A library team has to make a neglected room useful to teenagers. One person suggests brighter paint; another asks why teenagers avoid the room; a third proposes moving noisy activities elsewhere and inviting young people to test layouts. Which suggestion is creative? The answer depends on more than surprise. An unfamiliar idea can fail the purpose, while a modest change can be new to this setting and make the room work. Creativity involves producing or developing ideas that are both original in a relevant context and effective for a purpose, then testing what those judgments mean.
Creativity is not restricted to famous artists or a moment of sudden inspiration. It can appear in learning, technical work, social problem-solving and the arts. It also is not identical to producing many ideas in a timed exercise. This entry defines the broader construct, explains how researchers assess it, and separates claims about creative products, processes and people. The neighboring divergent-thinking entry concerns one family of ideation tasks; it cannot stand in for the whole of creativity.
Originality and effectiveness
Runco and Jaeger’s account of a standard definition describes creativity through originality and effectiveness. Originality asks whether an idea departs from what is already familiar in the relevant setting. Effectiveness asks whether it works, has value or meets a constraint. The second term changes meaning across domains: a bridge design must be safe, a joke may be effective if it lands with an audience, and an artwork may open a new way of seeing rather than solve a practical problem.
These criteria are necessary to avoid two confusions. A random arrangement can be novel without being useful or meaningful. A flawlessly copied solution can be effective without adding anything new. Yet neither criterion is simple to score. What counts as new depends on the comparison set, and what counts as valuable depends on the task, evaluators and time horizon. A once-unusual proposal can become routine; a rejected work can later be valued.
Creative activity often includes generating possibilities, choosing among them, revising and communicating a result. A person who produces one strong option may be more successful than someone who produces twenty weak ones. Evaluation can also be creative: noticing that the question is framed badly, or that a seemingly impractical suggestion can be adapted to a different constraint, may change what the group can do next.
This definition does not make creativity a moral virtue by itself. An original and effective method can serve a harmful end. Nor does it imply that every idea must be unprecedented to the world. A child’s new interpretation of a story may matter in learning even if scholars have seen the interpretation before. The level at which originality is judged needs to be named.
More than one scale of creative work
Kaufman and Beghetto proposed a Four C model to distinguish creativity in personal learning, everyday action, professional work and historically eminent accomplishment. Their “mini-c” emphasizes personally meaningful insight during learning. “Little-c” concerns everyday contributions, while “Pro-c” recognizes developed professional expertise, and “Big-C” refers to work judged eminent over time. The model is conceptual, not a test that assigns each person permanently to a rank.
The distinctions help prevent an unfair comparison. A learner who invents a way to understand a fraction has achieved something meaningful in her own development, even if mathematicians already know it. A designer making a useful service improvement may not transform a field. A celebrated invention often rests on years of training, collaboration and institutional support. Treating only the last case as creativity hides the processes through which people learn and contribute.
Scale also affects evidence. An evaluator may be able to judge whether a classroom response is unusual relative to peers. Establishing that a scientific contribution changed a field requires much longer observation and expert debate. The same short task cannot certify both. Claims about a person’s “creative potential” should therefore specify the domain and the type of achievement being predicted.
Plucker, Beghetto and Dow argued that myths and imprecise definitions have limited educational research on creativity. Their warning remains practical. A school can say it values creativity while rewarding only fast answers that match a key. A company can praise innovation while punishing experiments that do not immediately raise a metric. Before proposing a creativity program, an institution should name the kind of contribution it wants and the conditions in which that contribution is possible.
Products, processes and the people who judge them
Some studies evaluate a finished product. Amabile’s consensual assessment technique asks knowledgeable judges to assess creative work in a domain rather than applying one universal numerical rule to every task. Agreement among suitable judges can provide a useful measure for a specified set of products. It does not reveal every step in the making of those products, prove the creator’s inner experience or eliminate disagreements about cultural value.
Product judgments depend on what judges see. The same idea may seem stronger when a prototype shows how it operates, and weaker when it is described in unfamiliar language. An evaluation should consider whether judges understand the task, have relevant expertise and know the constraints the creator faced. Otherwise a score can reflect presentation or status as much as the quality of the work.
Process evidence asks different questions. How was the problem framed? Which alternatives were considered? What happened after feedback? Did the group test an idea against reality? Such evidence can explain how a result emerged and where it might improve. It also introduces interpretation. A polished process narrative may be constructed after the fact; a messy sketchbook may conceal insight. Neither product nor process alone is the complete construct.
Aalto-linked research by Pennanen and colleagues illustrates how judgments can shift with perceptual evidence. In a robot-drawing study, participants rated creativity after seeing different amounts of information about product, process and producer. Ratings increased as more of that evidence became available; the tested robot morphologies did not show a statistically significant difference in the main creativity ratings. This finding concerns human attribution in a particular robot-art setting. It is not a general definition of creativity, nor proof that a machine possesses or lacks a human creative process.
The example is useful precisely because it is narrow. When someone asks whether an AI system is creative, they may be judging the output, the visible generation process, the human contribution or an assumed capacity of the system. Different questions require different evidence. A person can reasonably find an output original and useful while remaining agnostic about whether its generator has intentions. The broad creativity concept should not be settled by a single human-robot interaction experiment.
How creative thinking is assessed
The OECD’s PISA 2022 creative-thinking framework describes a capacity to generate diverse and original ideas, and to evaluate and improve them. Its assessment uses open-ended tasks in expressive and problem-solving settings. That is a useful operational definition for a particular educational assessment. It is not a complete measure of every kind of creativity, including sustained artistic practice, professional invention or collective social change.
Generating diverse ideas is a common part of divergent-thinking tasks. A prompt may ask for several uses of an object or possible responses to a problem. Fluency, flexibility and originality can be scored in various ways. Such tasks reveal something about idea production under their particular instructions and time limits. They do not show whether a person can identify a workable idea, develop it through revision or carry it into a real setting.
Some assessments ask participants to improve an idea supplied by someone else. This matters because real creative work often begins with existing material. A redesign can be creative even when the first concept was borrowed; collaboration can distribute imagination, judgment and execution across people. A test that counts only new ideas from one person may miss these forms of contribution.
Measurement also depends on language and culture. A joke, design or social proposal may be judged differently by people who do not share the same context. A rare answer is not automatically better, and a familiar answer in one population can be unusual in another. Comparing scores across groups requires attention to the prompts, evaluators and opportunities people had to learn the domain.
The OECD report can help education systems ask where students are able to generate, evaluate and improve ideas. It cannot tell a particular teacher that a single classroom exercise caused a change in a student’s long-term creative capacity. Large assessments describe patterns under standardized conditions. They need to be interpreted alongside curriculum, resources and classroom work rather than used as a permanent label for an individual child.
Knowledge, imagination and revision
Creativity is often depicted as freedom from knowledge. In many domains, knowledge gives a person materials to recombine and standards for judging whether a proposal is sound. A musician needs some grasp of sound and form; a public-service team needs to understand law, access barriers and the people served. Expertise can also bring habitual assumptions that need questioning. The relevant issue is how knowledge is used, not whether it is present.
Imagination can generate an alternative to the current arrangement. It may involve mental simulation, analogy, memory or an unfamiliar connection. Yet an imagined possibility remains only one stage. A design has to meet physical constraints; a policy proposal has to consider whom it burdens; a story has to work as a form. Revision brings the imagined option into contact with evidence and response from others.
Beaty and colleagues review research on creative cognition and brain network dynamics, describing interactions among processes associated with spontaneous generation and cognitive control. Their review challenges a caricature in which one isolated “creative side” of the brain produces finished ideas. It does not provide a brain scan that diagnoses creativity in an individual or a simple exercise to activate a creativity circuit.
Creative work can alternate between loose exploration and stringent selection. During an early phase, a group may benefit from withholding immediate rejection long enough to consider unusual options. Later, it may need to compare those options with evidence and constraints. The sequence varies by task. An emergency repair may require rapid use of established expertise; an open design inquiry may allow broad exploration. Neither mode alone should be renamed “the creative process.”
People and conditions
A creative outcome rarely belongs only to an isolated individual. Tools, collaborators, institutions, materials and audiences shape what can be proposed and realized. The library-room example depends on teenagers’ accounts of their needs, the building’s constraints, budget and the staff’s ability to change procedures. Someone can produce a promising idea and still be unable to implement it because the institution does not permit a trial.
This matters when creativity is used as a demand. Asking employees to be innovative without time or authority can shift responsibility for a structural problem onto them. A school that celebrates original answers but grades only compliance sends conflicting signals. A more credible approach gives people a clear problem, access to relevant knowledge, room to test alternatives and a way to learn from attempts that do not work.
Evaluation should include usefulness and consequence, not only applause for novelty. A highly visible idea can displace a quieter improvement that helps more people. Conversely, an unusual proposal may require time before others recognize its value. A small pilot, feedback from affected people and a documented revision cycle can provide better evidence than a one-off creativity contest. These are practical applications of the research distinctions, not a validated universal program.
Creative participation also has an access dimension. People who lack money, equipment, language support or safe time to experiment may have less opportunity to show what they can do. If a task is unfamiliar, a low score can reflect missing preparation rather than low capacity. Fair evaluation asks what support and constraints were present before attributing the whole outcome to a person.
Creativity and AI-assisted work
Generative systems can produce many variations quickly and help a person explore options. That may support a worker who then compares, edits and tests ideas. It may also flood a team with similar plausible suggestions, narrow attention to what the system makes easy or obscure whose work shaped an output. A claim that AI “increases creativity” needs an outcome: more ideas, more original ideas, better judged products, greater inclusion or a stronger capacity to create later.
Pennanen and colleagues’ robot-drawing study is relevant to how observers attribute creativity when they see more of a producer and process. It does not test the long-term impact of using a writing or design assistant. Research on generative AI in UI/UX workflows concerns a specialized practice and is too narrow to anchor a general definition of creativity. Broader definition, assessment and cognition research is needed, while the robot-drawing study addresses the specific attribution question it tested.
A sound AI evaluation could compare final work and the human process behind it. Did the tool help the user discover a worthwhile direction, or did it merely increase the number of initial options? Were affected people consulted? Could the creator explain and revise the result? Did the workflow preserve opportunities to learn? These are proposed evaluation questions. No study cited here establishes that a particular generative model answers them well in every domain.
Why this matters in Northern Europe
Nordic design, education and public services offer settings in which creative work can be understood as collective problem-solving, not only as individual artistic expression. The Aalto-associated robot study shows that Nordic researchers contribute to current debates about how creative AI is judged. It does not establish a regional advantage or a Nordic creativity score. The larger question for a school, municipality or studio is what forms of original and effective contribution the local setting enables.
A public-service team could begin with a problem experienced by residents, include them in judging whether proposed changes are useful and document revisions. A design school could assess both the student’s final artifact and the decisions that improved it. These are plausible applications of the research, not results measured in the cited sources. Local language, inclusion and resource constraints would have to be examined rather than assumed away.
What the evidence does not show
There is no single score that measures all creativity. Originality depends on the comparison group and domain; effectiveness depends on purposes and evaluators. Expert agreement about a product is useful but does not reveal every process or guarantee future achievement. Timed idea-generation tasks assess a slice of creative thinking, not a whole person’s creative capacity. Brain-network findings do not identify a unique creative brain region or justify diagnosing people from an image.
The Four C model organizes scales of contribution; it does not prove a fixed developmental ladder. The OECD assessment captures specified student tasks, not every form of art, invention or civic problem-solving. The robot study measures judgments in a controlled setting, not whether an AI system has human experience or whether all AI tools change people’s creativity. Broad claims about who is creative, or what training reliably makes people creative, require domain-specific and longitudinal evidence.
Creativity is not guaranteed by removing all constraints or by demanding more novelty. Some constraints guide useful work, while others shut it down. The research gives a vocabulary for asking which is which, but a local intervention still needs testing. Nor is a creative idea automatically beneficial: consequences, distribution of costs and the voices of those affected must remain part of evaluation.
Return to the library room
The team tests two arrangements with the teenagers who would use the room. One idea that sounded exciting blocks a quiet reading area; another less dramatic change makes the room usable after school. The team revises its plan. Creativity here is not the loudest suggestion at the first meeting. It is the development of an original response that survives contact with a real need and can improve through feedback.
Defined carefully, creativity brings possibility and judgment together. It can be personal or collective, modest or field-changing, expressive or practical. Its study becomes more useful when the task, comparison group, evaluators and constraints are visible. That precision leaves room to value imagination without turning it into a vague compliment or a metric that hides the work behind an idea.
Research references
- Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. View source
- Amabile, T. M. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 43(5), 997–1013. View source
- 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. View source
- Plucker, J. A., Beghetto, R. A., & Dow, G. T. (2004). Why isn’t creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research. Educational Psychologist, 39(2), 83–96. View source
- Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95. View source
- OECD. (2024). PISA 2022 results (Volume III): Creative minds, creative schools. OECD Publishing. View source
- Pennanen, N., Linkola, S., Kantosalo, A., Hiillos, N., Männistö, T., & Guckelsberger, C. (2025). From product to producer: The impact of perceptual evidence and robot embodiment on the human assessment of AI creativity. ACM Transactions on Human-Robot Interaction, 14(3), Article 41. View source