32,000 items. The practice of using this program involved the use of logistic regression as a modeling method that requires an understanding of the flow of this complex process. In fact, understanding this complex ML algorithm precisely was difficult not only for the students but also for the teachers, which will be discussed in the next section on teachers’ challenges. Although it was a challenge to understand the AI algorithm used in the lesson, most students and teachers seemed to have understood an overview of the AI algorithm, including training the program for creating a predictive model and testing it to solve the problem. Most students (32 out of 37) correctly answered the questions in the worksheet that asked which data set was the most accurate for the prediction among the five data sets consisting of different combinations of the variables. Figure  6 is an example of one student’s answer. Students’ responses showing that they understood the general idea of the AI algorithm were also observed by the teachers and two researchers in the three lessons. Excerpt 8 shows Tom’s observation about the students’ understanding.

R1: What do you think your students learned from this instructional package? Tom: Based on my students’ responses, right? I think the learning that they had after the lesson, I say the majority, … they roughly know all AI is linked to models. It’s like the order you need to feed data in and then it creates its own model and you can use that model to solve problems . … I think only a very small group of students will go on further and then they’ll think about it. They will go on to think about other problems or deeper about AI, such as the model that they created also depends on the data that you fit.

A student’s response to the questions about finding the most accurate model based on the percentages in the confusion matrix as a result of logistic regression

(Excerpt 8 from the Reflection, Tom)

We thought that the conceptual understanding of the AI algorithm was possible because of its well-visualized flow in the program, which can be seen in the middle column of Fig.  3 . Tom also agreed with this idea, as shown in Excerpt 9.

R1: Do you think there is a benefit in using the Orange program in learning AI algorithms ? Tom: I would say, after doing this Orange program, I do know this general process. Feeding the data into the AI, it analyzes it and it throws out some kind of model and then you see how the model is. … What benefits can I see about this program in AI? … I really don’t know anything else about any other programs, but this program has served the purpose that I was hoping for. …. R1: So, do you mean that although you don’t know the other programs, you know that this program provides some process visually to understand what you are doing? Tom: Ah, yes, that would be the best way to say, yeah, it provides the visual part. Visual understanding of the flow of how the AI creates the model.

(Excerpt 9 from the Reflection, Tom)

What the students experienced in the lesson was actual data modeling that requires a rudimentary understanding of the AI algorithm. Although there were some black boxes in the processes, the teachers thought that the students mostly achieved the goal of the lesson. This is another point we can pay attention to in selecting an appropriate program or platform for AI involvement. For example, using block coding programs, which is a widespread phenomenon in STEM education, affords younger students opportunities to easily experience coding (Charters, 2003 ; Lane, 2021 ). Likewise, in selecting an AI platform for integrating AI into science classrooms, a critical consideration for educators should be its ease of bringing AI skills to younger students.

RQ2: What did the teachers identify as challenges in integrating AI into science lessons?

A lack of confidence in teaching ai to students: content knowledge and pck.

The biggest challenge of the lesson implementation perceived by the teachers was confidence in teaching AI content that was related to understanding (1) content knowledge and (2) pedagogical content knowledge. This result was a general challenge for teachers when they taught AI in classrooms in terms of the relationship between confidence in AI and how to teach it to students (Ayanwale et al., 2022 ). This was mainly because they had been trained as science teachers and may have had no background in AI unless they were personally interested in AI. The three teachers mentioned the challenge of teaching AI to their students during the reflection sessions. Chris shared his challenge in teaching AI (Excerpt 10).

Chris: I think the most difficult part of the lesson, as a teacher, was I might not have like a lot of confidence in explaining it to my students. If I have no background in AI and then suddenly I need to teach them what these different layers are doing, what the confusion matrix is. I also would have some trouble with that, so…

(Excerpt 10 from the Reflection, Chris)

It can be said the teachers’ current concern was that they did not have sufficient knowledge and skills in AI. This challenge is indeed directly related to teachers’ understanding of content knowledge, which is still a major part of teachers’ professional knowledge (Carlson et al., 2019 ). As reported in recent studies of teachers’ perceptions of AI (e.g., Chounta et al., 2022 ), teachers who majored in other disciplines tend not to have sufficient content knowledge of AI. Tom also faced this difficulty and tried to overcome it for the lesson (Excerpt 11).

Tom: I think the most challenging part was reading up about AI . I barely know enough about AI, so to be teaching it, I felt like I needed to know more in depth than what I was saying. In the process I read a few articles on the internet to summarize what AI is, what the different forms of AI are, and also the conversations with all of you over Zoom really deepened my understanding. The AISG website was also quite helpful. This was the most challenging part for me because I was not confident about my content knowledge of AI to teach it, and therefore most of my initial time was spent on developing my content knowledge rather than how to teach it .

(Excerpt 11 from the Reflection, Tom)

This suggests that teachers could make an effort to learn about AI. Teachers may also have pedagogical difficulties in teaching AI that go beyond understanding AI. Although the teachers intentionally taught the AI algorithm in a simpler manner in the lessons, which was illustrated in the first result, this could also cause teachers to have concerns about their pedagogical approach. Jennifer expressed her challenge in teaching the steps of using the Orange program (Excerpt 12).

Jennifer: It’s back to the part where I mentioned how I chose to dedicate quite a big part of my lesson to going through the rationale of the different steps with the students ... To have clarity of the rationale of doing certain steps in the Orange program, I also led the students into why is it that you want to do certain steps ... I did it on the spot, I also didn’t feel I did a very good job explaining it. Yeah, so I think that was the most difficult challenge.

(Excerpt 12 from the Reflection, Jennifer)

Jennifer was challenged to teach the rationale of each step in using the Orange program. After teaching them, however, she realized that she did not teach the steps well even though she spent significant time on the lesson. She might want to know efficient strategies for how to teach the simplified version of AI to the younger students. One of the common challenges when employing new approaches in classrooms is pedagogical challenges, such as how teachers step up and establish classroom environments in STEM education (Margot & Kettler, 2019 ). Likewise, integrating AI into an existing discipline such as science, as was the case in this study, may also lead teachers to question how to implement AI in their classrooms.

AI as a supplementary component of the current curriculum: temporal and audience issues

The teachers thought that teaching AI could be a supplementary component for science teachers, although not yet an essential one, that can be added to the existing curriculum. As mentioned above, science teachers are trained to teach science, so they may perceive AI as an additional layer on top of science even though there are some commonalities between AI and science. Considering AI a supplementary component would be relatively relevant to a discipline-focused orientation (Kim et al., 2021 ) in integrating AI into science lessons. Excerpt 13 shows how Tom perceived this integration.

Tom: I would say AI is a supplement. It’s like an extra thing, adding on . Not as something that we use to teach other things. We used it to teach scientific concepts. Yeah, .. maybe we should teach the science concepts first when everything is OK and ready, then teach about AI . … I think that’s more possible.

(Excerpt 13 from the Reflection, Tom)

Tom’s statement also raised a temporal issue in implementing the integration of AI into science lessons. He seemed to have assumed that AI can be taught from the commonality of AI and science, in particular, creating a more accurate predictive model—which was shown in Excerpt 4 in the first result. Speaking in this context, the AI component can be introduced after learning the features of scientific models in a science subject to refine students’ understanding of AI as a cutting-edge method of science. For example, climate modeling can be an exemplary topic for using AI as a scientific method as it is used in developing more accurate models to predict future weather, which requires a huge amount of historical data related to weather (e.g., Barnes et al., 2019 ). Naturally, Tom’s perception might stem from a teacher’s primary role: teaching science to achieve curriculum goals. When accomplishing the primary goal, teachers will then be able to teach AI by adding it on to the existing curriculum, as Tom mentioned. This teachers’ prioritization of teaching science may cause employing the approach of AI integration with science lessons to be more challenging.

Another issue in integrating AI is targeting the audience. Since up to present there has been a dearth of empirical evidence on which grade and profile of students are most appropriate for teaching AI, targeting an audience can be a conundrum when integrating AI into science lessons. The teachers in this study felt that implementation of the AI lesson package would be appropriate in a science enrichment program for students who are interested in science (Excerpt 14).

Jennifer: I think at least my target audience was the bunch of people who I recruited for the lesson. The students were all part of the science enrichment program, so they were already the bunch of students already were very interested in science and did quite well in science. They have an inherent curiosity. Yeah, .. something, so I think it’s a tall order. ... you can never cater to all student profiles, ... I’m not sure, but I would propose that maybe you just cater to, like, a science enrichment program. People who already are a little bit interested in science .

(Excerpt 14 from the Reflection, Jennifer)

All three teachers agreed that at this point integrating AI into science is more suitable for students who are interested in science rather than all students. Their thoughts may have been more related to integrating advanced AI knowledge, such as logistic regression, which was used in the lesson package. However, realistically, it can still be challenging for teachers who are targeting all students, given teachers’ difficulties in understanding both content knowledge and pedagogical content knowledge and their main focus on achieving curriculum goals. For a similar reason, a mathematics-focused AI subject in South Korea has been developed as an elective course for Grade 11 and 12 high school students (Ministry of Education, 2022 ). This curriculum was developed to foster students’ mathematical competencies based on an understanding of the utilization of AI in mathematics. Likewise, if advanced knowledge of AI is integrated into science lessons, it can be suggested for students who are more interested in an enrichment program or an elective course. On the other hand, it's obvious that simpler AI concepts, which may not involve complex processes, should be further explored for integration into existing disciplines. This is particularly relevant as the breadth of AI literacy curricula (e.g., Touretzky et al., 2019 ) expands to reach a wider student audience, given their significance.

RQ3: What did the teachers recommend for the improvement of the AI-integrated science lesson package?

Improving teachers’ ai literacy and implementing the ai-integrated lesson package for an enrichment program.

The teachers’ recommendations for improvement stemmed from their challenges with the AI lesson package implementations, focusing mainly on (1) teachers’ AI literacy and (2) positioning of AI-integrated science lessons practically as an after-school program for students. The first suggestion was to provide comprehensive resources to support their understanding of AI content knowledge, because their biggest challenge was understanding what AI is and how to train an AI model and interpret the results of the trained model. Since science teachers are not generally trained to utilize and teach AI, they struggled with it. In general, teachers may need further AI literacy to teach AI to their students (Ayanwale et al., 2022 ). The following shows what Jennifer mentioned for the improvement of the package (Excerpt 15).

Jennifer: I’d like a kind of introduction to AI for teachers , not for students. So, I think teachers will appreciate greater clarity on what AI is. I seriously think that the main barrier to preventing buy-in from teachers is the lack of content mastery.

(Excerpt 15 from the Reflection, Jennifer)

The second recommendation was employing this AI lesson package as an enrichment or after-school program. The teachers thought that this program would be more appropriate for students interested in science, based on their observations in the lesson implementations (Excerpt 16).

Jennifer: I would propose that maybe you just cater to like a science enrichment program . People who you know already are a little bit interested in science, and you know there’s just like additional interest because I think if you want to push it out to like … Chris: I think, as Jennifer mentioned, having this for maybe a selected group or of students who are in the science enrichment or like a science talent program . And I, I agree. I also think that this works best with that profile of students.

(Excerpt 16 from the Reflection, Jennifer and Chris)

Although the teachers observed that their students for the most part achieved the goals of the lesson, they might feel the difficulty level would be high if this lesson was executed in general science classrooms. However, they perceived that this AI-integrated lesson could be applicable to Grade 7 students—Secondary 1 in Singapore—if they are interested in science (Excerpt 17).

R1: Which school grades, such as Secondary 1 to 4 or would be most appropriate for this instructional package? Why? Chris: Appropriate for Secondary 1 students . However, if we want to focus more on the AI model architecture, it would only be possible in upper secondary [which is Grades 9 and 10] because of the prerequisite math knowledge.

(Excerpt 17 from the Reflection, Chris)

Since the AI lesson package did not require a precise logical understanding of logistic regression, Chris thought that it would be appropriate for Grade 7 students as a science enrichment program.

Towards actualization of AI-integrated science lessons as an interdisciplinary integration

To expand the presence of AI-integrated science lessons in more schools, it is important to compile resources into accessible teaching materials and to bolster teachers’ improvement of their capabilities and confidence. Drawing upon teachers’ perspectives from this study and existing literature, we will discuss the organization of resources and teacher support in this section to actualize AI-integrated science lessons. First, organizing resources for an AI-integrated science curriculum may consider (1) identification of common themes between AI and science; (2) selection of a suitable program; and (3) data appropriate for the selected program. Resources, including lesson plans and teaching materials, could be co-designed around themes shared between AI and science. As demonstrated in this study, the development of more precise models through relevant data is a key concept shared between AI and science. Identifying a connection between AI and science could be the first avenue to deepen our understanding of how to integrate them using an interdisciplinary integration approach (Vasquez et al., 2013 ), which was applied in this study. The selection of a tool for teaching AI is another critical pedagogical consideration (see Table 1 ) in designing a curriculum, as indicated in the Theoretical Background section. The tool used in the lesson package was the Orange program, which offers a significant advantage in visualizing complex data modeling processes in ways understandable to both students and teachers. Since it may influence the range and complexity of AI content knowledge taught within the context of science, the tool should be carefully chosen considering the common theme and the tool's features (Ng et al., 2021 ). The data used for teaching AI should also be arranged in advance to coincide with the development of the AI-integrated curriculum. In this study, the data set was sourced from AI resources through a partnership with AISG. For science teachers, involving other educational institutions with AI experts may be more feasible than developing the data set themselves at the initial stage if the curriculum needs big data.

These three points can also align with the approaches of other AI-integrated science programs. For instance, classification inquiry in science and Google’s Teachable Machine as an AI tool have a common theme—classification—and they have been arranged together in AI-integrated science curricula in several studies. Since Google’s Teachable Machine is simple enough for younger students to grasp the basic concept of machine learning (Sanusi et al., 2023 ), this tool has been utilized in several AI-integrated science lessons. For instance, as cited in the literature, Shin and Shin ( 2021 ) used Google’s Teachable Machine as an AI tool in teaching plant classification, integrating AI teaching into an online learning environment. In their study, they collected plant images in advance, using them to train and evaluate the machine. This approach reduced students’ difficulties and ensured the quality of the data. With this in mind, when organizing resources for an AI-integrated science curriculum, teachers should intentionally consider these three points: common theme, program, and data, especially given the importance of tailoring the curriculum to their specific context (Dai et al., 2023 ; Lin & Van Brummelen, 2021 ).

Supporting teachers in actualizing AI-integrated science curricula is essential because they play a crucial role in implementation. However, many are not generally trained in teaching AI. Therefore, it can be beneficial to provide continuous PD programs for teachers to learn how to teach AI-integrated science curricula. Furthermore, collaborating with AI experts such as scientists or researchers for PD would be beneficial (Dai, 2023 ). According to the concerns-based adoption model (CBAM; Hall & Hord, 2013 ), which is a framework that indicates the level of teachers' engagement in implementing a new pedagogical approach (Ohlemann et al., 2023 ), teachers' main concerns typically progress through three broad stages over several years. Teachers begin by increasing their awareness of what the new approach is, its requirements, their potential roles, and the potential rewards and conflicts (self stage). They then move on to concerns about managing and implementing the new approach (task stage) and finally focus on the approach's outcomes, how to collaborate with colleagues, and how to refine the approach (impact stage). Given this model, continuous PD, encompassing both mastery of AI content and how to teach AI-integrated science curricula, should be offered to interested teachers over the years, bolstering their capabilities and confidence (Ayanwale et al., 2022 ).

In addition to continuous teacher PD, encouraging an environment that allows teachers to collaborate with other teachers and experts should also be considered. As indicated by the CBAM model (Hall & Hord, 2013 ), collaboration with colleagues is necessary to ensure successful outcomes from the implementation of the new approach. Regarding teachers' perceptions of STEM program implementations, research has shown that teachers believe collaborations increase the viability of these programs (Margot & Kettler, 2019 ). Similarly, AI-integrated science curricula can be more effectively executed through collaborative work among teachers. As found in this study, the three teacher participants also shared their experiences and challenges through reflection sessions. On the other hand, providing well-designed AI-integrated science programs to teachers is also important. Seeing the benefits of STEM programs for their students, which in turn influences teachers’ beliefs about educational practices, helps motivate them to implement innovative programs in their classrooms (Van Haneghan et al., 2015 ). Likewise, in order for teachers to recognize the value of AI-integrated science curricula, the development of various high-quality programs is needed.

Engagement of epistemic practices of AI and science in AI-integrated science lessons

Teachers may utilize “black boxes” as a way of involving simplified reasoning in educational contexts, as illustrated in the Findings section, when they teach complex concepts (e.g., AI algorithms) at the beginning stage of AI-integrated science lessons. Since AI algorithms and other difficult concepts can be pedagogically simplified because of their complex nature, which would be a barrier to teaching, teachers may need to use black boxes in classrooms. However, this is fundamentally connected to the matter of epistemic practices—how we recognize the process of knowledge construction—which have become increasingly important in the information age, where knowledge production has been extensively enlarged in terms of credibility (Aradau & Huysmans, 2019 ). In this regard, although teachers purposefully use simplified reasoning at the initial stages, they may move on to unpacking the black boxes incrementally for students’ better learning (Haskel-Ittah, 2023 ).

Teachers’ use of unpacked black boxes in their lessons indeed relates to epistemic practices of AI and science. Although AI and science demand evidence-based high-quality outcomes involving inherent possibilities of error (Kim, 2022 ), they differ relatively in the transparency of the processes in school settings. Traditionally, scientific inquiry in classrooms has been done through the human agency of teachers and students, which has given more opportunities for epistemic practices. On the other hand, AI, and in particular, ML, has uncovered processes of big data modeling that lack transparency for teachers and students, which may cause trustworthiness issues. To overcome this transparency issue in ML, a key research area in AI, such as explainable machine-learning challenge (Rudin & Radin, 2019 ), is essential, as teachers should be able to manage the black box issue meticulously when integrating AI into science lessons. For example, during the implementation of this study’s lesson package, it was necessary for the teachers to ask what kinds of data sets could make the model more accurate, allowing their students to unpack the black-boxed ideas associated with the relevant and quality data in the context. Indeed, in cases of appropriate educational contexts, such as a more knowledgeable group of students and aligned lesson objectives with features and data quality related to AI ethics, teachers can pose more in-depth questions related to reliability and validity. They can ask about the timing and methods of data collection, the magnitude of measurement errors, and so forth. This aspect was not the focus of this study, which is a limitation, suggesting the need for further research.

In this study, the teachers were able to appropriately use simplified reasoning with a program that shows a visualized flow of the modeling process. Consequently, the students achieved the goal of understanding a simplified idea of the ML algorithm and found the most appropriate data set to develop a more accurate model in the lesson, as shown in Fig.  4 . This result shows that the students were able to come to a simplified understanding of the ML mechanism involving big data when supported by various resources. Likewise, even though starting with simplified reasoning can be done, teachers can transition to more in-depth discussion with their students for more meaningful learning experiences (Haskel-Ittah, 2023 ).

Conclusion and implications

This study investigated the teachers’ experiences and their views in a case of AI integration into science lessons. We drew two implications through this study that may provide: (1) an empirical case in designing AI-integrated discipline-focused curricula and (2) a consideration for refining the AI-integrated TPACK framework. There has been increasing attention to teaching AI content in schools because the usage of AI will likely be a fundamental skill in the future along with existing literacies such as reading, writing, arithmetic, and digital skills (Ng et al., 2021 ). Although there has been an incremental number of empirical studies on teaching AI content using AI-focused orientations (e.g., Su et al., 2022 ), few studies have examined AI integration into existing disciplines, which is an area that needs to be actively explored so that AI can be used in school settings. In this regard, the case of this study could provide an empirical example of how to integrate AI into science-focused lessons involving Grade 7 students and their science teachers. As in this study’s approach, exploring the relationship between AI and the existing discipline of science can be a starting point for integrating science and AI in designing curricular or lesson packages. In particular, as presented in the results of this study, it may be possible to teach the similarity between AI and science, which involves developing a more accurate model that enables the recognition of patterns from data at the beginning. Designing lessons can be followed up by situating AI and science as functioning in their complementary roles in the integration. It can then be helpful for educators in developing curricula to teach basic ideas on how AI works and why AI should be integrated into science as an existing discipline, encompassing a broader range of topics that may involve cutting-edge scientific methods involving a huge amount of data.

This exploratory study also provides consideration for refining the AI-integrated TPACK framework in consideration of the needs of an educational context where AI literacy has been increasingly emphasized in classrooms, where there is a need for interdisciplinary consideration that involves changes in each component for refining the AI-integrated TPACK model (Koehler & Mishra, 2009 ). This means that changes in the integration will happen not only to pedagogical and technological dimensions (i.e., PK, PK, and TPK), but also to content-related dimensions (i.e., CK, TCK, and TPACK) because AI enables the expansion of a range of existing disciplines in schools such as science and STEM, including their methodologies, as discussed. The current TPACK framework developments have had two different directions for involving AI technology: (1) as a supportive tool for teaching and learning and (2) as content to teach. Koehler and Mishra’s ( 2009 ) TPACK mode, as shown in Fig.  7 (left), corresponds to teachers’ capabilities in using AI technologies in ways such as identifying student performance and automated grading. On the other hand, related to AI literacy, Ng et al. ( 2021 ) suggested the AI literacy TPACK framework, as shown in Fig.  7 (right), which involves AI awareness, the use of AI ethics, and other key ideas of AI itself such as the five big ideas: perceptions, representation and reasoning, learning, natural interaction, and societal impact (Touretzky et al., 2019 ).

The TPACK framework (on the left; Koehler & Mishra, 2009 , p. 63) and AI literacy TPACK framework (one the right; Ng et al., 2021 , p. 5)

However, so far, there has been less attention paid to the discussion of how the content of existing disciplines would be changed. Since AI-integrated TPACK involves the interactive relationship among teaching strategies, subject matter, and AI technology, which is beyond the TPACK framework (Zhang, 2021 ), adding AI components into the TPACK framework may require an understanding of how AI can be utilized as a tool for teaching, and AI as content to teach involves a further area of content knowledge, as shown in the findings of this study.

AI is not merely a technology being used for convenience: it also has enormous potential to change the world as part of the digital revolution (Makridakis, 2017 ). It essentially requires the need for educational innovations involving integrating AI technology to respond to the changes in society. In addressing the importance of AI literacy for learners, as it can be one of the fundamental skills in the future (Ng et al., 2021 ), this study explored a possible way to integrate AI into science lessons and investigated how teachers perceived the AI-integrated science lesson package in terms of practical considerations such as general science teachers’ current understanding of AI, the science curriculum, and students’ engagement. Starting with recognizing a need to integrate AI into existing disciplines in classrooms, which involves many challenges, this study aims to contribute to the further development of AI integration to develop students’ AI literacy and learning an extended range of science subjects and STEM education using varied epistemic practices.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to privacy protection of the participants but are available from the corresponding author on reasonable request. The materials for the AI-integrated science lesson package, including the video clip 1 ( https://www.youtube.com/watch?v=OyznIW3GGaM ), video clip 2 ( https://www.youtube.com/watch?v=9fv-TatdW4g ), and AI guide for students ( https://drive.google.com/file/d/1VAr9S3KPEcteE7vlRXUBDG8TIEAIAU9d/view ), are made available via the links.

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Acknowledgements

This research/project is supported by the National Research Foundation, Singapore, under its AI Singapore Programme. AI Singapore® provided the resources for developing the lesson package including the two video clips for pre-lesson primers, and the data for modeling. We would also like to extend our heartfelt appreciation to the three teachers and their students who participated in this study. Additionally, our gratitude goes to the developers of the Orange Program, an open-source, free-licensed software that played a vital role in the AI-integrated science lesson. The software is freely available at https://orangedatamining.com/ .

This paper refers to data from the research project ‘Drafting Artificial Intelligence (AI) literacy curriculum materials with STEM education’ [PG 13/21 PJH], funded by the Education Research Funding Programme, National Institute of Education (NIE), Nanyang Technological University, Singapore. The views presented in this paper are those of the authors and not the institution.

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JP (PI), TWT (Co-PI) and JSH (Collaborator) acquired funding and supervised the research team and were responsible for the conceptualization of this study. The instructional package for this study was developed by JP, TWT and AT, advised by SK (Collaborator). Data analysis was performed by JP, TWT and JC. The primary manuscript was mainly drafted by JP—theoretical background, methodology, findings, discussion and conclusion and implications, TWT—introduction and methodology, AT—methodology, and JC—theoretical background. All authors read and approved the final manuscript.

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Park, J., Teo, T.W., Teo, A. et al. Integrating artificial intelligence into science lessons: teachers’ experiences and views. IJ STEM Ed 10 , 61 (2023). https://doi.org/10.1186/s40594-023-00454-3

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• Artificial intelligence (AI)
• Teacher perception
• AI-integrated lessons
• Science lessons

Integrating Science and Mathematics in Elementary School: Impact on Selected Student Perceptual Variables

• Published: 29 June 2023
• Volume 22 , pages 837–860, ( 2024 )

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• Simon Langlois   ORCID: orcid.org/0000-0001-9565-3714 1 ,
• Nathan Béchard 2 ,
• Guillaume Poliquin 3 ,
• Stéphane Cyr 4 &
• Patrice Potvin 4  

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Integrating science and mathematics in the elementary classroom is a significant challenge, as evidenced by the few convincing examples currently available in the scholarly literature. Yet, this integration is often seen as presenting epistemological and pedagogical value. This study aims to measure the impact of science and mathematics (S&M) learning situations on certain perceptual variables, namely, self-efficacy (SE), interest, and perceived S&M links, by comparing three experimental conditions: (0) control , (1) science , and (2) integration of S&M . Multilevel linear regressions using data from the Science and Mathematics Integration Questionnaire (SMIQ) suggest that making mathematics explicit in science learning situations (2) or keeping mathematics instruction implicit (1) significantly increases SE in science and SE in mathematics compared to the control condition (0). Further analysis suggests that there were significant a posteriori differences between conditions in students’ ability to perceive the links between science and mathematics, with the highest score being for S&M and the lowest for control .

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Data Availability

The data that support the findings of this study are available from the corresponding author, S.L., upon reasonable request.

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Acknowledgements

The authors wish to warmly thank Viviane Desbiens, research assistant, for her help during data collection and analysis.

This work was supported by the Social Sciences and Humanities Research Council (SSHRC) under grant 890–2015-2005 of the Community and College Social Innovation Fund (CCSIF).

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Simon Langlois

Université Laval, Quebec, Canada

Nathan Béchard

Collège Ahuntsic, Montréal, Canada

Guillaume Poliquin

Université du Québec à Montréal, Montréal, Canada

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Scientific measurements.

Introduction

The pedagogical intent of this activity is to cleverly solve a set of problems (challenges) using scientific measurements. In order to do so, the student must use various instruments (e.g., dropper, graduated cylinder) appropriately.

Several concepts suggested by the Ministry of Education of Quebec for the third cycle of elementary school in Québec, Canada are included in this activity. Among these (non-exhaustive list) are the proper use of simple measuring instruments in science and in mathematics the meaning and writing of numbers ( approximating and associating a fraction with a part of a whole or a group of objects ).

Teaching sequence

As described in the “ Methods ” section of the article, the activity takes place in three stages: the review of mathematical prerequisites , the realization of the challenges , and the objectivation .

Review of mathematical prerequisites

Before the activity, the teacher ensures that students have mastered the transition from fractional to decimal notation, as well as the conversion of capacity units (liters to milliliters and vice versa). If needed, activity sheets are suggested to do so.

Application of mathematical knowledge during the learning situation

Students must solve a total of six challenges. The first two challenges are mostly done during plenary sessions, while challenges 3 to 6 are done in pairs.

Objectivation

The contribution of mathematics to the completion of the challenges is discussed. Possible questions the teacher could ask include: What mathematical content was used? Would it have been possible to do the experiments without using mathematics?

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Langlois, S., Béchard, N., Poliquin, G. et al. Integrating Science and Mathematics in Elementary School: Impact on Selected Student Perceptual Variables. Int J of Sci and Math Educ 22 , 837–860 (2024). https://doi.org/10.1007/s10763-023-10390-x

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Received : 06 September 2022

Accepted : 22 May 2023

Published : 29 June 2023

Issue Date : April 2024

DOI : https://doi.org/10.1007/s10763-023-10390-x

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