From social structures and fire, to the wheel, the internet, and ideas, humans have always created; however, the trait, creativity, as a personality trait, was only invented in the mid 20th century. Dr. Samuel W. Franklin, the author of The Cult of Creativity, unpacks this history in the following episode of Throughline: No Bad Ideas. The desire for creativity emerged in the Cold War era, out of a fear of conformity and computers and a need for innovation (these conditions sound all too relevant in 2026). It wasn’t until relatively recently that creativity had such a large role in society and education. Research in creativity followed suit, accounting for only 0.2% of all psychological research in 1950 (Guilford, 1950).

Now, half a century later, creativity is widely seen as a core competency of education (Organisation for Economic Co-operation and Development [OECD], 2023), for similar reasons as the advent of creativity (i.e., innovation, knowledge creation, emerging challenges). On an individual level, creativity “can support a range of other skills including metacognitive, inter- and intra-personal and problem-solving skills, as well as promoting identity development, academic achievement, social engagement and career success” (OECD, 2023, p. 141). Locally, the Province of British Columbia (2015) labels creative thinking (one of only seven competencies) as a necessary skill for navigating complex futures.

“there should be no doubt that the centrality of humans in the future will depend fundamentally on their adaptive performance related to non-routine tasks, requiring flexibility of mind, capacity to take decisions based on incomplete information, intuition, problem solving ability, artistic and aesthetic sense: in a word, on their creativity”

Corazza, 2016, p. 259

However, there remains a gap between what education values and the realities of educational contexts, including standardization, assessment, overextended teachers, and the rapid advancement and adoption of genAI-powered tools. This gap may threaten human creativity, but it also underscores the growing need for it.

Creativity and GenAI

This standard definition however is static; it implies a judgement of the outcomes, yet originality and effectiveness cannot be absolutely assessed. Originality, in particular, is a relative measure, so what is the baseline in which creative works are measured against and by whom? Corazza (2016) argues creativity is dynamic and necessitates inconclusiveness in the process. Recognizing that creativity varies across contexts (i.e., time and place, domain and culture, assessor). They propose that “creativity requires potential originality and effectiveness” (p. 262).

Kaufman and Beghetto (2009) further refine creativity with the Four C Model of Creativity. They introduce magnitudes of creative acts:

1. Mini-c: “novel and personally meaningful interpretation of experiences, actions, and events” (Beghetto & Kaufman, as cited by Kaufman & Beghetto, 2009, p. 3). This can be personal realizations and explorations.
2. Little-c: everyday innovation; tangible and shareable creative works
3. Pro-c: professional creators, paid for their creative works
4. Big-C: eminent accomplishments, creative works that may last centuries

When we consider the criteria of novelty prevalent in the standard definition of creativity, we may deduce that Big-C creative is novel with respect to the world, Pro-c with respect to domain experts, Little-c with respect to peers, and Mini-c with respect to the self (Beghetto, 2024).

Although many scholars tend to agree on the definition of creativity, I am still not convinced that one’s creativity should be determined by their products relative to others. It will need to wait for another time to fully elaborate my rationale, though it stem around the idea that creativity is a process, not a product. “[T]he difference between what you see and a designer sees” is the entire premise of a podcast: 99% Invisible (where the name implies that just 1% of what goes into a design is visible at the end). Secondly, consider abstract art. Try this quiz, where the success rate is 59%, collapsing the boundaries of Little-C and Big-C: Toddler Art or Modern Art? Can you tell if a painting is the work of a small child or a famous modern artist? Hawley-Dolan and Winner’s (2011) study on the same topic found an approximate 70% success rate.

Are GenAI-powered Tools Creative?

Given our definition of creativity, are genAI-powered tools creative? There is an important distinction here to be made between the trait of creativity and the ability to produce creative works. Creativity as a trait may often lead to the production of creative works, however production of creative works does not imply creativity. It has been shown that genAI-powered tools are capable of producing creative works and demonstrate ‘creativity’, though often measured through testing (e.g., Bellemare-Pepin et al., 2026; Gilhooly, 2024; Hubert et al., 2024; Koivisto & Grassini, 2023; Wang et al., 2025). Two of these common tests measure creativity through the singular dimension of divergent thinking:

1. alternative uses task, where given a common object, one is to brainstorm the most unusual uses of it
2. divergent association task, where one is to generate ten nouns with the greatest variance (try it, it’s fun, but I question its efficacy on repeat attempts)

Such tests impose clear rules and success conditions, and it is easy to imagine that an algorithm built from an immense amount of knowledge to develop mathematical links between words would fare well on such tests, but success doesn’t imply creativity.

Do genAI-powered tools generate potentially original and relevant ideas? The second criterion, of producing relevant solutions, I believe is a simple yes, most of the time. However, the first criterion of originality is much harder to determine. Large language models can often pass as professionals across a range of fields, producing work that is impressive to non-experts but is ultimately generated through statistical pattern prediction. If something is highly probable then it is likely not novel. But it may not be that simple, as combinations of disparate probable responses may result in something new. Research on patents (legally requiring novelty) has shown that new technologies are very rare and instead, 40% of new patents are refinements of existing technologies and 60% are novel combinations of existing technologies (Youn et al., 2015). Given large language models’ breadth of knowledge, it is reasonable to believe that it could create a novel combination of ideas. Among I’m sure, many other instances, this happened in May of 2026 in the field of mathematics where one of OpenAI’s models solved an 80-year old math problem by connecting two normally separate branches of mathematics: discrete geometry and number theory, in an elegant manner (Abrams, 2026). From another perspective, original and relevant ideas could also occur at the hands of a monkey with a typewriter and infinite time. How frequently must a tool (or person) generate creative ideas to be considered creative?

GenAI does not act independently. It is the role of the engineer or prompter that may point genAI in creating novelty. On its own, it’s unlikely that genAI will produce originality, yet it is capable of generating original outputs. Would it be safe to assume then that originality stems from a human? And if so, is that human practicing creativity? Do we attribute genAI’s creative outputs to the human who designed the genAI task?

Standardized Schooling &
High-Stakes Testing

Sir Ken Robinson’s (2006) famous TED talk, Do Schools Kill Creativity? elaborates upon this. He outlines that traditional educational systems stigmatize mistakes, resulting in learners being fearful of being wrong; “if you’re not prepared to be wrong, you’ll never come up with anything original” (05:30).

Catch-22

Creativity is a highly regarded trait yet elusive to cultivate. How could a teacher support the development of 28+ different individuals’ originality and classify those individual expressions as per provincial assessment guidelines? How do teachers overcome most learners’ drive for good grades over experimentation? How can teachers mediate the production capacities of genAI-powered tools and its potential of democratization—its ability to create things that only professionals previously could, yet in practice, raises the bar for all productions to be shiny and polished grammar-perfect, robust, prose? Writing as thinking seemed to be already out of style, replaced with the conclusive five-paragraph statement (Corbin et al, 2025). Where is the room for thinking in progress amid genAI? And if it occurs in large language model chat windows, then whose thought is it really? Given this context, it’s understandable that making mistakes is not welcome. I feel this pressure too, right now as I write.

It appears as a catch-22: creativity is what our educational system stifles, yet the very thing that may offer a solution. Our current system holds grades as the target, which can increase a fear of failure, minimizing experimentation and creativity. Teachers are overworked and unable to provide individualized support, leading to generalized instruction and less originality. GenAI holds promise for personalized support yet (for reasons above) may lead to homogenization of learner performance (Laak et al., 2024). Within this system, creativity is minimally cultivated, and the people in positions of power are themselves shaped by, if not products of, the system. So what if instead, creativity is the method?

I am drawn into the world of design as inspiration. Design emphasizes not only the process but also making that process visible, contributing to both learning and a basis for using tools such as genAI (Al-Sa’di & Miller, 2026). Design, where there is hardly ever a right answer but many options worthy of exploration. Design, which celebrates individuals, originality, and effectiveness. Design that focuses on low-stakes testing, failures, and iteration. I look to leading schools of design thinking, like the Stanford d.school and IDEO U, as amazing methods of developing creativity. Their programs are hands-on, multimodal, and interdisciplinary. They’re ungraded yet filled with healthy feedback, critique, and a tolerance for failure. In design education the process is often the focus of interest, and in doing so, much of the pressure that harms creativity may be alleviated.

So, you may be wondering, as am I: how will this piece end?

I suppose I’m still thinking …

References