Recent research co-authored by Kaufman<\/a>, along with his new edited scholarly volume on generative AI and creativity<\/a>, suggests that while artificial intelligence can support some aspects of creative work, it does not replace human creativity. Instead, it can amplify existing differences in skill, judgment, and expertise \u2014 raising important questions for education, equity, and the future of creative work.<\/p>\n Unlike many technology enthusiasts, Kaufman approaches AI with skepticism grounded in research rather than fear of the technology itself.<\/p>\n \u201cMost creativity researchers tend to fall into two camps, those who are very excited about AI and those who are deeply concerned about it”\u00a0Kaufman says. \u201cI\u2019m in the second camp.\u201d<\/p>\n His concern stems largely from how quickly generative AI systems were released and adopted, often without the safeguards, testing, or regulatory frameworks that typically accompany transformative technologies.<\/p>\n \u201cAI was being actively used by people before we had time to study it carefully,\u201d Kaufman says. \u201cThat\u2019s especially problematic when we\u2019re talking about learning, creativity, and long-term skill development.\u201d<\/p>\n AI was being actively used by people before we had time to study it carefully. That\u2019s especially problematic when we\u2019re talking about learning, creativity, and long-term skill development. — James C. Kaufman<\/cite><\/p>\n<\/blockquote>\n In a recent two-part study conducted with collaborators from other institutions<\/a>, Kaufman and his colleagues examined how people engage in creative tasks both independently and with the assistance of large language models (LLMs). Participants completed storytelling tasks either on their own or with AI support. The researchers then assessed creativity, intelligence, and overall performance across both conditions. The study has not yet been peer-reviewed or accepted for publication.<\/p>\n \u201cWhat we found is that creativity and intelligence still matter,\u201d Kaufman says. \u201cParticipants who were more creative without AI also tended to perform better when collaborating with AI.\u201d<\/p>\n Rather than flattening differences in creative ability, AI acted as an amplifier \u2014 benefiting those who already possessed stronger creative and cognitive skills.<\/p>\n \u201cIf you already have strengths in a domain, you should be able to use AI more effectively,\u201d Kaufman says. \u201cAI doesn\u2019t suddenly make everyone equally creative.\u201d<\/p>\n The reason, he explains, lies in how creativity actually works. Generating ideas is only part of the process. Creativity also requires evaluating ideas, refining them, and deciding which are worth pursuing.<\/p>\n \u201cAI is much better at generating ideas than it is at evaluating them,\u201d Kaufman says. \u201cDeciding what makes sense, what is original, and what is worth pursuing still requires human judgment.\u201d<\/p>\n That evaluative stage relies heavily on experience, intelligence, and metacognition \u2014 an awareness of one\u2019s own strengths, limitations, and goals.<\/p>\n \u201cKnowing what kind of help you actually need from AI is a skill in itself,\u201d Kaufman says, offering a simple example to illustrate the point. \u201cIf you think of AI as producing work at about a B or B-plus level, someone who is already working at an A level can use it selectively and still produce excellent work. But if someone is operating below that level, their ceiling may simply become the AI\u2019s output.\u201d<\/p>\n Nowhere are the implications of these findings more concerning than in education.<\/p>\n “The goal of an assignment isn\u2019t the final product,\u201d Kaufman says. \u201cThe goal is learning how to do the work.\u201d<\/p>\n When students rely heavily on AI to generate essays, stories, or solutions to problems, they may produce acceptable outcomes, but they risk bypassing the cognitive effort required for meaningful learning. Several recent studies suggest that when AI assistance is removed, gains in creativity and learning often disappear.<\/p>\n \u201cThat suggests students aren\u2019t necessarily developing lasting skills,\u201d Kaufman says. \u201cThey\u2019re outsourcing the work.\u201d<\/p>\n “The goal of an assignment isn\u2019t the final product. The goal is learning how to do the work.\u201d — James C. Kaufman<\/cite><\/p>\n<\/blockquote>\n Kaufman also points to evidence from other studies showing that students frequently overestimate how much they collaborate with AI, reporting thoughtful engagement even when usage data show extensive copying and pasting. One of the study\u2019s central findings challenges the popular notion that AI \u201cdemocratizes\u201d creativity.<\/p>\n \u201cCreativity is already one of the most democratic human traits we have,\u201d he says. \u201cAcross gender, culture, and socioeconomic status, there are generally no meaningful differences in creative potential.\u201d<\/p>\n What AI may introduce instead, he warns, are new inequities.<\/p>\n \u201cAs paid versions improve and free versions decline in quality, access becomes increasingly important,\u201d Kaufman says. \u201cThe most powerful tools will be available to those who can afford them.\u201d<\/p>\n The consequences extend beyond classrooms into creative industries themselves. Many entry-level creative jobs, such as caption writing, concept art, and freelance digital illustration, are already being displaced by AI systems, according to Kaufman.<\/p>\n \u201cThese are the kinds of jobs people rely on while they\u2019re trying to establish themselves in creative fields,\u201d Kaufman says. \u201cWhen those disappear, entire pipelines of talent are disrupted.\u201d<\/p>\n He worries this could lead to a polarized creative landscape, with hobbyist creativity on one end and elite, well-funded creative production on the other. These concerns are explored more fully in his new edited volume, \u201cGenerative Artificial Intelligence and Creativity: Precautions, Perspectives, and Possibilities<\/a>,\u201d co-edited with\u00a0Matthew J. Worwood<\/a>, assistant professor-in-residence in UConn\u2019s Department of Digital Media and Design. The book brings together scholars from psychology, education, computer science, philosophy, and related fields to examine AI\u2019s impact on creative thinking, teaching, assessment, and ethics.<\/p>\n Working across disciplines, Worwood says one of the most surprising aspects of the project was the range of perspectives contributors brought to discussions of generative AI and creativity.<\/p>\n \u201cThe variety in views of generative AI surprised me,\u201d Worwood says. \u201cRarely are we sharing perspectives within a single context, and that makes conversations fun and insightful, but also challenging.\u201d<\/p>\n That diversity, Worwood says, reinforces the book\u2019s central argument that AI should be treated as a tool rather than a replacement for human creativity, particularly in educational settings.<\/p>\n \u201cResponsible, intentional use starts with the teacher and the learning expert,\u201d Worwood says. \u201cIt begins with learning objectives and taking time to consider how choices in AI use can either support or hinder students in meeting those objectives.\u201d<\/p>\n Transparency, he adds, is critical, and he cautions against allowing decisions about AI use in education to be driven primarily by technologists.<\/p>\n Responsible, intentional use starts with the teacher and the learning expert. It begins with learning objectives and taking time to consider how choices in AI use can either support or hinder students in meeting those objectives. — Matthew J. Worwood<\/cite><\/p>\n<\/blockquote>\n \u201cAt the lower grade levels, it has to be the teacher, guided by an administrative team that has consulted with subject-matter experts or scholars in the learning sciences,\u201d he says. \u201cRight now, I worry that we\u2019re too often defaulting to advice from technologists who may not fully understand how learning works.\u201d<\/p>\n At more advanced levels of education, Worwood envisions a gradual shift toward student autonomy.<\/p>\n \u201cWe hope to reach a point where students can determine how and when to use AI to support their learning,\u201d he says. \u201cBut during that transition, thoughtful guidance will be crucial.\u201d<\/p>\n Ultimately, Kaufman frames AI as neither inherently good nor inherently bad, but powerful.<\/p>\n \u201cCreativity itself is neutral,\u201d Kaufman says. \u201cIf everyone were more creative, the world would not automatically be a better place. The same is true of intelligence\u00a0and\u00a0AI.\u201d<\/p>\n What matters, he adds, is who controls these tools, how they are used\u00a0and\u00a0whether institutions invest in thoughtful oversight. For educators, policymakers, and creative professionals, the challenge is not whether to engage with AI, but how to do so without sacrificing learning, equity, and human judgement.<\/p>\n \u201cWe\u2019re living in interesting times,\u201d Kaufman says. \u201cAnd we\u2019re still deciding what kind of future we want these tools to help create.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":" James C. Kaufman explores questions regarding AI\u2019s impact on education, equity, and creative work across two new publications, one with a School of Fine Arts colleague<\/p>\n","protected":false},"author":59,"featured_media":239833,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_crdt_document":"","wds_primary_category":0,"wds_primary_series":0,"wds_primary_attribution":0,"footnotes":""},"categories":[2428,2711,1855,2076,1914],"tags":[],"magazine-issues":[],"coauthors":[1879],"class_list":["post-239830","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-educational-psychology","category-emerging-technology","category-neag","category-research","category-sfa"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-30 01:20:31","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/239830","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/users\/59"}],"replies":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/comments?post=239830"}],"version-history":[{"count":14,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/239830\/revisions"}],"predecessor-version":[{"id":239942,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/239830\/revisions\/239942"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media\/239833"}],"wp:attachment":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media?parent=239830"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/categories?post=239830"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/tags?post=239830"},{"taxonomy":"magazine-issue","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/magazine-issues?post=239830"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/coauthors?post=239830"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Implications for Learning and Equity<\/h2>\n
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Shifting Creative Landscapes<\/h2>\n
![\"Matthew](\"https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/01\/WORWOOD_headshot-1500x1000-1-300x200.jpg\")
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