Workshop: Creativity in an AI-Enabled World

Aim of the Workshop

Generative AI is fundamentally changing the nature of creativity, presenting an unprecedented opportunity to turbocharge innovation while raising the urgent imperative to ensure that these technologies benefit rather than harm humankind. For the future workforce, a core competency and one of the top 10 essential skills is creative problem-solving (Lee & So, 2025; World Economic Forum, 2020). Creative work is evolving into an interactive human–AI collaborative process (Medeiros et al., 2025), resulting in two major shifts. First, at the individual level, idea generation is now abundant. Human effort shifts upstream to problem finding and downstream to evaluation and judgment (Krakowski, 2025; Verganti et al., 2020). Second, at the group level, AI creates a diversity trade-off. Individuals seem more creative with AI, but collective diversity declines. That is, group outputs become much more similar to one another (Doshi & Hauser, 2024). Because of these shifts, key debates remain open: whether AI enhances or undermines individuals’ creativity, and whether AI serves as an equalizer or a gap-widener across groups (Dell’Acqua et al., 2026; Noy & Zhang, 2023). These tensions raise fundamental questions: How can we measure and reward true novelty when human work is deeply blended with AI outputs? Should we update our definition of creativity to account for “artificial creativity” (Runco, 2025)? We must refine how we understand and model creativity to identify the new forms of human creativity required to keep pace with AI.

AI alters not only where human creative value is generated, but also how individuals, teams, and AI interact with each other. On the one hand, AI amplifies creative innovation by dramatically expanding hypothesis search spaces, facilitating rapid access to cross-disciplinary knowledge, and automating the translation and recombination of concepts into integrated discovery workflows (Wang et al., 2023). On the other hand, AI systems remain fundamentally constrained: they lack contextual understanding, generate superficial or spurious cross-disciplinary connections, introduce severe verification challenges, and are prone to “illusions of understanding”, particularly along a jagged technological frontier (Dell’Acqua et al., 2026; Messeri & Crockett, 2024). It is therefore necessary to examine how interdisciplinary collaboration can counterbalance these deficits by uniting complementary domain expertise, critical sensemaking, and ethical judgment to ensure that cross-field discoveries are rigorous, robust, and socially beneficial (Fügener et al., 2026; Verganti et al., 2020).

Under a new division of labor between humans and AI, education should cultivate the ability to identify enduring principles amid rapid change and to look beyond surface-level technological novelty to the deeper foundations of human creativity. Major educational policy frameworks from the United Nations Educational, Scientific and Cultural Organization (UNESCO, 2023) and the Organisation for Economic Co-operation and Development (OECD, 2019, 2022) advocate human-centered AI integration, emphasizing learner agency, critical reflection, creative problem-solving, and responsible technology use. Emerging pedagogical practices have begun to address both upstream and downstream dimensions. Upstream, curricula must train students to identify neglected societal problems, frame inquiries independently, and question algorithmic defaults (Lee & So, 2025; Wadinambiarachchi et al., 2024). Downstream, learners must cultivate the metacognitive agency needed to preserve independent thought, resist cognitive offloading, rigorously verify and evaluate alternatives across disciplines, and exercise ethical judgment regarding which solutions warrant real-world action (Darvishi et al., 2024; Lee & So, 2025). Yet, it remains unclear how to systematically translate these principles into educational designs through Human-Centered AI that empower learners to truly think outside the box and achieve meaningful real-world impact.

Expected Workshop outcome

Following the workshop and structured post-workshop virtual interactions, participants will collaborate to develop a high-impact journal paper. This publication will synthesize the group’s collective thinking, summarize background literature, and propose conceptual and methodological insights on creativity in the AI era.

Guidelines to prospective participants

Submission for the Workshop

To foster in-depth discussion and high-quality exchange, participation will be primarily by invitation. Interested participants who are not formally invited but wish to attend the workshop are most welcome to submit a Workshop Participation Statement (1–2 pages would suffice) that includes: (1) name, position, and affiliation; (2) a brief summary of relevant academic or professional experience in HCI, AI, or related fields; (3) selected publications or professional contributions (optional); and (4) a short statement of motivation and expected contribution to the workshop.

All confirmed workshop participants will be invited to complete a pre-workshop questionnaire addressing key issues related to the workshop themes. Responses will be requested and will be analyzed and synthesized in advance to inform the workshop presentations and discussions.

Prospective authors should submit their statement in PDF format through the HCII Conference Management System (CMS).

Workshop organizers

Jia Zhou
Google Scholar Homepage
Jia Zhou is a Professor at Chongqing University. Her research interests include Human-Centered AI, Spatial Cognition and Interaction, Smart Technology for Older Adults, Human-Building Interaction. She is the co-chair of the International Conference on Human Aspects of IT for the Aged Population (ITAP).

Gavriel Salvendy
http://www.iems.ucf.edu/salvendy/
Gavriel Salvendy is a member of the National Academy of Engineering and a distinguished professor at the University of Central Florida. He is the founding editor of the International Journal of Human Computer Interaction and the founder of the HCII conferences.

References

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• Dell’Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838
• Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290
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