Understanding Creativity

  • Posted June 25, 2020
  • By Emily Boudreau

Teens with laptops and a chalk drawing of lightbulb

Understanding the learning that happens with creative work can often be elusive in any K–12 subject. A new study from Harvard Graduate School of Education Associate Professor Karen Brennan , and researchers Paulina Haduong and Emily Veno, compiles case studies, interviews, and assessment artifacts from 80 computer science teachers across the K–12 space. These data shed new light on how teachers tackle this challenge in an emerging subject area.

“A common refrain we were hearing from teachers was, ‘We’re really excited about doing creative work in the classroom but we’re uncertain about how to assess what kids are learning, and that makes it hard for us to do what we want to do,’” Brennan says. “We wanted to learn from teachers who are supporting and assessing creativity in the classroom, and amplify their work, and celebrate it and show what’s possible as a way of helping other teachers.”

Create a culture that values meaningful assessment for learning — not just grades

As many schools and districts decided to suspend letter grades during the pandemic, teachers need to help students find intrinsic motivation. “It’s a great moment to ask, ‘What would assessment look like without a focus on grades and competition?’” says Veno.

Indeed, the practice of fostering a classroom culture that celebrates student voice, creativity, and exploration isn’t limited to computer science. The practice of being a creative agent in the world extends through all subject areas.

The research team suggests the following principles from computer science classrooms may help shape assessment culture across grade levels and subject areas.

Solicit different kinds of feedback

Give students the time and space to receive and incorporate feedback. “One thing that’s been highlighted in assessment work is that it is not about the teacher talking to a student in a vacuum,” says Haduong, noting that hearing from peers and outside audience members can help students find meaning and direction as they move forward with their projects.

  • Feedback rubrics help students receive targeted feedback from audience members. Additionally, looking at the rubrics can help the teacher gather data on student work.

Emphasize the process for teachers and students

Finding the appropriate rubric or creating effective project scaffolding is a journey. Indeed, according to Haduong, “we found that many educators had a deep commitment to iteration in their own work.” Successful assessment practices conveyed that spirit to students.

  • Keeping design journals can help students see their work as it progresses and provides documentation for teachers on the student’s process.
  • Consider the message sent by the form and aesthetics of rubrics. One educator decided to use a handwritten assessment to convey that teachers, too, are working on refining their practice.

Scaffold independence

Students need to be able to take ownership of their learning as virtual learning lessens teacher oversight. Students need to look at their own work critically and know when they’ve done their best. Teachers need to guide students in this process and provide scaffolded opportunities for reflection.

  • Have students design their own assessment rubric. Students then develop their own continuum to help independently set expectations for themselves and their work.

Key Takeaways

  • Assessment shouldn’t be limited to the grade a student receives at the end of the semester or a final exam. Rather, it should be part of the classroom culture and it should be continuous, with an emphasis on using assessment not for accountability or extrinsic motivation, but to support student learning.
  • Teachers can help learners see that learning and teaching are iterative processes by being more transparent about their own efforts to reflect and iterate on their practices.
  • Teachers should scaffold opportunities for students to evaluate their own work and develop independence.

Additional Resources

  • Creative Computing curriculum and projects
  • Karen Brennan on helping kids get “unstuck”
  • Usable Knowledge on how assessment can help continue the learning process

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creativity in education articles

What creativity really is - and why schools need it

creativity in education articles

Associate Professor of Psychology and Creative Studies, University of British Columbia

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Although educators claim to value creativity , they don’t always prioritize it.

Teachers often have biases against creative students , fearing that creativity in the classroom will be disruptive. They devalue creative personality attributes such as risk taking, impulsivity and independence. They inhibit creativity by focusing on the reproduction of knowledge and obedience in class.

Why the disconnect between educators’ official stance toward creativity, and what actually happens in school?

How can teachers nurture creativity in the classroom in an era of rapid technological change, when human innovation is needed more than ever and children are more distracted and hyper-stimulated ?

These are some of the questions we ask in my research lab at the Okanagan campus of the University of British Columbia. We study the creative process , as well as how ideas evolve over time and across societies. I’ve written almost 200 scholarly papers and book chapters on creativity, and lectured on it worldwide. My research involves both computational models and studies with human participants. I also write fiction, compose music for the piano and do freestyle dance.

What is creativity?

Although creativity is often defined in terms of new and useful products, I believe it makes more sense to define it in terms of processes. Specifically, creativity involves cognitive processes that transform one’s understanding of, or relationship to, the world.

creativity in education articles

There may be adaptive value to the seemingly mixed messages that teachers send about creativity. Creativity is the novelty-generating component of cultural evolution. As in any kind of evolutionary process, novelty must be balanced by preservation.

In biological evolution, the novelty-generating components are genetic mutation and recombination, and the novelty-preserving components include the survival and reproduction of “fit” individuals. In cultural evolution , the novelty-generating component is creativity, and the novelty-preserving components include imitation and other forms of social learning.

It isn’t actually necessary for everyone to be creative for the benefits of creativity to be felt by all. We can reap the rewards of the creative person’s ideas by copying them, buying from them or simply admiring them. Few of us can build a computer or write a symphony, but they are ours to use and enjoy nevertheless.

Inventor or imitator?

There are also drawbacks to creativity . Sure, creative people solve problems, crack jokes, invent stuff; they make the world pretty and interesting and fun. But generating creative ideas is time-consuming. A creative solution to one problem often generates other problems, or has unexpected negative side effects.

Creativity is correlated with rule bending, law breaking, social unrest, aggression, group conflict and dishonesty. Creative people often direct their nurturing energy towards ideas rather than relationships, and may be viewed as aloof, arrogant, competitive, hostile, independent or unfriendly.

creativity in education articles

Also, if I’m wrapped up in my own creative reverie, I may fail to notice that someone else has already solved the problem I’m working on. In an agent-based computational model of cultural evolution , in which artificial neural network-based agents invent and imitate ideas, the society’s ideas evolve most quickly when there is a good mix of creative “inventors” and conforming “imitators.” Too many creative agents and the collective suffers. They are like holes in the fabric of society, fixated on their own (potentially inferior) ideas, rather than propagating proven effective ideas.

Of course, a computational model of this sort is highly artificial. The results of such simulations must be taken with a grain of salt. However, they suggest an adaptive value to the mixed signals teachers send about creativity. A society thrives when some individuals create and others preserve their best ideas.

This also makes sense given how creative people encode and process information. Creative people tend to encode episodes of experience in much more detail than is actually needed. This has drawbacks: Each episode takes up more memory space and has a richer network of associations. Some of these associations will be spurious. On the bright side, some may lead to new ideas that are useful or aesthetically pleasing.

So, there’s a trade-off to peppering the world with creative minds. They may fail to see the forest for the trees but they may produce the next Mona Lisa.

Innovation might keep us afloat

So will society naturally self-organize into creators and conformers? Should we avoid trying to enhance creativity in the classroom?

The answer is: No! The pace of cultural change is accelerating more quickly than ever before. In some biological systems, when the environment is changing quickly, the mutation rate goes up. Similarly, in times of change we need to bump up creativity levels — to generate the innovative ideas that will keep us afloat.

This is particularly important now. In our high-stimulation environment, children spend so much time processing new stimuli that there is less time to “go deep” with the stimuli they’ve already encountered. There is less time for thinking about ideas and situations from different perspectives, such that their ideas become more interconnected and their mental models of understanding become more integrated.

This “going deep” process has been modeled computationally using a program called Deep Dream , a variation on the machine learning technique “Deep Learning” and used to generate images such as the ones in the figure below.

creativity in education articles

The images show how an input is subjected to different kinds of processing at different levels, in the same way that our minds gain a deeper understanding of something by looking at it from different perspectives. It is this kind of deep processing and the resulting integrated webs of understanding that make the crucial connections that lead to important advances and innovations.

Cultivating creativity in the classroom

So the obvious next question is: How can creativity be cultivated in the classroom? It turns out there are lots of ways ! Here are three key ways in which teachers can begin:

Focus less on the reproduction of information and more on critical thinking and problem solving .

Curate activities that transcend traditional disciplinary boundaries, such as by painting murals that depict biological food chains, or acting out plays about historical events, or writing poems about the cosmos. After all, the world doesn’t come carved up into different subject areas. Our culture tells us these disciplinary boundaries are real and our thinking becomes trapped in them.

Pose questions and challenges, and follow up with opportunities for solitude and reflection. This provides time and space to foster the forging of new connections that is so vital to creativity.

  • Cultural evolution
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Creativity and technology in teaching and learning: a literature review of the uneasy space of implementation 

  • Published: 11 January 2021
  • Volume 69 , pages 2091–2108, ( 2021 )

Cite this article

  • Danah Henriksen   ORCID: orcid.org/0000-0001-5109-6960 1 ,
  • Edwin Creely 2 ,
  • Michael Henderson 2 &
  • Punya Mishra 1  

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Internationally, creativity is a widely discussed construct that is pivotal to educational practice and curriculum. It is often situated alongside technology as a key component of education futures. Despite the enthusiasm for integrating creativity with technologies in classrooms, there is a lack of common ground within and between disciplines and research about how creativity relates to technology in teaching and learning—especially in the uncertain space of classroom implementation. This article provides a critical thematic review of international literature on creativity and technology in the context of educational practice. We identify four essential domains that emerge from the literature and represent these in a conceptual model, based around: (1) Learning in regard to creativity, (2) Meanings of creativity, (3) Discourses that surround creativity, and (4) the Futures or impacts on creativity and education. Each of these clusters is contextualized in regard to emerging technologies and the developing scope of twenty-first century skills in classroom implementation. We offer conclusions and implications for research and practice.

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Henriksen, D., Creely, E., Henderson, M. et al. Creativity and technology in teaching and learning: a literature review of the uneasy space of implementation . Education Tech Research Dev 69 , 2091–2108 (2021). https://doi.org/10.1007/s11423-020-09912-z

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DOI : https://doi.org/10.1007/s11423-020-09912-z

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Review article, a conceptual graph-based model of creativity in learning.

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  • 1 Educational Technology Lab, German Research Center for Artificial Intelligence, Berlin, Germany
  • 2 Computer Science Education/Computer Science and Society Lab, Institute of Informatics, Humboldt-University of Berlin, Berlin, Germany
  • 3 Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
  • 4 School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

Teaching creativity is one of the key goals of modern education. Yet, promoting creativity in teaching remains challenging, not least because creative achievement is contingent on multiple factors, such as prior knowledge, the classroom environment, the instruction given, and the affective state of the student. Understanding these factors and their interactions is crucial for successfully integrating creativity in teaching. However, keeping track of all factors and interactions on an individual student level may well exceed the capacity of human teachers. Artificial intelligence techniques may thus prove helpful and necessary to support creativity in teaching. This paper provides a review of the existing literature on creativity. More importantly, the review is distilled into a novel, graph-based model of creativity with three target audiences: Educators, to gain a concise overview of the research and theory of creativity; educational researchers, to use the interactions predicted by theory to guide experimental design; and artificial intelligence researchers, who may use parts of the model as a starting point for tools which measure and facilitate creativity.

1. Introduction

Fostering creative problem solving in students is becoming an important objective of modern education ( Spendlove, 2008 ; Henriksen et al., 2016 ). However, psychological research has found that creativity in classrooms is contingent on many contextual variables ( Kozbelt et al., 2010 ; Csikszentmihalyi, 2014 ; Amabile, 2018 ), that negative myths regarding creativity are abound ( Plucker et al., 2004 ), and that creativity is in tension with other educational goals like standardization ( Spendlove, 2008 ; Henriksen et al., 2016 ). As such, it appears highly challenging to successfully integrate creativity in teaching, alongside a wide variety of other educational goals that have to be achieved ( Spendlove, 2008 ).

Artificial intelligence may point a way forward by monitoring and enhancing the creative process in students without putting additional workload on teachers ( Swanson and Gordon, 2012 ; Muldner and Burleson, 2015 ; Roemmele and Gordon, 2015 ; Clark et al., 2018 ; Kovalkov et al., 2020 ; Beaty and Johnson, 2021 ). However, for such systems to be successful, we require a model of creativity that can be implemented computationally ( Kovalkov et al., 2020 ). In this paper, we review the existing literature on creativity in learning to provide a starting point for such a model–although our conceptual model must still be translated to a computational version. Few reviews of creativity research have focused on education and none, to our knowledge, have attempted to integrate the research result into a single model. We close this gap in the literature.

More precisely, we develop a conceptual, graph-based model of creativity in learning (see Figure 1 ), which we distill from prior research from the fields of psychology, education, and artificial intelligence. We design our model with three criteria in mind. It should be

• Comprehensive , in the sense that it includes all variables and interactions that are important for creativity in teaching, according to the existing literature,

• Minimal , in the sense that it does not introduce variables or interactions beyond what has been found in prior literature and restricts itself to variables that are relevant to creativity in teaching, and

• Consistent , in the sense that it remains a valid causal graph without loops or disconnected nodes.

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Figure 1 . Our conceptual, graph-based model of creativity. Each student/personality, process, and product corresponds to a replicate of a plate in this graph. Red-colored nodes refer to interventional variables, orange nodes to observable variables, and blue nodes to hidden variables.

We note that there is tension between these goals. Namely, comprehensiveness encourages more nodes, whereas minimality encourages fewer nodes. Comprehensiveness means including nodes and relationships which are in conflict, whereas consistency means avoiding such conflicts. During the construction of our model, we will make note of such tensions and how we chose to resolve them. Thus, we also provide insight into consensus and lack thereof in the literature.

Following the 4P framework of Rhodes (1961) , our model has four components, namely the (social) place or press in which creativity occurs, the person who performs a creative task, the creative process itself, and the product of the task. For each component, we distinguish between latent variables, observable variables, and intervention variables. This distinction is useful to design practical strategies for promoting creativity: we can manipulate an intervention variable, monitor observable variables, and thus make inferences regarding the effect of our intervention on latent variables.

Consider the example of a math course. One variable we can intervene upon is the difficulty of a math task. But even this simple intervention may influence creativity very differently, depending on a multitude of factors: if we make a task easy, some students may be bored such that they disengage and submit a basic and uncreative solution. Other students may be motivated to solve a boring task in a particularly creative way to make it interesting. Conversely, a harder task may lead some students to submit particularly unoriginal solutions to solve the task at all, whereas other students may be engaged by the challenge and thus more motivated to find a particularly clever solution.

The purpose of our conceptual model is to make such mechanisms more transparent, to make creative achievement more predictable and, as a result, enable interventions to facilitate creativity. Accordingly, we believe that our proposed model is not only useful for artificial intelligence researchers, but also for teachers and educational researchers to inform their instructional strategy and their study design, respectively.

In this paper, we focus on providing three main contributions:

(1) Reviewing the existing work on creativity in learning,

(2) Distilling a conceptual, graph-based model of creativity in learning from our review, and

(3) Discussion of potential applications and challenges of putting the developed conceptual model into practice.

The paper is structured as follows: First, we provide a detailed discussion on the evolution of creativity definitions and creativity research to date (Section 2). The discussion is intended to show more clearly the multifaceted nature of creativity. Consequently, we discuss prior works on how artificial intelligence techniques have been used to generate creative behaviors in computers and in humans (Section 2). The third section presents the methodologies used for gathering the necessary literature for the conceptual model (Section 3). In the fourth section (Section 4), we present the proposed conceptual model of creativity, in four different creativity plates namely place, person, process, and product. Finally, we discuss limitations and points to future work (Section 5).

2. Background and related work

The roots of creativity research date back at least to the 19th century, when scholars attempted to define creativity philosophically ( Runco and Jaeger, 2012 ). The motivation for such scholarship was to find a shared trait that enabled creative geniuses to achieve works of art and science ( Runco and Jaeger, 2012 ). Accordingly, creativity was mostly seen as an innate trait of a small elite, a gift to create things both useful and beautiful ( Runco and Jaeger, 2012 ). Creativity was defined much broader after the second world war, in attempts to develop creativity tests which quantify creative problem solving skills in the general population ( Csikszentmihalyi, 2014 ). Creativity tests broadly fell into two classes: First, tests that pose creative problem-solving tasks and measure creativity as the success in solving these tasks (e.g., Torrance, 1972 ; Williams, 1980 ; Runco et al., 2016 ). Second, autobiographic surveys which measure creativity as the sum of past creative achievement (e.g., Hocevar, 1979 ; Diedrich et al., 2018 ). Importantly, both classes of tests frame creativity as the trait of a person . In the words of Guilford (1950) : “creativity refers to the abilities that are most characteristic of creative people”. The implicit view 1 of the time seems to be that creativity is an innate property of people that is either present or not, independent of context. This view has been criticized in the decades to come, especially by Amabile (2018) and Csikszentmihalyi (2014) , who emphasized that creativity is dependent on a host of contextual factors such as individual motivation, ability to solve a problem from multiple perspectives, the domain in question ( Baer, 2010 ), and who gets to be the judge of creativity. Table 1 shows an overview of creativity definitions in the literature and how they relate to education.

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Table 1 . Definitions of creativity in prior literature.

The current discussion about creativity research is characterized by two aspects: the variety in creativity theories and creativity definitions, and the challenges of applying creativity models for pedagogical implementations. The aspect of pedagogical implementations is important because there still exists potential barriers to computationalize the definitions of creativity. To address this complex phenomenon, we discuss existing works on creativity and works on artificial intelligence for creativity in learning.

2.1. Prior reviews on creativity

Multiple other scholars have already provided reviews of this long research tradition, complementary to our present work. Mumford (2003) summarizes book chapters on creativity, covering multiple theories that existed at the time, as well as empirical findings on the role of factors such as expertise, motivation, affect, situational factors, and development. These findings form one of the bases for our own model.

Cropley and Cropley (2008) propose a theory that divides a creative activity into seven phases, namely preparation, activation, cogitation, illumination, verification, communication, and validation. A key motivation for this phase model is to resolve paradoxes in creativity, e.g., that convergent thinking both hampers and supports creativity. In the phase model, convergent thinking is crucial in the preparation, illumination, and verification phases, but detrimental in the activation phase, where divergent thinking is required. More generally, Cropley and Cropley (2008) relate each phase to the four P's—press, person, process, and product—of Rhodes (1961) . Our own work follows the example of Cropley and Cropley (2008) in that we try to provide a consistent model that is compatible with the wider literature. However, our perspective is slightly wider, in that we do not only focus on a single creative activity but an entire course or tutoring system.

Kozbelt et al. (2010) reviewed theories of creativity and classified them into ten different classes, namely developmental, psychometric, economic, process, expertise-based, problem-finding, evolutionary, typological, and systems theories. Given the wide variety of perspectives, they recommend to not attempt a “grand unifying theory” but to include the perspectives relevant to a certain application. We aim to follow this recommendation. In particular, we limit ourselves to theories that apply to creativity in learning, but we aim to be comprehensive for this setting, including the developmental, process, expertise-based, and systems perspective, and we try to be explicit how our model is situated in the broader landscape of creativity research.

Sawyer (2011) reviews neuroscience studies of creativity, especially studies involving EEG, PET, and fMRI recordings. He highlights that neural activation during creative activity is not localized in a certain brain area but involves a wide variety of areas (such as psychological and cognitive areas; Guilford, 1950 ; Vosburg, 1998 ; Mumford, 2003 ; Sawyer, 2006 ; Runco and Jaeger, 2012 ; Csikszentmihalyi, 2014 ; Kaufman, 2016 ; Zhou, 2018 ) that are also active during everyday activity ( Khalil et al., 2019 ); that subconscious processes appear to be crucial for creativity, such as mind wandering; and that the importance of domain-specific knowledge is confirmed. Despite this complexity, the work of Muldner and Burleson (2015) indicates that creativity can be detected from EEG signals (in combination with skin conductance and eye tracking) at least for a geometry problem.

Similarly, Zhou (2018) reviews creativity-related studies involving fMRI and EEG signals to assess human brain function while performing creativity-related cognitive tasks. In line with Sawyer (2011) 's findings, the author highlights studies that show neural activities are not limited to a particular region in a human brain, and in fact, some studies ( Liu et al., 2012 ; Takeuchi et al., 2013 ; Vartanian et al., 2013 ) show that neural efficiency (i.e., most efficient brain functioning or more focused brain activation) in creative thinking can be attained through cognitive training, as well as targeted training on fundamental cognitive abilities such as attention and working memory ( Vartanian et al., 2013 ). Our review is different because our focus is not neuroscience but, rather, creativity as an outcome of a cognitive process that depends on personal and context variables.

Runco and Jaeger (2012) review the history of creativity research leading up to what they call the standard definition of creativity , namely that creativity combines originality with effectiveness (alternatively: usefulness, fit, or appropriateness). We include this standard definition to define creativity in products, but we also go beyond the standard definition by including place, person, and process in our model.

Finally, Schubert and Loderer (2019) review creativity-related tests and classify them according to their relation to the 4P model ( Rhodes, 1961 ) and their method (self-report survey, expert judgment, psychometrics, and qualitative interview). We incorporate such techniques as observable variables in our model.

Overall, we build upon all these prior reviews but also provide complementary value in our focus (creativity in teaching), our scope (all variables related to a course), and our approach (a graph model).

2.2. Artificial intelligence and creativity

Our goal is to facilitate the construction of artificial intelligence tools that measure and support human creativity. This is in contrast to most prior work in artificial intelligence on creativity, which has been focused on generating creative behavior in computers (computational creativity; Jordanous, 2012 ; Mateja and Heinzl, 2021 ). In this field, the work of Boden (1998) has been foundational. Boden understands creativity as three operations on a knowledge base, namely

• Exploration: Computing the knowledge space corresponding to a given domain,

• Recombination: Combining existing ideas in a new context or fashion, and

• Transformation: Giving the knowledge space new rules by which it can be processed (a distant reminder of SWRL rules in OWL, as proposed by Horrocks and Patel-Schneider, 2004 ).

While these three operations do not necessarily describe creativity in human thinking, we do believe that it can be useful to distinguish between ideas that emerge by insight/illumination and ideas that result from recombining existing ideas. Accordingly, we translate this distinction into our model.

Ram et al. (1995) elaborate Boden's model by discussing the difference between knowledge and thinking. The authors add the task, situation, and strategic control of inference as dimensions and claim that only a combination of these will constitute the basis for thought. We believe that these extensions are suitably covered in our model by the process and person variables.

Similar to the standard definition of creativity, Boden states that creativity requires novelty and a positive evaluation of the creative product (i.e., appropriateness). In terms of novelty, Boden distinguishes between P-creativity (an idea is novel only to myself), and H-creativity (an idea is novel with respect to the entire society). Lustig (1995) suggests to generalize this distinction to “novelty with respect to a reference community”, which is also the view we take.

Jordanous (2012) argues that it is crucial to evaluate computationally generated products with a shared (fair) standard. Just as in human creativity, optimizing for originality alone is insufficient, one also requires a domain-specific usefulness standard. Accordingly, most seminal works in computational creativity have invested much effort into finding domain-specific rules to explore in a way that is more likely to generate appropriate results ( Baer, 2010 ; Colton and Wiggins, 2012 ). A lesson for our model is that the “appropriateness” measure of creative products needs to be well-adjusted to the task in order to make sure that we do not misjudge creative products. Further, there is debate whether it is sufficient for evaluation to judge the final product or whether the computational process must be included in the evaluation. We account for this by including the process in our model.

Recently, machine learning models and, specifically, generative neural networks have been utilized to generative computationally creative works ( DiPaola et al., 2018 ; Berns and Colton, 2020 ; Mateja and Heinzl, 2021 ). This is somewhat surprising as generative models are intrinsically novelty-averse as they are trained to model and reproduce an existing data distribution ( DiPaola et al., 2018 ; Berns and Colton, 2020 ). Still, by cleverly exploring the latent space of such models, one can generate samples that appear both novel and domain-appropriate, hence indicating creativity ( DiPaola et al., 2018 ; Berns and Colton, 2020 ). Such an approach searches for novelty between existing works and can, as such, be viewed as recombination ( DiPaola et al., 2018 ), which we also include in our model.

2.3. Artificial intelligence for creativity in education

Using artificial intelligence to measure and support creativity in education is a relatively recent approach. Huang et al. (2010) developed an “idea storming cube” application for collaborative brainstorming which automatically measures creativity by the number of distinct generated ideas. Muldner and Burleson (2015) used biosensors and machine learning to distinguish high and low creativity students in a geometry tasks. Kovalkov et al. (2020 , 2021) define automatic measures of creativity in computer programs in terms of fluency, flexibility, and originality, following the work of Torrance (1972) . Hershkovitz et al. (2019) ; Israel-Fishelson et al. (2021) quantify the relation between creativity and computational thinking in a learning environment. Finally, Cropley (2020) highlights the need to teach creativity-focused technology fluency to make use of AI and other novel technologies. Given the relative paucity of such works, we believe there is ample opportunity for further research at the intersection of artificial intelligence, education, and creativity, which we wish to facilitate with our model.

3. Literature search

To scan the literature for relevant contributions, we used two techniques.

First, we performed a snowball sampling ( Lecy and Beatty, 2012 ), meaning we started with the foundational seed papers of Boden (1998) and Runco and Jaeger (2012) and branched out from there, following their references as well as papers that cited them, recursively.

Second, we started a structured keyword search. Here we focused on the application of creativity measurement in the area of learning and formal educational institutions. The keywords used were “creativity AND measure”, “creativity AND analytics”, “creativity AND learning”, “creativity AND tutoring”, as well as “creativity AND [school subject]”. For the list of school subjects we used the German secondary curriculum.

We searched for the keywords in the following data bases (with the number of initial search results in brackets).

• Google Scholar (107)

• ACM digital library (8)

• ScienceDirect (17)

• Elsevier (344)

• IEEE Explore (20)

• Jstor (2)

Note that there are duplicates between the searches.

In order to narrow down the relevant literature for the goal of constructing a model of creativity that is easy to use in the field of educational technologies, content filters were applied. These are not as succinct as the keywords as they usually consist of two or more dimensions that function as decision boundaries whether to keep a paper for the output model or not. For example, we encountered one paper that dealt with creativity as part of design. On the one hand, it fit our lens because it provided a clear and operational definition of creativity. However, it did not satisfy the rule that the creativity definition should be generic regarding the fields of learning.

In the following we provide list of dimensions that were used to filter the literature.

• A general definition of creativity beyond a single domain,

• a clear and well-defined concept of creativity,

• creativity is seen as measurable,

• the concept of creativity does not contradict its use in the learning field, and

• the creativity definition contains either a measurement or a product component.

In the end, 77 papers remained after applying our filters (marked with a * in the literature list). Of these, eight cover artificial intelligence approaches.

Note that it is still possible that interesting related works are not covered because they evaded our particular search criteria. Nonetheless, we aim to be comprehensive and representative. We distill our results into a graph-based model in the following section.

4. A conceptual graph-based model of creativity

In this section, we provide a conceptual, graph-based model of creativity (refer to Figure 1 ), based on a review of the existing literature. As noted in Section 1, we aim for a model which is comprehensive, minimal , and consistent . To achieve these objectives, we opt for a conceptual, graph-based model ( Waard et al., 2009 ). A graph enables us to include all variables and their relationships, as stated in the literature (comprehensiveness), aggregate variables that fulfill the same function in the graph (minimality), and avoid cycles in the graph (consistency). We keep our model abstract enough to cover a wide range of positions expressed in the literature but specific enough to understand how creativity in learning comes about. Therefore, we model the network structure but avoid quantitative claims regarding the strength of connections.

In particular, we represent a relevant variable x as a node in our graph and a hypothesized causal influence of a variable x on another variable y as an edge/arrow ( Waard et al., 2009 ). We further distinguish between three kinds of variables: Intervention variables (red) are variables that educators can manipulate to influence creativity, namely the curriculum and task design. Observable variables (orange) are variables that we can measure via tools established in the literature, such as sensors, creativity tests, or teacher judgments. Finally, latent variables (blue) are all remaining variables, i.e., those that we can neither directly observe nor intervene on, but which are nonetheless crucial for creativity. Most importantly, this includes the creative process inside a student's mind. Importantly, we only include a node if the respective variable is named in at least one of the 77 papers we reviewed; and we only include an edge if the respective connection is indicated in at least one of these papers. In Figures 2 – 5 , each edge is annotated with the literature it is based on.

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Figure 2 . A closer look at the place (or environment) plate. It highlights a (few) external factors that influences an individuals' knowledge and behavior. Citations: A1 ( Csikszentmihalyi, 2014 ; Amabile, 2018 ), A2 ( Runco et al., 2017 ; Castillo-Vergara et al., 2018 ).

Finally, we group our variables into plates according to the four Ps of Rhodes (1961) , namely place, person, process, and product. We use this particular structure for three reasons. First, the paper of Rhodes (1961) can be regarded as a foundational paper of the field (cited 3,099 times according to Google scholar) such that the basic structure of the four Ps can hopefully be regarded as accepted–we did not find evidence to the contrary, at least. Second, the four Ps comprehensively cover a wide variety of topics and are, thus, well-suited for a literature review. Third, the four Ps provide a well-defined framework which allows us to sort existing work according to scope (from societal to personal) and time scales (from societal change over years to second scale).

Consider the example of a math course. For each relevant social group in our class, we need a copy of the “place” plate that models the respective socialization. For each student, we need a copy of the “person” plate, describing individual domain knowledge and creative affinity. For each learning task and each student, we need a copy of the “process” plate which describes the student's work on this particular task. And finally, for each submitted task solution in the course, we need a copy of the “product” plate.

In the remainder of this section, we will introduce each plate in detail and justify nodes and edges based on the literature.

In our model, the term “place” or “press” (press was the original word used by Rhodes, 1961 ) covers environmental factors influencing creativity which go beyond a single learning task or student. Prior work has covered, for example, the social group in which students learn ( Amabile, 2018 ), students' socio-economic status ( Hayes, 1989 ), and the broader culture, where notions of creativity change over decades and centuries ( Csikszentmihalyi, 2014 ). Following this work, we define “place” as the aggregation of all variables outside of a student's individual cognition which may influence their creativity. To make this definition more practically applicable, we introduce two separate nodes in our graph: the learning environment and the socialization.

In more detail, we define the learning environment as the collection of variables that educators or system designers can intervene upon but which go beyond an individual student or task, such as the teaching staff, the access to auxiliary resources, the quality of such resources, and the prior curriculum that the students were exposed to before entering the current course. By contrast, we define the socialization as the collection of variables which we are outside educators' control but nonetheless influence students' creativity beyond a single person or task. While the socialization, as such, is hidden, we can measure proxy variables, such as gender, socioeconomic status, or ethnicity ( Runco et al., 2017 ; Castillo-Vergara et al., 2018 ), which can also be captured in digital learning environments or intelligent tutoring systems.

While not the focus of this work, we note that students are oftentimes subject of (structural, indirect) discrimination based on such proxy features and special attention must be paid to promoting equity instead of exacerbating existing biases in society ( Loukina et al., 2019 ). For example, one can try to adjust the learning environment to deliberately counterbalance the differential impact of socialization on creativity.

Note that there is no consensus in the literature how strongly different aspects of socialization or learning environment influence creativity. Amabile (2018) ; Csikszentmihalyi (2014) would argue for a strong influence of socialization, for example, whereas (some) creativity tests (implicitly) assume that it is possible to quantify creative affinity independent of context, in a lab setting ( Torrance, 1972 ; Williams, 1980 ). Further, the relationship between socialization and observable, easy-to-measure demographic variables is complex and one can argue for different scales ( Buchmann, 2002 ). Our model is abstract enough to accommodate either position: If one believes that socialization has a small or large influence, one can apply a small or large weight to the respective arrow. Similarly, one could fill the “demographic features” node with different scales, depending on which aspects of socialization should be measured.

4.2. Person

In our model, a person is a student who is enrolled in a course or an intelligent tutoring system and has an individual capacity for creative achievement within this course.

A large number of prior works has investigated which personality traits or skills facilitate creativity. For example, Hayes (1989) argues that creative thinking can be broken down into a combination of other skills, like domain knowledge, general education, mental flexibility, different representations of knowledge, and hard work. Hayes (1989) also claims that there is no relation between general intelligence and creativity, after controlling for domain knowledge and education. By contrast, Guilford (1967) argues that intelligence is a necessary but not sufficient condition for creativity, which sparked an ongoing series of empiric studies (e.g., Jauk et al., 2013 ; Weiss et al., 2020 ). Beyond intelligence and cognitive skills, there has been ample research on the connection between personality traits, especially openness to experiences and extraversion in the big-five inventory (e.g., Eysenck, 1993 ; Sung and Choi, 2009 ; Karwowski et al., 2013 ; Jauk et al., 2014 ).

Most tests for of a person's capacity for creativity assess either the amount of past creative achievement via biographic questions ( Hocevar, 1979 ; Diedrich et al., 2018 ), or confront a person with a specific, psychometrically validated creative task and measure their performance in this task ( Torrance, 1972 ; Williams, 1980 ; Runco et al., 2016 ). Such tasks typically consist of a prompt, in response to which a person is asked to come up with as many ideas as possible. The number (fluency), distinctness (flexibility), and novelty (originality) of these ideas is then used as a measure of creativity ( Torrance, 1972 ; Kim, 2006 ).

Note that all these tests share an implicit assumption, namely that creativity is, to some degree, generalizable. In other words, if a person behaves creatively in one context, this translates to creativity in other contexts. This is in tension with the view that creativity can only be judged in context ( Amabile, 2018 ). Sternberg (2005) proposes an intermediate position: knowledge is domain-specific but there also exist thinking styles and other factors that are domain-general. This view is also mirrored in cognitive science. For example, ( Burnard, 2011 , p. 141) writes: “Especially important is the notion that creative learning is a mediated activity in which imaginative achievement and the development of knowledge have a crucial role.”, and ( Mumford et al., 2011 , p. 32) adds: “Knowledge is domain-specific. Moreover, multiple alternative knowledge structures may be employed in creative thought within a domain, schematic, case-based, associational, spatial, and mental model knowledge structures, and these knowledge structures appear to interact in complex ways.” In this quote, Mumford also indicates that domain-specific knowledge and domain-general skills influence the creative process in different ways. We will account for this difference in our process plate later.

In our model, we represent a person—that is, a student—by two nodes, namely domain knowledge and creative affinity. Domain knowledge includes declarative, procedural, and conceptual knowledge for any domain that is relevant to the current course. By contrast, creative affinity includes all variables that vary between people but are domain-general, such as openness to experiences, extraversion, (general) intelligence, and generalized creative capacity. To measure domain knowledge, we suggest domain-specific knowledge tests, which we do not cover here for brevity (refer, e.g., to Schubert and Loderer, 2019 ). To measure creative affinity, literature suggests personality tests 2 and/or creativity tests, as listed above, yielding the graph in Figure 3 . In the overall model ( Figure 1 ), we also include incoming arrows that account for possible influence of the (social) context on both domain knowledge and creative affinity.

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Figure 3 . A closer look at the person plate. This plate includes variables describing the creative capacity of a single student. We provide examples of such variables from the literature in transparent nodes. Citations: B1 ( Hayes, 1989 ; Sternberg, 2005 ; Mumford et al., 2011 ; Schubert and Loderer, 2019 ), B2 ( Eysenck, 1993 ; Sung and Choi, 2009 ; Karwowski et al., 2013 ; Jauk et al., 2014 ), B3 ( Torrance, 1972 ; Hocevar, 1979 ; Williams, 1980 ; Diedrich et al., 2018 ).

Our model refrains from making any assumptions regarding the weight of each edge or the specific form of the influence because there is no consensus in the literature regarding these questions. Some authors might argue that there is no general “creative affinity” at all, but only context-dependent affinity ( Amabile, 2018 ), whereas some creativity tests would argue that domain-general creative affinity does exist ( Runco et al., 2016 ). There is also professional debate regarding the value of personality tests to measure creative affinity ( Schubert and Loderer, 2019 ), which creativity test is best suited to measure creative affinity ( Runco et al., 2016 ), or how knowledge tests ought to be constructed ( Schubert and Loderer, 2019 ).

4.3. Process

In our model, a process refers to the chain of cognitive activities a student engages in while trying to solve a specific learning task, from receiving the task instructions to submitting a solution attempt.

Researchers have developed multiple theories how the creative process is structured. Rhodes (1961) lists four different steps, namely preparation, incubation, inspiration, and verification. Preparation refers to the pre-processing of input information; incubation to the conscious and unconscious further processing, revealing new connections between known pieces; inspiration to the actual generation of an idea during incubation; and verification to the conversion of a rough idea to a creative product. Cropley and Cropley (2008) splits “incubation” into “activation” (relating a problem to prior knowledge) and “cogitation” (processing the problem and prior knowledge), renames “inspiration” to “illumination”, and adds two new phases at the end, namely “communication” and “validation”. These new phases account for the social context of creativity, namely that a creative product only “counts” if it has been communicated to and validated by other people.

In contrast to these models, Treffinger (1995) argues that creative problem solving does not occur in strict phases but by inter-related activities such as problem-finding and solution-finding. Similarly, Davidson and Sternberg (1984) suggest the following three processes:

1. Selective encoding: distinguishing irrelevant from relevant information,

2. Selective combination: taking selectively encoded information and combining it in a novel but productive way, and

3. Selective comparison: relating new information to old information.

Davidson and Sternberg's view aligns well with Boden's model of artificial creativity ( Boden, 1998 ). In particular, selective encoding can be related to exploration, combination and comparison to recombination, and comparison to transformation. An alternative computational view is provided by Towsey et al. (2001) , who argue that creativity can be described as an evolutionary process. From a set of existing ideas, the ones that best address the current problem are selected (selective comparison and encoding) and recombined to form a new set of existing ideas (selective combination), until a sufficiently good solution to the problem is found.

Multiple scholars agree that repurposing and combining prior knowledge is crucial for creativity. For example, Lee and Kolodner (2011) relate creativity to case-based reasoning, where a new problem is compared against a data base of known problems and the best-matching solution is retrieved and adapted to the present case. Such case-based reasoning can be regarded as creative if the relation between the past case and the present case is non-obvious but the solution still works. Similarly, Hwang et al. (2007) argue that creativity is related to making ordinary objects useful in a novel and unexpected way. Sullivan (2011) names this repurposing process “Bricolage” in reference to the work of Levi-Strauss (1966) .

There is some disagreement in the literature regarding the creative process. Nonetheless, we aim to provide a model that is as widely compatible as possible while remaining useful. In particular, we include three cognitive processes, namely incubation, recombination, and insight. Incubation refers to processing the existing set of ideas to support idea generation. Recombination refers to generating new ideas by combining existing ones. Finally, insight refers to generating new ideas beyond combination, e.g., via re-purposing. Both recombination and insight generate ideas which the student needs to validate against the problem at hand ( Cropley and Cropley, 2008 ). After validation, the ideas become part of the “idea bundle”, that is, the current working set of ideas that may end up as parts of the solution. Note that our model is compatible both with models that emphasize the order of different phases ( Cropley and Cropley, 2008 ), as well as models which focus more on the different types of operations used to generate creative ideas, without regard for their order ( Davidson and Sternberg, 1984 ; Treffinger, 1995 ). Across theories, there is broad agreement that ideas can be generated via recombination or insight and that they get filtered or validated before they become part of a solution to a learning task.

The final component of our process model is the affective state. Amabile (2018) argues that the affective state influences creativity, which is confirmed by several empiric studies. For example, Csikszentmihalyi (1996) finds that a positive affective state (such as flow state) is identified in individuals when they are being highly creative; Vosburg (1998) find that positive mood facilitates divergent thinking; and the review of Davis (2009) finds that (moderate amounts of) positive also affect enhances creativity. However, George and Zhou (2002) also point to scenarios where bad mood is related to better creativity outputs, especially when short moments of frustration motivate refinement and improvement ( Muldner and Burleson, 2015 , as described by), which could be seen as an aspect of incubation. Further, Baird et al. (2012) ; Sawyer (2011) found that absent-mindedness or mind-wandering are crucial to incubation. Accordingly, we include an arrow form the affective state to incubation, yielding the graph of blue nodes in Figure 4 .

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Figure 4 . A closer look at the process plate. The nodes represent different stages of the student cognitive process to generate and validate creative ideas, namely insight, incubation, and recombination, as well as the affective state which influences the process and the idea bundle as result of the process. Citations: C1 ( George and Zhou, 2002 ; Sawyer, 2011 ; Baird et al., 2012 ), C2 ( Rhodes, 1961 ; Cropley and Cropley, 2008 ), C3 ( Davidson and Sternberg, 1984 ; Boden, 1998 ), C4/C5 ( Cropley and Cropley, 2008 ), C6 ( Cooper et al., 2010 ; Blanchard et al., 2014 ; Muldner and Burleson, 2015 ; Pham and Wang, 2015 ; Faber et al., 2018 ), C7 ( Kim, 2006 ; Huang et al., 2010 ; Bower, 2011 ; Sullivan, 2011 ; Liu et al., 2016 ), C8 ( Amabile et al., 2002 ; Baer and Oldham, 2006 ).

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Figure 5 . A closer look at the product plate. It describes the creativity of a product in terms of correctness, fluency, flexibility, and originality. Citations: D1 ( Runco and Jaeger, 2012 ), D2 ( Torrance, 1972 ; Huang et al., 2010 ; Muldner and Burleson, 2015 ; Yeh and Lin, 2015 ; Kovalkov et al., 2020 , 2021 ).

In line with Cropley and Cropley (2008) ; Amabile (1982) ; Baer (2010) , we emphasize that different cognitive skills contribute to different parts of the creative process. For example, domain knowledge is required for both insight and recombination of ideas, whereas creative affinity more broadly may also affect incubation ( Burnard, 2011 ), as summarized in Baer (2010) . Within creative affinity, one may also distinguish between divergent thinking, which is crucial for recombination and insight, whereas convergent thinking is crucial for filtering ideas before they get added to the idea bundle ( Cropley and Cropley, 2008 ).

While the process is, in principle, hidden because it occurs inside a student's mind, there do exist approaches to measure different aspects of the creative process as it happens. First, we can monitor a students' affective state via biosignals, as is evidenced by the literature on the detection of mind-wandering via skin conductance ( Cooper et al., 2010 ; Blanchard et al., 2014 ; Muldner and Burleson, 2015 ), heart rate ( Pham and Wang, 2015 ), or eye movement ( Iqbal et al., 2004 ; Schultheis and Jameson, 2004 ; Muldner and Burleson, 2015 ; Faber et al., 2018 ). Second, we can indirectly observe how the student's idea bundle develops over time. For one, we can ask students to verbalize their thinking while it happens (“think aloud” protocols). Such techniques are particularly promising for collaborative work where students need to interact and communicate their incomplete creative process with their group partners anyways ( Kim, 2006 ; Huang et al., 2010 ; Bower, 2011 ; Sullivan, 2011 ; Liu et al., 2016 ). Third, if students work inside a digital learning environment or intelligent tutoring system, we can log student activity and thus gather insight into their process ( Greiff et al., 2016 ).

As an example, consider a simple math multiplication question, such as 25·12. We could now ask the student to write down all intermediate steps they take. One student may apply a long multiplication, which requires the initial insight that long multiplication can be applied, the decomposition into 25·10 and 25·2, the solution of these intermediate steps (250 and 50), and finally the combination to the overall answer (250 + 50 = 300). Another student may connect the multiplication with geometry, draw a rectangle of 25 cm x 12 cm on a grid and count the number of grid cells covered. Finally, another student may recognize that 12 factors as 4·3, work out 25·4 = 100 and 100·3 = 300. In all cases, the different creative process becomes apparent by inspecting an activity log of the intermediate steps the students took.

This example also illustrates how the process is influenced by personal or contextual factors: If a student hasn't learned long multiplication, the first strategy is unavailable. If a student lacks time, or if the learning environment does not supply a grid, the second strategy is unavailable. If a student lacks experience in factorizing or is too stressed, the third strategy is unavailable.

Importantly, we can intervene on the creative process by designing task instruction and/or task environment in a specific way.

For example, Baer and Oldham (2006) found that time pressure influences creativity. In a workplace context, experienced time pressure was generally detrimental for creativity, except for participants with high openness to experience and high support for creativity, who performed best with a moderate level of time pressure. Similarly, Amabile et al. (2002) suggest that moderate levels of time pressure are endogenous within a team project, as it only allows an individual to be positively challenged, in turn triggering creativity. For our multiplication example, we would discourage the second strategy by imposing a strict time limit, which prohibits the time-intensive re-representation via geometry. Conversely, we would encourage the third strategy by providing the prime factorization of 12 as a hint in our instruction.

We aggregate all options of educators/designers to influence how a task is processed in a node we call “task features”. Following the terminology of VanLehn (2006) , task features include all aspects of the “inner loop” of our tutoring system, whereas the “learning/environment” in “place” includes the “outer loop”.

4.4. Product

We define a creative product as the result of translating a student's idea bundle into something tangible that can be inspected by a teacher, such as a response to a math question, including a log of all intermediate steps. This translation is lossy: Depending on the task features, a student may be more or less able to translate ideas into a product. Further, even the ideas that do get translated into a product may not be picked up by the sensors of our system because they lie outside our expectations when designing the system. As Hennessey et al. (2011) put it: creativity may be difficult to formalize in all its richness, but people recognize it when they see it. Accordingly, they suggest to assess creativity via a consensual assessment technique (CAT), using the judgment of a panel of human domain experts. Unfortunately, though, a panel of multiple experts is usually not available in education, especially not in automated systems. Accordingly, we turn toward notions of creativity in products that are easier to evaluate automatically.

There is wide agreement that two abstract criteria are necessary for creativity in products, namely novelty and appropriateness (sometimes with different names; refer to Sternberg and Lubart, 1999 ; Runco and Jaeger, 2012 ). For example, submitting a drawn flower as solution to an multiplication task is certainly novel, but it is inappropriate for the task. However, if the drawn flower encodes the right answer (e.g., via the number of petals), it is both novel and appropriate, thus counting as creative. Note that both criteria are context-dependent: appropriateness depends on the current learning task and novelty on the reference set to which the current solution is compared ( Sternberg and Lubart, 1999 ; Csikszentmihalyi, 2014 ; Amabile, 2018 ). In other words, if all students in a class submit flowers, this representation seizes to be novel.

Creativity tests provide further detail. For example, Torrance (1972) suggests multiple scales including

• Fluency: the number of generated ideas,

• Flexibility: the number of distinct classes of ideas, and

• Originality: the infrequency of ideas compared to a typical sample of students.

These three scales are particularly interesting because they have been applied in recent work on artificial intelligence for creativity in education. In particular, Huang et al. (2010) , Muldner and Burleson (2015) , and Kovalkov et al. (2020) all use fluency, flexibility, and originality to measure the creativity of student solutions (namely in a collaborative brainstorming task, geometry proofs, and Scratch programs, respectively). The Digital Imagery Test of Yeh and Lin (2015) measures creativity by the amount of unique associations (fluency/flexibility) in reaction to an ambiguous, inkblot-like picture. There is also some evidence that combining measures of fluency, flexibility, and originality with artificial intelligence can approximate human ratings ( Kovalkov et al., 2021 ).

We believe this body of work establishes that at least fluency, flexibility, and originality can be automatically assessed with computational methods and thus introduce these three dimensions as observable nodes in our model. Additionally, we include appropriateness as required by the “standard definition of creativity” of Runco and Jaeger (2012) . However, we call our node “correctness” to be more in line with the educational setting.

Returning to our math example, consider an assignment of multiple multiplication questions. We can measure correctness by counting how many answers a student got right; we can measure fluency by counting the number of different strategies the student employed; we can measure flexibility by measuring how different those strategies are; and we can measure originality by counting how often these strategies were used in a typical sample of students with the same amount of prior knowledge on the same assignment.

5. Discussion and conclusion

In this article, we reviewed the research on creativity and distilled a conceptual, graph-based model which captures all crucial variables as well as their relations (refer to Figure 1 ). This model can serve teachers to get a clearer understanding of creativity and how to measure and facilitate it in the classroom by adjusting task features/instruction. More specifically, Cropley and Cropley (2008) discuss how to improve instruction for creativity based on different phases of the creative process; and several interventions investigate automatic measurement and support for creativity in educational technology ( Huang et al., 2010 ; Muldner and Burleson, 2015 ; Hershkovitz et al., 2019 ; Israel-Fishelson et al., 2021 ; Kovalkov et al., 2021 ).

The proposed model can also be useful for educational researchers as a basis for study design, that is, which measures to include in a study and which connections to investigate. Finally, we hope to provide a starting point for the construction of artificial intelligence tools that measure and facilitate creativity, e.g., in intelligent tutoring systems. For example, one can use our model as an initial graph for a Bayesian network ( Barber, 2012 ) or a structural causal model ( Pearl, 2009 ). Such an implementation would permit probabilistic estimates for every variable and every individual student at every point in time, thus giving students and teachers a detailed view of creative developments and highlighting individual opportunities for higher creative achievement. We note that some approaches already exist which assess creativity in an educational setting, using AI components. For example, Muldner and Burleson (2015) classify high vs. low creative students from biosensor data, and Kovalkov et al. (2021) estimate the creativity of multimodal computer programs using regression forests.

Still, we acknowledge serious challenges in putting our model into practice. First, while we justified our nodes and edges via literature, we do not provide precise structural equations, as required by a structural causal model; nor probability distributions, as required by a Bayesian network. Any implementation needs to fill our model with “mathematical life” by making reasonable assumptions regarding connection strengths and the relation of incoming influences at each variable. Some of the following questions can help designers who aim to implement our conceptual model for a specific application scenario. Is the broader (social) context crucial in the scenario or are personal variables sufficient to model individual differences? Which knowledge domain is concerned and how can we measure domain-specific knowledge? Is a “generic” creativity affinity plausible in the scenario or is the contextual influence more important? Which aspects of a student's affective state are important for the scenario? Which theory of the creative process appears most plausible; e.g., a phase model or an “unordered model”? None of these questions is easy to answer and answers will require application-specific considerations. Nonetheless, the works cited in this paper can serve as inspiration.

Second, it is technologically challenging to implement a sufficient number of sensors (i.e., the orange nodes in Figure 1 ) to accurately estimate all latent variables (i.e., the blue nodes) in our model. Some sensors are domain-specific and thus need to be developed for any new domain, such as correctness, fluency, flexibility, and originality ( Kovalkov et al., 2020 ). Further, some of the sensors raise privacy concerns, especially biosignals. As such, it may be pragmatically advisable to limit the number of sensors. However, fewer sensors mean that it may become impossible to estimate (some) latent variables with sufficient certainty. Accordingly, one also needs to consider whether to exclude/simplify some latent variables for pragmatic reasons.

Third, creativity is not value-neutral. If a system judges a student/product as more creative than another, this judgment is value-laden and should not be made lightly. This is especially critical as even a full implementation of our model is unlikely to capture the full richness of creativity, including elements of aesthetic beauty, surprise, and other hard-to-formalize dimensions ( Runco and Jaeger, 2012 ). All that non-withstanding, we believe it is crucial to face the full complexity of creativity and to be explicit where we simplify the model to comply with practical constraints.

Beyond our existing model, further extensions may be useful in the future: First, our process model does not include cognitive load as explicit construct, which is a crucial variable for classroom instruction ( Longo and Orru, 2022 ) and is likely related to creativity ( Sun and Yao, 2012 ). Second, our current model is focused on individual creativity and does not explicitly include group work. If students work in groups, we need to copy the “person” and “process” plate in the model for every group member and draw additional arrows between the idea bundles of the group members, referring to their communication. Third, our model currently does not account for personal development over time. Such an extension would require a copy of the “person” plate for a next time step and drawing arrows from the creative product in the previous time step to the domain knowledge and creative affinity variables in the next time step.

Finally, we note that future work should validate our model beyond its utility as a distillation of the literature: In particular, empiric studies in education may reveal the actual strength of influence between variables; educational researchers should investigate whether the model can be used to assess instruction from the perspective of creativity; educators may validate the model's utility for teaching, and AIEd engineers may extend the model to a full-fledged computational model for practical applications. Such research does not only benefit our model but will deepen our understanding of creativity in education in its own right. As such, we hope that our model will form a symbiotic relationship with future research: being improved and revised by research, but also being useful as a conceptual tool to guide and support research.

Author contributions

JD and AK performed the original literature review and wrote the initial draft. BP performed the main revision work and distilled the initial graphical model. SK performed revision for text and model. KG and NP supervised the research and performed additional revision. All authors contributed to the article and approved the submitted version.

This work was supported by the German Research Foundation (DFG) under grant numbers PI 764/14-1 and PA 3460/2-1. The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ Not promoted by Guilford (1950) , one should add; his paper already mentions that creativity research should investigate not only how to detect creative potential but how to ensure circumstances in which creative potential can be realized.

2. ^ For brevity, we subsume intelligence tests under personality tests, even though that is inaccurate.

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Runco, M. A., Acar, S., and Cayirdag, N. (2017). A closer look at the creativity gap and why students are less creative at school than outside of school. Think. Skills Creat . 24, 242–249. doi: 10.1016/j.tsc.2017.04.003*

Runco, M. A., and Jaeger, G. J. (2012). The standard definition of creativity. Creat. Res. J . 24, 92–96. doi: 10.1080/10400419.2012.650092*

Sawyer, K. (2011). The cognitive neuroscience of creativity: a critical review. Creat. Res. J . 23, 137–154. doi: 10.1080/10400419.2011.571191*

Sawyer, R. K. (2006). Explaining Creativity: The Science of Human Innovation . Oxford: Oxford University Press.

Schubert, S., and Loderer, K. (2019). “Wie erkennt man Kreativität?” in Kreativität in der Schule–finden, fördern, leben , eds J. S. Haager and T. G. Baudson (Wiesbaden: Springer Fachmedien), 39–74. doi: 10.1007/978-3-658-22970-2_3*

Schultheis, H., and Jameson, A. (2004). “Assessing cognitive load in adaptive hypermedia systems: physiological and behavioral methods,” in Adaptive Hypermedia and Adaptive Web-Based Systems , eds P. M. E. De Bra and W. Nejdl (Berlin; Heidelberg: Springer), 225–234. doi: 10.1007/978-3-540-27780-4_26*

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Keywords: creativity, 4P model, graph-based model, literature review, artificial intelligence in education

Citation: Paaßen B, Dehne J, Krishnaraja S, Kovalkov A, Gal K and Pinkwart N (2022) A conceptual graph-based model of creativity in learning. Front. Educ. 7:1033682. doi: 10.3389/feduc.2022.1033682

Received: 31 August 2022; Accepted: 24 October 2022; Published: 07 November 2022.

Reviewed by:

Copyright © 2022 Paaßen, Dehne, Krishnaraja, Kovalkov, Gal and Pinkwart. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Niels Pinkwart, niels.pinkwart@hu-berlin.de

This article is part of the Research Topic

New Teaching and Learning Worlds - Potentials and Limitations of Digitalization for Innovative and Sustainable Research and Practice in Education and Training

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What is creativity in education?

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Human beings have always been creative. The fact that we have survived on the planet is testament to this. Humans adapted to and then began to modify their environment. We expanded across the planet into a whole range of climates. At some point in time we developed consciousness and then language. We began to question who we are, how we should behave, and how we came into existence in the first place. Part of human questioning was how we became creative.

The myth that creativity is only for a special few has a long, long history. For the Ancient Chinese and the Romans, creativity was a gift from the gods. Fast forward to the mid-nineteenth century and creativity was seen as a gift, but only for the highly talented, romantically indulgent, long-suffering and mentally unstable artist. Fortunately, in the 1920s the field of science began to look at creativity as a series of human processes. Creative problem solving was the initial focus, from idea generation to idea selection and the choice of a final product. The 1950s were a watershed moment for creativity. After the Second World War, the Cold War began and competition for creative solutions to keep a technological advantage was intense. It was at this time that the first calls for STEM in education and its associated creativity were made. Since this time, creativity has been researched across a whole range of human activities, including maths, science, engineering, business and the arts.

The components of creativity

So what exactly is creativity? In the academic field of creativity, there is broad consensus regarding the definition of creativity and the components which make it up. Creativity is the interaction between the learning environment, both physical and social, the attitudes and attributes of both teachers and students, and a clear problem-solving process which produces a perceptible product (that can be an idea or a process as well as a tangible physical object). Creativity is producing something new, relevant and useful to the person or people who created the product within their own social context. The idea of context is very important in education. Something that is very creative to a Year One student – for example, the discovery that a greater incline on a ramp causes objects to roll faster – would not be considered creative in a university student. Creativity can also be used to propose new solutions to problems in different contexts, communities or countries. An example of this is having different schools solve the same problem and share solutions.

Creativity is an inherent part of learning. Whenever we try something new, there is an element of creativity involved. There are different levels of creativity, and creativity develops with both time and experience. A commonly cited model of creativity is the 4Cs [i] . At the mini-c level of creativity, what someone creates might not be revolutionary, but it is new and meaningful to them. For example, a child brings home their first drawing from school. It means something to the child, and they are excited to have produced it. It may show a very low level of skill but create a high level of emotional response which inspires the child to share it with their parents.

The little-c level of creativity is one level up from the mini-c level, in that it involves feedback from others combined with an attempt to build knowledge and skills in a particular area. For example, the painting the child brought home might receive some positive feedback from their parents. They place it on the refrigerator to show that it has value, give their child a sketchbook, and make some suggestions about how to improve their drawing. In high school the student chooses art as an elective and begins to receive explicit instruction and assessed feedback. In terms of students at school, the vast majority of creativity in students is at the mini-c and little-c level.

The Pro-c level of creativity in schools is usually the realm of teachers. The teacher of art in this case finds a variety of pedagogic approaches which enhance the student artist’s knowledge and skills in art as well as building their creative competencies in making works of art. They are a Pro-c teacher. The student will require many years of deliberate practice and training along with professional levels of feedback, including acknowledgement that their work is sufficiently new and novel for them to be considered a creative professional artist at the pro-c level.

The Big-C level of creativity is the rarefied territory of the very few. To take this example to the extreme, the student becomes one of the greatest artists of all time. After they are dead, their work is discussed by experts because their creativity in taking art to new forms of expression is of the highest level. Most of us operate at the mini-c and little-c level with our hobbies and activities. They give us great satisfaction and enjoyment and we enjoy building skills and knowledge over time.  Some of us are at the pro-c level in more than one area.

The value of creativity in education

Creativity is valuable in education because it builds cognitive complexity. Creativity relies on having deep knowledge and being able to use it effectively. Being creative involves using an existing set of knowledge or skills in a particular subject or context to experiment with new possibilities in the pursuit of valued outcomes , thus increasing both knowledge and skills. It develops over time and is more successful if the creative process begins at a point where people have at least some knowledge and skills. To continue the earlier example of the ramp, a student rolling a ball down an incline may notice that the ball goes faster if they increase the incline, and slower if they decrease it. This discovery may lead to other possibilities – the student might then go on to observe how far the ball rolls depending on the angle of the incline, and then develop some sort of target for the ball to reach. What started as play has developed in a way that builds the student’s knowledge, skills and reasoning. It represents the beginning of the scientific method of trial and error in experimentation.

Creativity is not just making things up. For something to meet the definition of creativity, it must not only be new but also relevant and useful. For example, if a student is asked to make a new type of musical instrument, one made of salami slices may be original and interesting, but neither relevant nor useful. (On the other hand, carrots can make excellent recorders). Creativity also works best with constraints, not open-ended tasks. For example, students can be given a limit to the number of lines used when writing a poem, or a set list of ingredients when making a recipe. Constrained limits lead to what cognitive scientists call desirable difficulties as students need to make more complex decisions about what they include and exclude in their final product. A common STEM example is to make a building using drinking straws but no sticky tape or glue. Students need to think more deeply about how the various elements of a building connect in order for the building to stand up.

Creativity must also have a result or an outcome . In some cases the result may be a specific output, such as the correct solution to a maths problem, a poem in the form of a sonnet, or a scientific experiment to demonstrate a particular type of reaction. As noted above, outputs may also be intangible: they might be an idea for a solution or a new way of looking at existing knowledge and ideas. The outcome of creativity may not necessarily be pre-determined and, when working with students, generating a specific number of ideas might be a sufficient creative outcome.

Myths about creativity

It is important that students are aware of the components that make up creativity, but it is also critical that students understand what creativity is not, and that the notion of creativity has been beset by a number of myths. The science of creativity has made great progress over the last 20 years and research has dispelled the following myths:

  • Creativity is only for the gifted
  • Creativity is only for those with a mental illness
  • Creativity only lives in the arts
  • Creativity cannot be taught
  • Creativity cannot be learned
  • Creativity cannot be assessed
  • Schools kill creativity in their students
  • Teachers do not understand what creativity is
  • Teachers do not like creative students

The science of creativity has come a long way from the idea of being bestowed by the gods of ancient Rome and China. We now know that creativity can be taught, learned and assessed in schools. We know that everyone can develop their creative capacities in a wide range of areas, and that creativity can develop from purely experiential play to a body of knowledge and skills that increases with motivation and feedback.

Creativity in education 

The world of education is now committed to creativity. Creativity is central to policy and curriculum documents in education systems from Iceland to Estonia, and of course New Zealand. The origins of this global shift lie in the 1990s, and it was driven predominantly by economics rather than educational philosophy.

There has also been a global trend in education to move from knowledge acquisition to competency development. Creativity often is positioned as a competency or skill within educational frameworks. However, it is important to remember that the incorporation of competencies into a curriculum does not discount the importance of knowledge acquisition. Research in cognitive science demonstrates that students need fundamental knowledge and skills. Indeed, it is the sound acquisition of knowledge that enables students to apply it in creative ways . It is essential that teachers consider both how they will support their students to acquire the necessary knowledge and skills in their learning area as well as the opportunities they will provide for applying this knowledge in ways that support creativity. In fact, creativity requires two different sets of knowledge: knowledge and skills in the learning area, and knowledge of and skills related to the creative process, from idea generation to idea selection, as well as the appropriate attitudes, attributes and environment.

Supporting students to be creative

In order for teachers to support students to be creative, they should attend to four key areas. Firstly, creativity needs an appropriate physical and social environment . Students need to feel a sense of psychological safety when being creative. The role of the teacher is to ensure that all ideas are listened to and given feedback in a respectful manner. In terms of the physical environment, a set of simple changes rather than a complete redesign of classrooms is required: modifying the size and makeup of student groups, working on both desks and on whiteboards, or taking students outside as part of the idea generation process can develop creative capacity. Even something as simple as making students more aware of the objects and affordances which lie within a classroom may help with the creative process.

Secondly, teachers can support students to develop the attitudes and attributes required for creativity , which include persistence, discipline, resilience, and curiosity. Students who are more intellectually curious are open to new experiences and can look at problems from multiple perspectives, which builds creative capacity. In maths, for example, this can mean students being shown three or four different ways to solve a problem and selecting the method that best suits them. In Japan, students are rewarded for offering multiple paths to a solution as well as coming up with the correct answer.

Thirdly, teachers can support the creative process . It begins with problem solving, or problem posing, and moves on to idea generation. There are a number of methods which can be used when generating ideas such as brainstorming, in which as many ideas as possible are generated by the individual or by a group. Another effective method, which has the additional benefit of showing the relationships between the ideas as they are generated, is mind-mapping. For example, rather than looking at possible causes of World War Two as a list, it might be better to categorise them into political, social and economic categories using a mind map or some other form of graphic organiser. This creative visual representation may provide students with new and useful insights into the causes of the war. Students may also realise that there are more categories that need to be considered and added, thus allowing them to move from surface to deep learning as they explore relationships rather than just recalling facts. Remember that creativity is not possible without some knowledge and skills in that subject area. For instance, proposing that World War Two was caused by aliens may be considered imaginative, but it is definitely not creative.

The final element to be considered is that of the outcomes – the product or results – of creativity . However, as with many other elements of education, it may be more useful to formatively assess the process which the students have gone through rather than the final product. By exploring how students generated ideas, whether the method of recording ideas was effective, whether the final solutions were practical, and whether they demonstrated curiosity or resilience can often be more useful than merely grading the final product. Encouraging the students to self-reflect during the creative process also provides students with increased skills in metacognition, as well as having a deeper understanding of the evolution of their creative competencies. It may in fact mean that the final grade for a piece of work may take into account a combination of the creative process as observed by the teacher, the creative process as experienced and reported by the student, and the final product, tangible or intangible.

Collard, P., & Looney, J. (2014). Nurturing creativity in education . European Journal of Education, 49 (3), 348-364.

Craft, A. (2001). An analysis of research and literature on creativity in education : Report prepared for the Qualifications and Curriculum Authority.

Runco, M. (2008). Creativity and education . New Horizons in Education, 56 (1), 96-104.

[i] Kaufman, J. C., & Beghetto, R. A. (2009). Beyond big and little: The four C model of creativity .  Review of General Psychology,  13(1), 1-12.

By Tim Patston

PREPARED FOR THE EDUCATION HUB BY

creativity in education articles

Dr Tim Patston

Dr Tim Patston is a researcher and educator with more than thirty years’ experience working with Primary, Secondary and Tertiary education providers and currently is the leader of consultancy activities for C reative Actions . He also is a senior adjunct at the University of South Australia in UniSA STEM and a senior fellow at the University of Melbourne in the Graduate School of Education. He publishes widely in the field of Creative Education and the development of creative competencies and is the featured expert on creativity in the documentary Finding Creativity, to be released in 2021. 

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Anthony D. Fredericks Ed.D.

How Education Quashed Your Creativity

Why it's difficult to find creative answers..

Posted August 13, 2021 | Reviewed by Davia Sills

  • Our education (K-college) is excessively focused on getting right answers, rather than promoting creative responses.
  • An overemphasis on standardized testing negatively impacts our creativity.
  • The kinds of questions we're asked in school severely limits our creative output.

For much of our lives, we are predisposed to look for a single solution to a single problem (e.g., What is 2 + 2? What is the state capital of North Dakota? Who wrote the Declaration of Independence?). We have been “brainwashed” to think that for every problem, there is one, and only one, way to solve that problem. Much of our educational experiences have been focused on learning the right answers to pre-established questions. Seldom have we been offered the opportunity to consider that there might be a multitude of potential responses to any problem. The “one-problem, one-answer” syndrome has been thoroughly ingrained into almost every educational curriculum, irrespective of grade level or subject matter.

Sir Ken Robinson put this all into perspective when he wrote, “…too often our educational systems don’t enable students to develop their natural creative powers. Instead, they promote uniformity and standardization. The result is that we’re draining people of their creative possibilities and… producing a workforce that’s conditioned to prioritize conformity over creativity.” In short, our educational system is focused more on getting the right answers (thinking inside the box) than on promoting creative possibilities (thinking outside the box).

What are the ramifications?

The implications can be staggering. Logic supports the notion that an excessive focus on a one-right-answer mentality forces us into a “don’t take any risks” mindset. This obsession with getting the right answer (a proven consequence of an over-emphasis on standardized testing) conditions us not to take chances… it teaches us not to be creative. That’s because when we make too many mistakes, we get a low test score. Get a low score, and you may deprive yourself of a college education (as a result of your SAT scores), a chance at graduate school (via your GRE scores), or an occupational advancement (via your score on the LSAT [law school] exam, MCAT [medical school] exams, or PAPA [teacher certification assessment], for example).

Simply put, we are not taught how to be creative; rather, our education is focused more on “mental compliance” than it is on innovative expression. Robert Sternberg writes, “Creativity is a habit. The problem is that schools… treat it as a bad habit…. Like any habit, creativity can either be encouraged or discouraged.”

Michael Roberto, in his book Unlocking Creativity, further cements this view when he states, “Our schools [are] discouraging creative students in a variety of ways. A stream of research has shown that teachers claim to value qualities such as independent thinking and curiosity, yet they reward behaviors such as obedience and conformity.” As an educator for more than 50 years, that concerns me!

tjevans/Pixabay

Because of the prevalence of exams in our lives (it has been estimated that students take nearly 2,500 tests, quizzes, and exams during their school years, grades K-12), we have a tendency to stay in a comfort zone: a focus on right answers. Occasionally, we may be asked to voice a creative response in class (“What do you think are some of the long-range consequences of our current trade policy with China?”), but are hesitant to do so on the belief that the teacher may be looking for a specific and particular response. Perhaps our creative answer is not the one the teacher was looking for. We may have stepped outside the bounds of what was expected and into the territory of the unknown.

The objective of most classroom lessons often becomes: Right answers get rewarded; innovative or inventive responses are frequently censured. In short, we are creating a generation of factual masters and a decided dearth of creative thinkers.

How to enhance your personal creativity

Fortunately, there are ways we can boost creative thinking at any age.

1. Ask the right questions.

On a Zoom meeting, a conference call, monthly department meeting, or any other kind of group discussion, try to avoid asking the following questions: “What is the answer?” or “What is the solution?” By posing those queries, you are severely limiting a multiplicity of responses simply because the group is now focused on finding the answer or the solution… rather than on generating a vast array of potential answers or solutions. More appropriate questions might include, “What are some possibilities here?”; “How many different ways can we look at this?”; or “What are some of the impediments we have to overcome?” In short, ask questions for which there may be a wide variety of responses, rather than questions that limit the number or type of responses.

Convincing research has overwhelmingly demonstrated that we tend to think based on the types of questions we are asked [emphasis added]. (Incidentally, during your educational career , you were asked approximately 400 classroom questions a day, or roughly 72,000 questions during any school year. There’s an abundance of data to show that about 80 percent of those questions were literal or simple recall questions.) Thus, if we ask questions for which there is the expectation of a single “correct” answer, that’s all we’ll get. On the other hand, if we pose questions that naturally generate a multiplicity of responses, then the collective creativity of the group is enhanced considerably.

creativity in education articles

2. Work backward.

Imagine writing a press release for a brand-new product long before you have even begun to design that product. Well, that’s what the folks at Amazon do. When they conceive a new product, the team sits down and drafts a full and complete press release for that product as their initial step. What are the most compelling features of the new product? What are the most significant values of the new product to consumers? What is their primary audience, and how will they target the new product to that audience? What benefits will customers get from the new product? Enormous time and energy are devoted to crafting a compelling press release long before (months or years) the product is ever ready for the marketplace.

In short, product developers must travel into the future and imagine the day the product is released to the public. Then, they are tasked with moving backward in time to conjure up the steps (in reverse order) that will be necessary to make that press release a reality. Backward thinking offers a new reality. A study in 2004 conclusively proved that when participants were tasked with completing a project from back to front (rather than the more logical front to back), they achieved higher levels of creativity. The researchers noted that participants were forced to utilize abstract, high-level, and conceptual thinking rather than logical, concrete, and time-worn thinking.

Kathryn Haydon. “When You Say You’re Not Creative…” Psychology Today.com (January 4, 2019). ( https://www.psychologytoday.com/us/blog/adventures-in-divergent-thinkin… ).

Ken Robinson. Out of Our Minds: Learning to be Creative . (New York: Wiley, 2011).

Robert J. Sternberg and T.I. Lubert. Defying the Crowd: Cultivating Creativity in a Culture of Conformity . (New York: Free Press, 1995).

Michael A. Roberto. Unlocking Creativity: How to Solve Any Problem and Make the Best Decisions by Shifting Creative Mindsets . (New York, Wiley: 2019).

Anthony D. Fredericks. Ace Your First Year Teaching: How to be an Effective and Successful Teacher . (Indianapolis, IN: Blue River Press, 2017).

Jeff Dyer and Hal Gregersen, “How Does Amazon Stay at Day One?,” Forbes , August 8, 2017.

Anthony D. Fredericks Ed.D.

Anthony D. Fredericks, Ed.D. , is Professor Emeritus of Education at York College of Pennsylvania. His latest book is In Search of the Old Ones: An Odyssey Among Ancient Trees (Smithsonian Books, 2023).

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Creativity and Artificial Intelligence—A Student Perspective

Rebecca marrone.

1 The Centre for Change and Complexity in Learning, The University of South Australia, Adelaide 5000, Australia

Victoria Taddeo

Gillian hill.

2 Centre for Research in Expertise Acquisition, Training and Excellence, School of Psychology, University of Buckingham, Buckingham MK18 1EG, UK

Associated Data

Restrictions apply to the availability of these data. Data was obtained from students and are available from the authors with the permission of the students.

Creativity is a core 21st-century skill taught globally in education systems. As Artificial Intelligence (AI) is being implemented in classrooms worldwide, a key question is proposed: how do students perceive AI and creativity? Twelve focus groups and eight one-on-one interviews were conducted with secondary school-aged students after they received training in both creativity and AI over eight weeks. An analysis of the interviews highlights that the students view the relationship between AI and creativity as four key concepts: social, affective, technological and learning factors. The students with a higher self-reported understanding of AI reported more positive thoughts about integrating AI into their classrooms. The students with a low understanding of AI tended to be fearful of AI. Most of the students indicated a thorough understanding of creativity and reported that AI could never match human creativity. The implications of the results are presented, along with recommendations for the future, to ensure AI can be effectively integrated into classrooms.

1. Introduction

There is a strong consensus that creativity is a crucial 21st-century competency. Education systems report the importance of creativity ( Patston et al. 2021 ). Similarly, Artificial Intelligence (AI) is significantly impacting a growing number of fields, including education ( Gabriel et al. 2022 ). Globally, education systems are developing strategic plans to embed AI in classrooms adequately (see Singapore, Estonia, Australia, New Zealand, and Scotland, to name a few) ( Gabriel et al. 2022 ). Whilst the importance of both creativity and AI are well established, less is known about how students perceive and value the relationship between AI and creativity. This paper will explore how students perceive AI and creativity, and endeavour to ensure that education systems support the development of both competencies.

1.1. Artificial Intelligence in Education

Artificial Intelligence (AI) is a branch of computer science that uses algorithms and machine learning techniques to replicate or simulate human intelligence ( Helm et al. 2020 ). There are three types of AI: narrow AI, general AI, and Artificial Superintelligence. Narrow AI is the most common and realized form of AI to date. It is very goal-orientated and uses machine learning techniques to achieve one specific goal or task (e.g., image and facial recognition, Siri/Alexa). General (or deep) AI is AI that is deemed on par with human capabilities (e.g., AI that can discern the needs and emotions of other intelligent beings). Thirdly, Artificial Superintelligence is AI that is more capable than humans (similar to a sci-fi movie portrayal of AI that supersedes humans in every regard) ( Hassani et al. 2020 ).

Within the education context, artificial intelligence development will likely remain in the form of narrow AI. Current educational technologies include speech semantic recognition, image recognition, Augmented Reality/Virtual Reality, machine learning, brain neuroscience, quantum computing, blockchain, et cetera. These technologies are rapidly being integrated within classrooms. An ever-increasing number of artificial intelligence education products are being applied to K-12 education ( Yufeia et al. 2020 ). Literature studies show that artificial intelligence technology in education has been used in at least 10 aspects: “the (i) automatic grading system, (ii) interval reminder, (iii) teacher’s feedback, (iv) virtual teachers, (v) personalized learning, (vi) adaptive learning, (vii) augmented reality/virtual reality, (viii) accurate reading, (ix) intelligent campus, and (x) distance learning” ( Yufeia et al. 2020, p. 550 ).

The Artificial Intelligence in Education (AIED) community emphasises the creation of systems that are as effective as one-on-one human tutoring ( VanLehn 2011 ). Over the last 25 years, there have been significant advances toward achieving that goal. However, by enforcing the human tutor/teacher as the gold standard, a typical example of AIED practices has often included a student working with a computer to solve step-based problems focused on domain-level knowledge in subjects such as science and mathematics ( Trilling and Fadel 2009 ). However, this example does not consider the recent developments in education practices and theories, including introducing 21st-century competencies. The 21st-century competency approach to education emphasises the value of general skills and competencies such as creativity. Today’s classrooms strive to incorporate authentic practices using real-world problems in collaborative learning settings. To maintain its relevance and increase its impact, the field of AIED has to adapt to these changes.

1.2. What Does Creativity in an AI Classroom Look Like?

Boden ( 1998 ), in her paper, suggests that AI techniques can be used to enhance creativity in three ways: ‘by producing novel combinations of familiar ideas; by exploring the potential of conceptual spaces; and by making transformations that enable the generation of previously impossible ideas’ (p. 1). While there have been attempts to combine the fields of AI and creativity, and to define them through the emerging field of computational creativity, it has often ended in confusion. Computational creativity (CC) (also known as artificial creativity or creative computation) places AI/computers at the centre of creativity ( Colton and Wiggins 2012 ). Computational creativity is underpinned by Rhodes’ 4P’s of creativity theory, which emphasises that creativity is an interaction between four factors: process, person, product, and press (environment) ( Rhodes 1961 ). While all four factors are crucial for human creativity, Cropley et al. ( 2021 ) have suggested that only two factors are important for human and artificial creativity: process (i.e., cognition), and product (i.e., outcome). Creative products are measured by novelty and effectiveness ( Cropley and Cropley 2012 ; Cropley and Kaufman 2012 ), where novelty refers to a new or original idea or concept, and effectiveness is the ability of the product or solution to achieve its desired result. Process is defined as the cognitive mechanisms of creativity and is key to understanding what artificial intelligence can offer to develop novel and effective solutions to problems. Therefore, to encourage the use of creativity and AI, educators should consider the process by which creativity has unfolded and/or the product of the creative endeavour.

There is emerging research on assessing the creative product using AI-based methodologies. Cropley and Marrone ( 2021 ) demonstrate how AI can successfully assess figural creativity using convolutional neural networks. Beaty and Johnson ( 2021 ), and Olson et al. ( 2021 ) also demonstrate the use of latent semantic analysis to assess the creativity of student responses to a traditional alternate uses task. While this is a growing field, this research focuses more on the outcome or product of creativity and less on the process.

1.3. The Process of Creativity and AI

Students should be aware of how AI can support their creativity and learning. Modern education favours problem-solving-based pedagogies, which emphasise the importance of fostering children’s ability to think creatively. However, considerable research supports the existence of a creativity slump in younger children across subjects ( Torrance 1968 ; Tubb et al. 2020 ). One proposal for this slump is an overly structured school curriculum and a lack of play-based learning activities in educational practices ( Alves-Oliveira et al. 2017 ). Emerging research shows how AI can support skills often associated with creativity, such as curiosity ( Gordon et al. 2015 ), grit, persistence, and attentiveness ( Belpaeme et al. 2018 ). The ability of AI to support creativity is also being explored. Kafai and Burke ( 2014 ), in their study, report that the purpose of AI in education is to encourage and support skills such as problem-solving and creativity through collaboration with AI, rather than simply acquiring knowledge in the specific domain. The paper suggests that AI can help creativity unfold and is therefore related to the process through which creativity occurs. Furthermore, Ryu and Han ( 2018 ) studied Korean school teachers’ perceptions of AI in education and report that teachers with experience in leadership recognized that AI would help to improve creativity. Therefore, it is proposed that AI in education may address some of the main concerns associated with the creativity slump, particularly an emphasis on the creative process. This may help improve creative thinking in students and comfortability using AI, and to adequately prepare students to enter the modern workforce.

To successfully combine and integrate AI and creativity, we must better understand how students perceive the relationship between the two concepts. To understand this perception, we should also situate AI with other predominant creativity theories, including the 4C model of creativity.

1.4. A 4C Approach to AI

Creativity and AI in an educational context can be viewed through a 4C model ( Kaufman and Beghetto 2009 ). Mini-c or ‘personal creativity’ embodies the personal ( Runco 1996 ; Vygotsky 2004 ) and developmental ( Cohen 1989 ) aspects of creativity. Mini-c relates to subjective self-discoveries that are creative to the individual involved and not necessarily others. An example may be an individual making a slight variation on a well-known recipe. Little-c is also called ‘everyday creativity’ and refers to something other people recognise as creative, such as generating a new recipe. Pro-c or ‘professional creativity’ is defined as becoming an expert in any field or discipline. An example may be the chef, Gordon Ramsey. Big-C or ‘legendary creativity’ is defined as eminent creativity and will be remembered for centuries. An example may be August Escoffier, who is credited as the founder of modern cuisine and has dramatically altered the field of cooking ( Beghetto et al. 2016 ).

Most obviously, AI can support creativity at the pro-c and potentially Big-C levels, as it can help extend expert knowledge in specific domains. Less obvious is how AI can support mini-c and little-c contributions. At the mini-c and little-c levels, the creative output is not as crucial as the self-discovery that occurs through the creative process. It is therefore essential to develop both an appreciation and understanding of when and where AI is most valuable, that is, in what narrow domains does AI best suit education, and how can AI be used to encourage mini-c and little-c contributions?

This research will investigate how students perceive AI and creativity, and the relationship between the two. We expect insights to highlight how AI can be designed to support creativity in the classroom.

2. Materials and Methods

2.1. participants.

Eighty secondary school students from four South Australian schools (mean age 15) participated in an eight-week programme. Students were tasked with the challenge of: ‘How do we sustain life on Mars?’ Sixty students completed this task as part of their regular science class. Twenty students completed this task as an extracurricular after-school programme. The programme’s content was identical, irrespective of whether the student participated in their regular science class or as an extracurricular activity. The same staff conducted both versions.

2.2. Method

Grounded theory (GT) is a structured yet flexible methodology that is appropriate when little is known about a phenomenon ( Chun Tie et al. 2019 ). Grounded theory investigates the experience of people and their responses and reactions and generates a theory. A defining characteristic of GT is that it aims to generate a theory that is grounded in the data. Considering there is minimal research on student perceptions of AI and creativity, this methodology was chosen.

2.3. Context

The students explored a variety of sub-problems related to their task; however, one task was around designing and building a Mars Rover. Those who engaged as part of their science class worked in groups of 4–5 students, and each team spent one week (four × 50-min lessons) engaging solely with artificial intelligence and building their Rover. For the other seven weeks, students engaged with the AI system, once a lesson for approximately 10 min each time (40 min per week over seven weeks). The students who engaged in the extracurricular version of this programme also were in groups of 4–5 and engaged with the AI system for six hours over a one-day, in-person event. The other lessons were hosted on Zoom and did not involve AI. The students physically built a Mars Rover using Fischer Technik kits and then engaged with an AI-based vision analytics tool to receive feedback on their build. Whilst the technology behind the vision analytics tool has been created by individuals at the pro-c level, its application in the classroom was created to elicit mini-c or little-c creativity in students. This is because the students use the system to get specific and targeted feedback on every step of their build. The students can then use this information to decide if the AI is helping them achieve their goals of creating the Rover. Once students had built their Rover, the vision analytics system could scan it and upload it into a 3D virtual environment, where students could drive their Rover on Mars. Here they learnt about planetary factors, such as gravity, and terrain.

This was an open-designed task with no instructions, and students were instructed to be creative with their choices and designs. They received creativity training, specifically: “What is creativity and what is it not?”.

2.4. Data Analysis

Twelve focus groups were conducted with the students engaged with this project in their regular science lessons. Eight one-on-one interviews were conducted with those students who participated in this programme as an extracurricular programme. The questions asked to all the students were the same, regardless of whether they engaged in their class or as an extracurricular activity. The interviews were framed around how students perceive both AI and creativity. See Appendix A for the interview questions. A content analysis methodology was used to analyse the meaning of the participants’ narratives. Fraenkel et al. ( 2006 ) define content analysis as ‘a technique that enables researchers to study human behaviour in an indirect way, through an analysis of their communications (p. 483). The purpose of content analysis is to explore participants’ verbal communication and social behaviour without influencing it. Content analysis allows a researcher to interpret what is being communicated, why it is being communicated, and with what effects ( Wagenaar and Babbie 2004 ). An objective codification process characterises content analysis and involves placing coded data into key categories and more abstract concepts.

One conceptualisation of creativity and AI that emerged from the students’ remarks was labelled ‘Social Factors’. Typical categories defining the concept were ‘conversation and lack of awareness’, ‘student interest’ and ‘social intelligence/social skills’. Another different conceptualisation identified in the content units was ‘affective’. Typical categories defining this concept were ‘comfortable with AI’ and ‘not comfortable with AI’. A different kind of conceptualisation was observed in the cognitive view expressed by some of the students interviewed. This led to the concept ‘Technological Factors’. The typical categories here were ‘access and use of AI’, ‘technology focused’, ‘robotics’, and ‘computers’. The final concept was labelled as ‘Learning Factors’. The typical categories related to the student’s current school environment were ‘AI provides a learning aid’, and ‘creativity takes time’. These concepts are shown in Appendix B , along with the content units from which they were derived, and the categories defined by these content units.

3. Results and Discussion

This study aimed to understand how the students view the relationship between AI and creativity. This topic was addressed through a content analysis interpretation of the students’ responses to key questions. The results highlight that the students in the study understood the relationship between AI and creativity as four fundamental concepts: social, affective, technological and learning factors.

3.1. Social Factors

The results from the interviews suggest that secondary school students in Australia hold opinions that AI can negatively impact their social skills. The AI facilitators/barriers category tended to include negative views and perceptions of AI. Previous research notes that AI will drive us into roles that require more social skills and typically encourage these social-based roles ( Deming 2017 ; Makridakis 2017 ). However, the students believed that AI would negatively impact their social skills. Comments such as ‘AI can make people lack ‘social-wise’. AI can make social intelligence weaken a little bit, which can affect them (students), and another comment: ‘Well, if we’re talking about robots and such for computers and phones and digital media social media, that kind of stuff…it’s taking away from people’s social lives, and they’re just more concerned about having a digital platform to present themselves on, rather than focusing on presenting themselves in the physical world.’. One student reported that getting AI to become ‘a mainstream thing so everyone can speak to everyone on it, so we can ask whole communities and get out with a lot of people’ was essential to changing the conversations about AI. These somewhat negative perceptions may hinder students’ willingness to adopt AI technologies in their classrooms. Chai et al. ( 2021 ) demonstrate that the intention to learn AI in primary school students is influenced by the students’ perception of the use of AI for social good. Furthermore, Chai et al. ( 2020 ) highlight that students perceive the purpose of learning of AI for social good as the most powerful predictor for their behavioural intention to continue learning AI. The students also reported that AI will never work in fields where human skills are required for problem-solving. When asked whether AI can match human skills, one focus group reported that the father of a participant in this group was a pilot. They mentioned that it was crucial AI never entered the cockpit as humans should be tasked with solving a complex problem like flying a plane. Interestingly, every member of this group agreed and seemed apparently unaware of the level of technology that is associated when flying. This represents a gap in student understanding of how AI can be used to assist humans. The students in this group failed to see the value of AI as a teammate and solely viewed this role as a human skill. Further emphasis should be placed on educating students on the role of human–AI teaming, and that AI can support humans, even in seemingly social or complex situations. The belief that AI can negatively hinder their social skills also represents an opportunity to demonstrate how AI can benefit social skills and enhance connections across communities.

3.2. Affective Factors

Students reported various affective responses to AI. Those students who verbally reported feeling more familiar with AI also reported feeling more comfortable using AI technologies. However, the students who said they were not sure what AI was, also said they felt less comfortable defining AI, as well as integrating it into their classrooms. This finding is supported by both Chiu ( 2017 ), and Teo and Tan ( 2012 ). These authors highlight that a positive attitude towards technology can explain one’s intention of using the technology. One student reported feeling comfortable because he had ‘all the safety programmes on it (his computer)’, so he reported having trust in his AI systems. Another student responded, ‘depends on the type of AI, so, I guess computers and programming and telling a computer instructions’. When prompted, they reported they wouldn’t feel as comfortable using ‘robots and machines’. This transparency in the AI system relates to an increase in trust in the AI. This is in line with previous research that transparency and the avoidance of ‘black box’ suggestions can foster AI adoption. This is referred to as explainable AI ( Lundberg et al. 2020 ).

3.3. Technological Factors

Interestingly, the majority of the students’ perceptions of AI were related to technological factors. Categories such as advanced technology, automation, coding/programming, futuristic, not human and robots, all had a lot in common. Students typically thought of AI as robots or computer-based, as this is how they interact with AI in their daily lives. These comments can be interpreted as the students possessing quite a limited view of AI applications, and they all struggled to move beyond the idea that AI is more than robots and computers. Several students felt that AI was a ‘futuristic’ phenomenon and was not as impactful in their current lives. All students reported that AI, to them, included some form of robotics. Chiu et al. ( 2021 ) and Chiu and Chai ( 2020 ) suggest that students should learn about AI by referring to real-life applications that they are likely to encounter in their daily experiences.

When asked if AI can ever match human creativity, students reported that, despite AI being technically superior to humans, human creativity will always be a uniquely human trait that should be fostered. One student commented, ‘Basically, most things in artificial intelligence are made by humans so, unless we actually create a robot which can be a human, it probably won’t be able to match the creativity of humans.’. The students who did believe that AI could match human creativity suggested that ‘maybe over time, when technology gets a lot more advanced, I think that it would be eventually possible to be as creative as humans’. Thus, they didn’t think AI could currently match human creativity but may do so in the future. When asked ‘do you think AI could ever match human creativity?’ One student made a very interesting comment. She said, ‘Yes, kind of. It’s a very interesting question. I think it can spark creativity. I don’t know if AI itself (can be creative). I don’t know if a robot can be creative because, in order for a robot to be creative, someone has had to create the robot and give it its creativity as such, so I don’t know if they can be creative themselves, but I think they can spark creativity.’. Therefore, they view AI as a way to facilitate or ‘spark’ creativity. Based on these comments, it is suggested that AI should be used to enhance creativity. Markauskaite et al. ( 2022 ), in their recent paper, demonstrate how AI can be used to support creativity across different age groups. The authors polylogue provides concrete suggestions based on a 4C theory of creativity approach on how and where AI can be used to enhance creativity, particularly for students.

3.4. Learning Factors

The most frequent and mentioned categories are related to the concept of learning factors. The students reported a positive view of AI and that it can support them to access information more efficiently; it can promote global connections, support their ideas, and aid learning. The students also reported that the benefits of creativity include time management and increasing their novel ideas. However, students also reported that their current school environments sometimes negatively impact their ability to exhibit creativity. Unsurprisingly, students mentioned not having enough time to be creative and that assignments were not designed to allow creativity to develop, indicated by comments such as ‘sometimes you can’t (be creative); sometimes you do have a set structure of things that you have to follow, and you can’t always be creative, which can sometimes be a bit sad because you want to do something interesting but sometimes you know you have to follow a set structure for an assignment or something’. The students provided suggestions on how their learning environments could support creativity. The students felt that AI could help develop their creativity by encouraging independent thinking and creating opportunities to be creative, such as encouraging ‘new ways to approach different situations’. Another student mentioned, ‘Also, if you’re trying to make a robot move down a path or something, sometimes it’s going to bump into things and it’s going to, you know, go a bit wonky, so you’ve got to think out of the box and you, hang on a second, what’s going wrong here and then backtrack kind of thing, thinking in a different mindset, I guess, to how you usually think.’.

The students think AI can assist creativity when asked to deepen their thoughts in their learning. It is suggested that schools adopt opportunities for students to engage with creativity and AI as the students desire to engage in these activities.

3.5. Theoretical and Practical Contribution (From 4C to 4AI)

The students’ perceptions of AI varied; those more comfortable with AI had a more comprehensive understanding of the concept. This is in line with the research on trust with AI research ( Ashoori and Weisz 2019 ). Similarly, those who accurately defined creativity and valued the competency tended to think AI could never match human creativity. However, what was notable was that, when students were asked to define AI, they had a very limited understanding of the concept and tended to view AI as general AI or Artificial Superintelligence. The students had experienced an intensive programme using narrow AI, so it was surprising that they did not acknowledge this. Adopting a 4C approach to these results, we propose that the students do not value what we have termed ‘everyday-AI’ (a combination of mini-c and little-c).

It is proposed that the effective integration of AI into classrooms must address the misconceptions students may have about AI. By extending the 4C theory of creativity, we propose a ‘4AI model of Artificial Intelligence’. Following the same principles of the 4C model, we suggest mini-AI, little-AI, Big-AI and legendary-AI. Students described an evident appreciation of Big and legendary AI but did not appear to appreciate the mini or little AI (despite the AI tool being created to support mini-c and little-c). Drawing analogies with the 4C theory of creativity, we propose that thinking about four aspects of AI, perhaps as a ‘4AI Model of Artificial Intelligence in Education’ may be useful. Therefore, educators should focus on this aspect as it is unlikely that Big- or legendary-AI will be as frequently experienced by students in the same way that children are more likely to experience mini-c and little-c. This could include explaining the myths and misconceptions of AI and encouraging students to look for and appreciate examples of mini- or little-AI in their everyday lives. There is also the suggestion that, as with creativity, where there is teaching with creativity, for creativity, and about creativity, there should be teaching for AI, with AI, and about AI. Within these three domains, mini- and little-AI can be explored. It is proposed that students would increase their realistic understandings of AI over time, and some of the issues raised by the students who participated in this programme could be minimised.

3.6. Future Research

This study investigated student perceptions of AI and creativity and has proposed a 4AI model of creativity and AI. Future research could establish this model through both qualitative and quantitative methods. Quantitively, AI-based tasks could be employed in classrooms, delineating mini-AI (perhaps around personalized feedback in learning) versus little-AI. Furthermore, this model could be compared against pre- to post-measures of creativity. Further qualitative work could explore broader perceptions of everyday AI in children and adolescents. Finally, future research should focus on increasing students’ limited views of AI to incorporate more of what AI entails and how widely it permeates society and their learning environments ( Yufeia et al. 2020 ).

3.7. Limitations

This study has several limitations. First, this study was limited to secondary school students in South Australia, Australia. Further research should examine and compare K-12 students’ perceptions from other countries and demographics. Secondly, the students reported issues with the AI system effectively working every time they used it. These issues may have contributed to some poorer attitudes for students, if this was their first experience working with AI. Thirdly, whilst the interviews provided rich and in-depth insights into student perceptions, more empirical attitude measures could have been used, which would have provided further insights.

4. Conclusions

The interviews highlighted that the students view the relationship between AI and creativity from four key concepts: social, affective, technological and learning factors. Most of the students reported that, although AI could never match human creativity, AI could certainly help them develop their creativity. A 4AI model of Artificial Intelligence has been proposed to help educators support mini-AI and little-AI experiences, which the findings show was overlooked by the students, despite these being the core of the programme they had experienced. Future research could focus on using AI to address the concerns students mentioned and be used to enhance their creativity.

Acknowledgments

The authors would like to acknowledge the participants and their teachers.

Creativity and Artificial Intelligence—a student perspective

Interview Questions for one-on-one interviews

Creativity:

  • What comes to mind when you hear the word ‘creativity’?
  • In what areas of your school life do you see creativity being beneficial?
  • What are the challenges associated with creativity?
  • Are some people more ‘creative’ than others?

I will now move into some questions on artificial intelligence.

  • 5. Do you know what AI is?
  • 6. How comfortable do you feel using AI?
  • 7. How often do you use AI—have you used it before?

Artificial Intelligence:

  • 8. What comes to mind when I say the words ‘Artificial Intelligence’?
  • 9. In what areas do you see AI being beneficial?
  • 10. What are the challenges associated with AI?
  • 11. Who can help bring AI into your classroom?
  • 12. What do you think needs to happen to see AI in a classroom?
  • 13. Do you want AI in your classroom?

Creativity and AI:

  • 14. What is the relationship between creativity and AI?
  • 15. Can AI be creative?
  • 16. What skills do you think are important for the future of work?
  • 17. How can we support these skills?
  • 18. Can AI ever match human creativity?

Due to nature of the focus groups, we condensed the above 18 questions into 11 questions

Interview Questions for Focus Groups

  • What comes to mind when I say the words ‘Artificial Intelligence’?
  • Do you know what AI is?
  • How comfortable do you feel using AI?
  • How often do you use AI—have you used it before?
  • How do you feel about AI in a collaborative learning environment?
  • Do you want AI in your classroom?
  • What was your experience working with Vianna? What did you like and did not like?
  • 8. What comes to mind when you hear the word ‘creativity’
  • 9. Do you think AI can ever match human skills/creativity in the future?
  • 10. What skills do you think are important for the future of work?
  • 11. Bearing your previous discussion in mind, in what ways were you and/or your group creative in this this project?

Content units, categories and concepts derived from the qualitative data.

Table A1 illustrates that the students in the study understood the relationship between creativity and AI in terms of four fundamental dimensions (referred to as ‘concepts’ in the table): social, affective, technological and learning factors.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, R.M., V.T. and G.H.; methodology, V.T. and R.M.; formal analysis, V.T. and R.M.; writing—original draft preparation, R.M., V.T. and G.H.; writing—review and editing, R.M., V.T. and G.H.; project administration, R.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of The University of South Australia (protocol code 203661 and date of approval 13 January 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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AI, Academic Integrity, and Creative Expression

If all academic and creative work is theft, is AI doing anything new? Martin Crawford argues that this new technology can be a launching point for managing knowledge and boosting creativity, rather than replacing human innovation.

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All academic work and all creativity is theft. The world exists as it does, science interprets and explains it, and so does art. But neither make anything new. A quotation attributed to Picasso and Wilde in various forms but originally from poet T.S. Eliot is a fine example. Eliot wrote, “Immature poets imitate, mature poets steal.” Picasso is claimed to have said something similar about artists and Wilde distilled it into the rather zingier “Talent borrows, genius steals.”

Perhaps each coined their version in isolation, perhaps one took (aka stole) directly from another, or perhaps it was passed through ethereal channels of communication and manifested itself in different forms – giving a slightly different tangibility to a thought that each of them abstractly possessed.

The same circumstances also exist with more pragmatic inventions. For example, it is possible that the pneumatic tyre was invented by John Dunlop and Robert William Thomson in isolation. A Google search for ‘who invented the pneumatic tyre’ yields a top result that leads with ‘Thomson is the rightful inventor of the pneumatic tyre’ and a second result of ‘Dunlop developed the first practical pneumatic or inflatable tyre for his son’s tricycle.’

These examples illustrate the concept that all knowledge, ideas, and thought already exist, that we are simply in the process of finding it – through science and creativity. AI tools such as ChatGPT are microcosms of the universe of potential knowledge that has existed since the beginning of time. In a far smaller way, every AI prompt is simply reaching into this pool of knowledge and pulling out an interpretation. So, where does this fit into our existing ideas about creativity, about research, and about academic honesty?

The argument over using ChatGPT in education is not really about students using it to cheat, or find information, or digest and summarize countless pieces of information into something that can be immediately quite useful. Nor is it about the viability of AI tools as a legitimate creative source. Rather, the concern is about academic honesty and whether we wish to preserve the current principles of academic writing. What is the best way to integrate this powerful new tool into our research and creative processes?

Academic writing draws upon existing ideas, applies source analysis, and summary – and also adds a new perspective through critical thinking. Good academic writing identifies trusted thinkers and sources, while adding original elements. Ultimately, academic writing weaves together threads of thought that are filtered, analyzed, assimilated, and digested to create something new. It is essentially a structured process for mining existing knowledge.

The immense vastness of the internet makes knowledge unmanageable.

A key part of the academic writing process has always been the correct citing of existing ideas and giving recognition to those academics who laid the foundation for the next evolution of newer ideas. Plagiarism regulations in academic institutions enforce this principle – as well as the importance of instilling the practice of citation, and the ethics behind it, in students and would-be academic writers.

I grew up in a time when the content of my essays was drawn from a pile of carefully selected library books. I would whittle down a high pile of books to just those that were most relevant, interesting, inspiring. Some books that I expected to be useful would turn out to be quite the opposite while others would surprise me with little morsels that would spur my thinking and lead me in new directions. Compared with today’s students, I had access to a vastly smaller amount of information but I learned how to search, summarize, and select – and these skills led me to interesting places in my own writing. A pile of books is tangible and, more importantly, manageable and can provide enough insight to prompt new ideas.

Contemporary students who type a search term into Google or a prompt into ChatGPT do not have this same opportunity. The immense vastness of the internet makes knowledge unmanageable – and thus we resort to engines that tell us what we should know and AI tools that summarize the information without necessarily revealing the original. There is a high propensity these days for knowledge to arrive on our screens pre-digested and pre-packaged. There is nothing left for us to do but swallow.

Then there is artistic creation, which generally relies on the assimilation of innumerable life experiences, observations, texts read, paintings viewed, music heard, interactions had, emotions felt, and so on. In essence, artists create in a way that is akin to AI itself. Thus, using AI to assimilate artistic creation is very close to an artist’s own process (particularly compared with an academic), and resembles the approach of a mature poet who steals from all that which existed before.

This idea, however, does not sit comfortably with many artists. To assimilate from human experience, including experiences of other artworks, surely offers more than what AI can. Nick Cave, for example, feels that AI creations are essentially stripped of meaning and suffering. And of course there are already several law suits underway – from The New York Times to authors including Jodi Picoult and Jonathan Franzen – against ChatGPT and its compatriots for the misappropriation of intellectual property without citation or payment. Essentially: plagiarism.

Whether in academic or creative writing, it would appear that those institutions that have allowed citation directly from ChatGPT have done so in order to embrace the inevitable onslaught of tech trends and also to be seen as progressive. But they have done so hastily, and without considering how it impacts the values of academic honesty and the promotion of critical thinking.

In my art classroom, students use AI to generate images and texts that then serve as prompts to their own creative processes, rather than use it to generate finished artworks based on those prompts. They also use the technology to compare artists and artworks through initial summaries of contexts, biographies, and criticisms. So, essentially AI is a jumping-off point. This method helps to take the unmanageable vastness of available knowledge and turn it into something a little closer to a pile of books on one’s desk. It has the capacity to encourage students – and all of us – to consider going off in different directions in our quest for creation.

Academic institutions have a responsibility to help students understand the positive and the negative of using technologies like ChatGPT. We now have such boundless information at our fingertips, but we must use it with care and discernment. AI does have the ability to boost and inspire creativity, yet the unfortunate reality is that artists whose contributions have fed this creativity often go uncredited. Similarly, it provides access to a vast array of academic knowledge, but we should all understand that, ultimately, this knowledge is the property of the creators, not the technology.

Immature prompters imitate, mature prompters steal.

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Herberger Institute Day: Celebrating creativity, collaboration, community

The annual event showcases diversity and innovation in arts education through performances, interactive activities and more.

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The Herberger Institute for Design and the Arts is preparing for a celebration of collaboration and innovation on Feb. 21 as it hosts its annual Herberger Institute Day at the Tempe campus and online from 11 a.m. to 5 p.m.

This year's event promotes inclusivity, interdisciplinary exchange and artistic excellence. It will be an immersive experience for students, faculty, staff and the wider ASU and local high school communities. 

This is the first year the event has opened up to groups outside university students and staff, marking a significant milestone for Herberger Institute Day.

Angela Storey, Fine Arts Academic Coach at Chandler Unified School District , expressed gratitude for the opportunity extended to Chandler Unified students.

 "This may be the first time for some of our students to be on a college campus," Storey said. "We hope it will help open their eyes to the thriving arts and culture departments on campus."

Attendees can participate in over 60 workshops, each offering a unique way for exploration and expression. From beginner piano to exploring AI in art creation, the workshops reflect the institute's commitment to showcasing how individuals can engage with the arts.

The event will also feature a Fashion Institute of Design & Merchandising workshop hosted by Cullanete Bloom, an associate teaching professor at the School of Art. 

This workshop provides a glimpse into the diverse pathways of artistic expression within the creative industry, aligning with the Herberger Institute's broader vision.

A Herberger Institute employee said throughout the day, there will be performances showcasing the talent and creativity of the Herberger Institute community. It's a moment to celebrate the power of artistic expression and its ability to unite people from diverse backgrounds.

"The goal is to try to explain that you can still be utilized in the arts in so many different ways," Danielle Pivonka, the executive administrative support specialist for the Herberger Institute, said. 

Herberger Institute Day is a testament to this belief, providing a platform for individuals to explore, create and connect in ways that transcend traditional boundaries.

The festivities will begin with a block party featuring live DJs, interactive tabling activities, games, trivia and a photo booth. It's a moment for the community to come together, share a meal and revel in the joy of creativity.

"An inviting and engaging photo capture environment will welcome the diverse audience," said Carlos Tarazon from the photo booth company MomentWave . "Our unique platform provides a live photo view for guests to enjoy as they orient their poses for their turn in front of our camera."

View this post on Instagram A post shared by ASU Herberger Institute (@asuherberger)

The event organizers, led by associate dean for the Office of Culture and Access Melita Belgrave, pursue an extensive vision for the event, aiming to construct an environment where creativity knows no bounds.

Belgrave's absence from the event due to illness emphasizes the team's dedication to organizing the event. Pivonka, who has stepped into the role of program coordinator in Belgrave's absence, is determined to ensure the event's success.

"I've had to step into the role of program coordinator even more than I already was," Pivonka said.

According to Pivonka, Belgrave envisions Herberger Institute Day as a beacon of creativity and community engagement. 

"Just hearing the way that she's talked about it (the event) and the things that we've discussed, I know that she imagines this day continuing in ways where more and more ASU students can participate," Pivonka said.

Edited by Sophia Braccio, Walker Smith and Grace Copperthite.

Reach the reporter at [email protected] and follow @asvargo on X.

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creativity in education articles

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A sociostylistic account of semantic creativity in nigerian english, esther robert.

This article examines the semantic features of Nigerian English (NE). It is based on and guided by Edgar Schneider’s dynamic model, Weinreich, and the idea of nativization introduced by Braji Kachru. A qualitative method was adopted in the collection and analysis of the data. The data were drawn from wide-ranging sources such as Nigerian literary works, Nigerian newspapers, social interactions and political rallies. The analysis indicates that the variety of English spoken in Nigeria addresses the sociocultural realities of the country, distinguishing this variety from other world English varieties. Semantic analyses revealed aspects such as semantic extension/semantic shift and coinages. The implication of semantic lexicalisation is that an existing word loses its denotative meaning and becomes only meaningful in relation to its context of use. Most coinages in Nigerian English present themselves in different morphological shapes. Some are compound words put together to express the speaker’s concepts. However, such compound words are very descriptive and transitional in nature. Examples of these include: Go-slow, Doctor-do-good, long-legs, legedez-benz and others functioning as slang, metaphors and euphemisms.

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CecureUs Announces Winners for the Secure and Inclusive Workplace Awards 2024

CecureUs Announces Winners for the Secure and Inclusive Workplace Awards 2024

CecureUs is proud to unveil the winners of the highly anticipated Secure and Inclusive Workplace Awards 2024. The awards ceremony, now in its third edition, celebrates organizations that have demonstrated exceptional commitment to fostering inclusive cultures, prioritizing employee wellness, and ensuring workplace safety. This year, the Secure and Inclusive Workplace Awards received an overwhelming response, with 170 nominations pouring in from esteemed corporate businesses across India. After a meticulous selection process involving an initial assessment, screening, in-depth interviews, and scoring compilation, 40 were shortlisted as finalists. Out of 40 finalists, 24 are the winners chosen by a distinguished jury panel comprising legal experts, activists, HR specialists, and government officials. We are thrilled to announce the winners in each category: Diversity & Inclusion (DEI) Award: • Amway India Enterprises • RRD Go Creative Employee Assistance Program (EAP) Award: • Dorling Kindersley Publishing Private Limited • G4S Corporate Services India Private Limited Prevention of Sexual Harassment (PoSH) Award: • Bio Med Healthcare Products Private Limited • Celestica India Private Limited • Chargeback Gurus Fintech Private Limited • DBS Bank India Limited • Hitachi India • IDP Education India Private Limited • IDP Education India Services LLP, Digital Campus • Kinaxis India Private Limited • Niterra India Private Limited • NoBroker Technologies Solutions Pvt. Ltd.

• OEC India Services Private Limited • Orange Health Labs • Quinnox Consulting Services • SaveBySwitching Global Solutions Private Limited • TVS Electronics Limited • Vajra Global Consulting Services LLP • Vestas R&D Chennai Private Limited • Vestas Wind Technology India Private Limited • WinWire Technologies India Private Limited Exemplary Award: • Apollo Health and Lifestyle Limited • Clarivate CecureUs extends heartfelt congratulations to all the winners for their outstanding contributions to creating secure, inclusive, and supportive workplaces. These organizations serve as exemplary models for the industry, embodying the values of diversity, equity, inclusion, and employee well-being. ''We are immensely proud to present the winners of the Secure and Inclusive Workplace Awards 2024," said Ms. Viji Hari, the Founder of CecureUs. "This event underscores our unwavering commitment to spotlighting organizations that prioritize safety, inclusion, and employee well-being. Each winning organization has not only met but exceeded the benchmarks set forth, demonstrating their dedication to creating environments where every individual feels valued and respected. We extend our sincerest congratulations to all the winners and commend their exemplary efforts in shaping the future of work,'' Viji added. For more information about the Secure and Inclusive Workplace Awards, please visit CecureUs website www.cecureus.com/awards. About CecureUs CecureUs is a leading organization dedicated to creating secure and harmonious workplaces through its offerings on Preventing Sexual Harassment, DEIB, and Employee Assistance Program. The Secure and Inclusive Workplace Awards is one of the initiatives to support and celebrate those who are making a difference in the corporate workplace.

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  1. Understanding Creativity

    Usable Knowledge Understanding Creativity New research provides insight for educators into how to effectively assess creative work in K-12 classrooms Posted June 25, 2020 By Emily Boudreau Understanding the learning that happens with creative work can often be elusive in any K-12 subject.

  2. What Is Creativity in Education? A Qualitative Study of International

    Abstract The concept of "creativity" as a desirable attribute within education is long-standing. The fields of education and creativity have developed, and periodically intersected with, government reports, policies, commentaries, and advice. Recently, an increasing number of countries have emphasized creativity in their official curricula.

  3. What creativity really is

    Creativity is the novelty-generating component of cultural evolution. As in any kind of evolutionary process, novelty must be balanced by preservation. In biological evolution, the...

  4. A Critical Review of Assessments of Creativity in Education

    Most importantly, assessments of creativity in education are useful in identifying the creative behaviors of all students and pointing the way to the educational interventions that will nurture each student's creative potential, thus leading to democratizing creativity in education.

  5. Children's Creativity: A Theoretical Framework and Systematic Review

    Within education, the importance of creativity is recognized as an essential 21st-century skill. Based on this premise, the first aim of this article is to provide a theoretical integration through the development of a framework based on the principles of complex dynamic systems theory, which describes and explains children's creativity.

  6. How does a creative learning environment foster student creativity? An

    Article 26 December 2017 Learning Environments for Academics: Reintroducing Scientists to the Power of Creative Environment Chapter © 2017 Introduction Creativity is becoming increasingly important in modern society (Beghetto and Kaufman 2014; Richardson and Mishra 2018; Yeh et al. 2012 ).

  7. Creative Learning in Education

    What's Creative About Creative Learning? Prior to exploring how creative learning can be supported in schools and classrooms, it is important to first address the question of what is creative about creative learning? Creative learning pertains to the development of new and meaningful contributions to one's own and others' learning and lives.

  8. PDF Creativity in Modern Education

    In the article, Creativity in Education System, says," Teacher should introduce innovative ways of teaching by giving priority to activity-based learning and enab le learning with experience and observation" (Chetty, n. d.) Many acad emics are inspiring leaders of education on applying creativity in education.

  9. Creativity and education: A bibliometric mapping of the research

    Creativity and education research is predominantly disseminated in journals specializing in creativity and emerges from the integrated knowledge generated in educational and psychological sciences. Finally, the results indicate that researchers in this field have investigated a wide range of topics and topics that can be grouped into four broad ...

  10. Creativity in Education: Teaching for Creativity Development

    Article Full-text available Dec 2023 Zakiyah Arifa Risna Rianti Sari Al Lastu Nurul Fatim Alif Cahya Setiyadi View Show abstract ... Creativity plays a crucial role in fostering innovative...

  11. Creativity and technology in teaching and learning: a ...

    Internationally, creativity is a widely discussed construct that is pivotal to educational practice and curriculum. It is often situated alongside technology as a key component of education futures. Despite the enthusiasm for integrating creativity with technologies in classrooms, there is a lack of common ground within and between disciplines and research about how creativity relates to ...

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    Benefits of Creativity in Education If additional reasons are needed to embed creativity in the engineering curriculum, consider the value of creativity at the level of the individual. D. H.

  13. A conceptual graph-based model of creativity in learning

    Teaching creativity is one of the key goals of modern education. Yet, promoting creativity in teaching remains challenging, not least because creative achievement is contingent on multiple factors, such as prior knowledge, the classroom environment, the instruction given, and the affective state of the student. Understanding these factors and their interactions is crucial for successfully ...

  14. Mindfulness and creativity: Implications for thinking and learning

    Existing research on creativity has examined its different relationships, connections, or variables—such as personality skills, neuroscientific or cognitive correlates of creativity, disciplinary knowledge, imagination, bodily thinking, or the ways that creativity emerges in real-world design settings, among others ( Runco, 2014 ).

  15. The science behind creativity

    Specifically, creativity often involves coordination between the cognitive control network, which is involved in executive functions such as planning and problem-solving, and the default mode network, which is most active during mind-wandering or daydreaming (Beaty, R. E., et al., Cerebral Cortex, Vol. 31, No. 10, 2021).

  16. What is creativity in education?

    Creativity is the interaction between the learning environment, both physical and social, the attitudes and attributes of both teachers and students, and a clear problem-solving process which produces a perceptible product (that can be an idea or a process as well as a tangible physical object).

  17. Creativity in Higher Education: A Qualitative Analysis of Experts

    Creativity is recognized as playing an important role in personal well-being and in social and economic innovation and as such has prompted significant developments in education [1,2].In Australia and beyond, creative and critical thinking skills have been embedded in school curricula, with efforts underway to design valid and reliable assessments of students' creativity [3,4].

  18. Creativity in primary schools: An analysis of a teacher's attempt to

    Volume 24, Issue 1 https://doi.org/10.1177/1365480220968332 PDF / ePub More Abstract The importance of creativity in education has been increasingly recognised by policy-makers and, as contemporary research argues, the way curricula are organised and implemented impact on children's creativity.

  19. Creativity in the Classroom

    A young person's schooling should make creativity a priority - kids need it in order to synthesize their learning and enjoy doing it. In addition to creating, students also need to share their ideas with the world.

  20. How Education Quashed Your Creativity

    Our education (K-college) is excessively focused on getting right answers, rather than promoting creative responses. An overemphasis on standardized testing negatively impacts our creativity. The ...

  21. Promoting creativity in early childhood education

    According to the results of the study, statements on the prominence of creative thinking mainly emphasized the child being able to express her/his emotions and thoughts effectively, developing the child's problem-solving skills, forming cause- effect relationships, and being able to create a different point of view towards events and situations.

  22. Creativity in Education: A Global Concern

    Creativity in education: A global concern 1 Global Education Review is a publication of The School of Education at Mercy College, New York. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, permitting all non-commercial use, distribution, and reproduction in any ...

  23. Creativity and Artificial Intelligence—A Student Perspective

    1. Introduction. There is a strong consensus that creativity is a crucial 21st-century competency. Education systems report the importance of creativity (Patston et al. 2021).Similarly, Artificial Intelligence (AI) is significantly impacting a growing number of fields, including education (Gabriel et al. 2022).Globally, education systems are developing strategic plans to embed AI in classrooms ...

  24. AI, Academic Integrity, and Creative Expression

    AI, Academic Integrity, and Creative Expression. 00:0000:00. All academic work and all creativity is theft. The world exists as it does, science interprets and explains it, and so does art. But neither make anything new. A quotation attributed to Picasso and Wilde in various forms but originally from poet T.S. Eliot is a fine example.

  25. Herberger Institute Day: Celebrating creativity, collaboration

    The Herberger Institute for Design and the Arts is preparing for a celebration of collaboration and innovation on Feb. 21 as it hosts its annual Herberger Institute Day at the Tempe campus and online from 11 a.m. to 5 p.m. This year's event promotes inclusivity, interdisciplinary exchange and artistic excellence.

  26. A Sociostylistic Account of Semantic Creativity in Nigerian English

    This article examines the semantic features of Nigerian English (NE). It is based on and guided by Edgar Schneider's dynamic model, Weinreich, and the idea of nativization introduced by Braji Kachru. A qualitative method was adopted in the collection and analysis of the data.

  27. CecureUs Announces Winners for the Secure and Inclusive Workplace

    Country: India. SHARE. CecureUs is proud to unveil the winners of the highly anticipated Secure and Inclusive Workplace Awards 2024. The awards ceremony, now in its third edition, celebrates organizations that have demonstrated exceptional commitment to fostering inclusive cultures, prioritizing employee wellness, and ensuring workplace safety.