Participants

A Role-Assigned AI Framework: Grounding Students' Mental Models in AI-assisted Ideation 

As generative AI becomes embedded in design workflows, a critical question arises: what does it mean to design with AI intentionally? While existing research measures AI's efficiency in ideation and iteration, little examines how AI fits within the human–tool system as a whole and how design education should respond.

This study approaches the design classroom as a joint cognitive activity, drawn from Cognitive Systems Engineering concept, traditionally applied to high-stakes domains such as aviation, healthcare, and nuclear systems. I argue that design education is equally critical: it shapes how designers learn, make decisions, and ultimately influence the world we inhabit. Reframing design education as designing a resilient cognitive system allows educators to analyze the interdependencies between the student (human), the AI (tool), and the learning environment as an integrated system.

This ongoing study adopts a Role-Assigned AI Framework as a design work analysis to examine student decision-making and AI interactions across experience levels. A pedagogical framework, currently in pilot testing, scaffolds generative AI through explicitly assigned roles(Evaluator, Co-designer, or Designer), defining a different relationship between student agency and AI output. Students navigate these roles during the ideation stage using a process book written sequentially as decisions unfold, creating real-time maps of each student's reflective creative trajectory.

This study addresses a central risk in AI-integrated education: without guided scaffolding, students may passively accept AI outputs, gradually surrendering creative decision-making without recognizing it. Rooted in Schön's notion of the reflective practitioner, the framework gives instructors and students a shared language for examining agency, authorship, and creative ownership — values central to design education yet increasingly under pressure from AI's speed and opacity.

I look forward to sharing pilot findings, exchanging insights from educators, and collaborating with researchers navigating these shared challenges.

I am currently developing a paper that reflects on teaching creative coding within undergraduate design education in a context increasingly shaped by AI. My broader research focuses on human expression, especially creative and artistic expression, in relation to technology, automation, and AI. Within this context, I am reflecting on a fourth-year undergraduate design course in which I teach creative coding to students with no prior coding background and, in many cases, no initial desire to study coding as a core part of their education. 

The case raises a question that feels increasingly urgent in creative higher education: is teaching coding from scratch still the right pedagogical approach, or should AI be introduced from the very beginning as part of the learning process? I am particularly interested in how this shift may affect self-expression, agency, technical understanding, experimentation, and the role of foundational skills in creative practice.

My current stance is exploratory. I am interested in understanding this problem through my own teaching reflections, as well as through students' perspectives and participations. Especially when students actively shape directions in my syllabus, topics, and modalities of intervention and learning. I would like to contribute to the workshop by discussing the benefits, risks, and trade-offs involved in teaching creative coding in an AI-saturated context. This may be particularly relevant for design students whose primary motivation is expression rather than programming itself.

By joining the workshop, I hope to refine the framing of this emerging research, connect it to wider discussions on AI in creative education, and exchange perspectives with the community, facing similar pedagogical questions. I would welcome the opportunity to share early reflections from my current work and contribute to the broader discussion around AI integration, boundaries, and literacies in creative and design education.

I research artist communities and their use of creative tooling. Regarding art education specifically, I have a paper forthcoming in DIS 2026 on how secondary school art teachers are navigating technology (including, but not limited to AI) in their art classrooms. My team interviewed art teachers across the United States and report on their sociotechnical contexts which translate to limitations in digital literacy, access to technology, and agency over how AI and other devices are used in the classroom. We also surface teachers' main priorities in art pedagogy and classroom design, as well as how emerging technologies (mis)align with their intentions. I am positioned, then, to bring perspectives on art teachers' concerns and needs (or lack thereof) around AI in American secondary education. I am also a freelance illustrator and active within both online and in-person creative communities; my creative practice also provides a lens on how creative communities approach or react to AI. Through this workshop I'd be excited to examine AI and technology in areas of design education outside of my own expertise (visual art in secondary school) such as in primary education, higher ed, or other art and design disciplines. I would also like to gain a broader understanding of the HCI and design community perspective on AI use, containment, and collaboration with educators and institutions. Finally, I'd like to meet potential collaborators since this workshop is very relevant to my current research.

Design research at its intersection with critical, discursive, and speculative practices has long been a field for questioning established paradigms and imagining otherwise. Yet in times of AI hype, the field risks becoming a site where dominant futures, stereotypical visions, and problematic values are reproduced and reinforced. This is not merely a disciplinary challenge — it is a pedagogical one. If young designers increasingly delegate ideation to tools that are powerful yet structurally biased toward the predictable, what remains of design's capacity to open up alternative possibilities? Over the past years, I have been working on this challenge in Germany at Bauhaus-Universität Weimar and University of Design Schwäbisch Gmünd, developing and testing teaching approaches that encourage critical exploration. Drawing on Speculative and Critical Design, my approach uses generative AI to create creative friction: deliberately provoking unexpected outputs to defamiliarize familiar objects, expose embedded assumptions, and foster critical reflection. This concept — which I call artificial irritation — treats AI's unpredictability as well as its technical limitations (hallucinations, glitches) as both a critical and generative design strategy: a way to make visible the values and narratives that AI systems reproduce, and to open these up for questioning and renegotiation. I bring together research and teaching practice, reflecting on approaches that intertwine AI literacy, hands-on exploration, conceptual engagement, and critical reflection. I also bring a set of open questions to discuss at the workshop: How do we build AI literacy that equips design students not just to use these tools, but to interrogate them? How do we preserve — and sharpen — design's capacity for criticality and imagination when working with AI systems? I look forward to mapping these challenges and contributing to a shared research agenda at C&C.

As AI reshapes social and urban systems, my work increasingly focuses on participatory methods for responsible AI, pluralistic alignment, organizational data literacy and capacity building. I would like to present case studies from my practice on AI and co-design and share critical reframings I talk about in classes on symptoms of AI systems and narratives.

In my previous research with blind and low vision artists using text-to-image AI, I found that their positions toward these tools were never simply about the technology's affordances. Whether they engaged, resisted, or adapted depended on their medium, their specific level and kind of visual disability, and years of negotiating what sighted audiences would recognize or dismiss. These were situated positions, shaped by exactly the values this workshop foregrounds: iterative, embodied skill development; process-oriented practice; and the right to determine when a work is complete on your own terms.

That study was collaborative, voluntary, and non-evaluative. I assisted and to an extent co-created the artifacts with them by entering their prompts into Midjourney and helping describe the outputs of generated images. What I am eager to discuss through the workshop is how the extent of collaboration, with AI as a tool and with sighted or non-disabled collaborators, can shape the relationship with the end artifact, the image as a creative object. What kinds of creative powers or limitations are ascribed to an object created in this process, and what does that say about assessing and judging creative work?

In my upcoming thesis study I want to examine what happens when a blind student uses text-to-image AI in the art classroom as a mediating artifact in conversations with a sighted art teacher. When the student brings an AI-generated image to a teacher, what happens to the creative intent that produced it? When a blind student brings an AI-generated image into a classroom, they are using outputs meant for a sighted person, built by a model trained largely on a sighted person's understanding, to describe their ideas to a sighted teacher who is previously used to their verbal descriptions alone. What text-to-image AI offers a blind student is therefore not entirely equivalent to searching Pinterest or Google for visual references. Through the prompt, they may access something closer to an independent ideation space, the kind of space that sketching has historically provided for sighted creators. When that externalized idea is filtered through a system trained on sighted norms, who owns the interpretation? And when evaluation happens, whose creative knowledge is actually being assessed?

This is the question I want to think through with other educators in this workshop: how do we build frameworks and literacy approaches that can account for students whose creative knowledge the tools were not designed to recognize?