AI, Creativity, and a Changing World
Talk Session 1: Wednesday, May 21 2:30 - 3:30 PM, ICM Auditorium
Enhancing Future-Oriented Thinking in Engineering Design Through Cognitive Interventions and AI Assistance
Avinash Aruon, Virginia Tech
Tripp Shealy, Virginia Tech
John Gero, University of North Carolina, Charlotte
Creativity in engineering design requires the ability to anticipate future challenges and opportunities, yet designers often focus on immediate constraints rather than long-term resilience. This study explores how structured interventions—future thinking prompts and generative AI assistance—shape creative problem-solving and neurocognitive engagement. Engineering students (n = 90) developed conceptual designs for a development project under three conditions: a control group without future-oriented prompts, a future-thinking group prompted to describe future site conditions, and an AI-assisted group using a generative AI tool to support future scenario envisioning. Functional near-infrared spectroscopy (fNIRS) captured neurocognitive activation patterns in the prefrontal cortex during the design process. Findings indicate that both future thinking prompts and AI assistance enhance creative ideation by encouraging designers to engage with long-term possibilities while reducing cognitive load. The group that used a generative AI tool developed more ideas and used significantly lower cognitive resources in their prefrontal cortex, specifically in the left ventrolateral PFC and left dorsolateral PFC regions. This research highlights how structured cognitive interventions can reshape neural activation patterns and foster more unique, creative, and resilient design solutions.
Agentic Perspective on Human-AI Collaboration for Image Generation and Creative Writing: Insights from Think-Aloud Protocols
Janet Rafner, Aarhus University
Blanka Zana, Aarhus University
Ida Bang Hansen, Aarhus University
Simon Ceh, Graz University
Jacob Sherson, Aarhus University
Mathias Benedek, University of Graz
Izabela Lebuda, University of Wraclaw
As generative AI becomes an increasingly prevalent in creative domains, questions arise regarding its impact on human agency—specifically autonomy, control, efficacy, and ownership—throughout the creative process. This study investigates how individuals negotiate their creative agency when collaborating with AI tools for image generation and creative writing. Using think-aloud protocols and post-task interviews, we analyze the experiences of 13 participants across two co-creative tasks (image generation, 6; writing, 7). Through qualitative coding, we systematically identify patterns in how participants express and regulate agency throughout their interactions with AI. Our findings reveal that agency is dynamic rather than static, fluctuating at different stages of the creative process. Participants experienced high autonomy in idea generation but struggled with control in execution, particularly in image generation tasks. Creative efficacy was enhanced when AI suggestions were used strategically—either by aligning with user intent or serving as counter-inspiration. Ownership perceptions varied, with some participants maintaining a strong sense of authorship, while others felt distanced from their outputs due to the AI’s unpredictable influence. These insights suggest that AI-mediated creativity is not a simple augmentation of human ability but a negotiation of agency that depends on the tools’ interaction styles and affordances. Our study highlights the need for co-creative AI systems that adapt to users’ shifting agency needs, fostering meaningful collaboration rather than passive reliance on automation.
Evaluating AI’s Ideas: The Roles of Individual Creativity and Expertise in Human-AI Co-Creativity
Paul V. DiStefano, Pennsylvania State University, United States
Daniel C. Zeitlen, Pennsylvania State University, United States
Janet Rafner, Aarhus University, Denmark
Pier-Luc de Chantal, University of Quebec, Canada3
Aoran Peng, Pennsylvania State University, United States
Scarlett Miller, Pennsylvania State University, United States
Roger E. Beaty, Pennsylvania State University, United States
As generative artificial intelligence (AI) increasingly integrates into education and work, it is crucial to understand who benefits most from human-AI collaboration. This study examines how domain expertise, creative self-efficacy, and baseline creative ability influence human-AI co-creativity in a real-world engineering design task. We simulated co-creativity using pre-generated ideas from GPT-3.5-turbo, ensuring consistent AI suggestions to assess idea generation and evaluation. Engineering (N = 99) and psychology students (N = 212) first generated an initial solution (Idea 1), evaluated AI-generated solutions, and then revised their response (Idea 2). Linear mixed-effects models revealed that expertise, baseline generation ability, and evaluation ability predicted Idea 2 quality. Engineering students consistently produced more novel and effective solutions, highlighting the role of expertise. However, both groups improved comparably after evaluating ChatGPT’s ideas, supporting the “rising tides lift all boats” hypothesis—AI benefits individuals across expertise equally. Additionally, using a novel categorization scheme comparing Idea 1, the ChatGPT ideas, and Idea 2, we found significant group differences in what inspired participants’ Idea 2. These findings underscore the importance of domain expertise and evaluation skills in human-AI co-creativity. While AI can enhance creative output, human expertise remains essential for grounding AI-generated ideas in practical reality, emphasizing the need to develop domain-specific knowledge and evaluation skills in education, work, and professional settings.
AI-Driven Transformation of Creative Learning Environments in STEAM
Estelle Linjun Wu, University of Cambridge
STEAM, fosters interdisciplinary learning and real-world problem-solving, aligning with the evolving demands of a globally connected world.Thus synergy between STEAM and emerging technologies-e.g. artificial intelligence (AI)—presents new opportunities for cultivating creativity in learners. With AI increasingly reshaping the social and material dimensions of learning environments, this study explores how AI-powered tools, such as ChatGPT, ERNIE Bot, complement traditional educational technologies, influence the construction of creative learning environments and student engagement in interdisciplinary STEAM programs. Using ethnographic methods, data were collected through participant observation, in-depth interviews with 32 students and 6 instructors, and artifact analyses within three Beijing secondary schools renowned for their innovative curricula. Thematic analysis identified five core themes shaping students' creative engagement: attitude, collaboration, climate, conflict, and material resources. Findings suggest that generative AI enhances creative practices by supporting prototyping visualization, ideational fluency, and iterative design. However, students also encounter tensions between human originality and AI-generated contributions, reflecting broader challenges in human-AI co-creation. Notably, the timing and manner of AI adoption, i.e. when, where, and how students integrate AI, influence the social and material interactions, and the overall creative process and learning experience. These insights highlight the need for design AI tools and learning environments,and offers practical recommendations for educators to meaningfully integrate AI into STEAM.
Creative Partner: Comparing human and generative AI as collaborator in creative tasks
Clin KY Lai, Pennsylvania State University
Simone Luchini, Pennsylvania State University
Roger Beaty, Pennsylvania State University
Creativity is often a collaborative process involving both the generation of novel ideas and the evaluation of those ideas to identify the best solutions. While the ability of generative AI to mimic naturalistic language has led to its increasing integration into various creative tasks, studies of human-AI co-creativity have shown mixed results. Few studies to date have directly contrasted human-human and human-AI creative collaboration—a crucial step in understanding creativity in an increasingly AI-present world. Thus, it remains unclear if ideas generated with AI tend to be more or less creative than ideas generated when collaborating with another human. This study addresses this gap by examining the dynamics of human-human and human-AI collaboration across two creative tasks: the Alternative Uses Task (AUT) and a creative short story writing task. Participants were paired to complete two creative tasks designed to assess divergent thinking and collaborative creativity. They were randomly assigned either Role A (responding first) or Role B (responding after A) and took turns generating responses with either a human or an AI partner. The human-AI condition will follow the same format, with the AI serving as the collaborative partner. This explored the effects of human-AI versus human-human collaboration on creative outcomes and how individuals' creative ability and perceptions of their partner (human or AI) influenced collaborative outcomes. Insights from this research will provide a deeper understanding of the potential benefits and limitations of integrating generative AI into creative workflows.
Human versus Large Language Models’ Associative and Meaning-making Processes
Liane Gabora and Linxuan Wang, University of British Columbia
To be helpful co-creators of textual content, large language models (LLMs) should be able to form and use associations in a human-like manner. We asked participants, ChatGPT (a LLM), and Seq2Seq (a LLM precursor), to invent new words along with their corresponding meanings such that each word feels well-suited to its meaning. (As an example, ChatGPT invented ‘sproinkle,’ and its corresponding meaning, ‘To energize or invigorate something in a lively and bouncy manner.’) Despite that all words were actually meaningless, participants were able to guess which word goes with which meaning for both human-generated and ChatGPT-generated word-meaning pairs, but not for Seq2Seq-generated word-meaning pairs. When ChatGPT was asked to explain how it came up with word-meaning pairs, the process it recounted was very similar to how humans generate new word-meaning pairs. In Study Two, to assess whether ChatGPT’s abilities extend across languages, this was repeated for Mandarin words. In Study Three, to assess whether a word-meaning pair feels right because the word shares phonetic elements with other semantically related words in the language, or due to universal properties inherent in the sounds themselves, we compare the ability of bilinguals with non-Mandarin speakers to identify which Chinese words correspond with each meaning. The relatability of ChatGPT’s word-meaning pairs suggests that ChatGPT mimics the aesthetic, associative processes underlying human sound symbolism and word generation. We analyze what features of both human brains and LLMs enable them to outperform Seq2Seq word-meaning pair generation task, and discuss the significance for machine creativity.