Co-Designing with Algorithms: Unpacking the Complex Role of GenAI in Interactive System Design Education

2024-10-17

AI Summary

This study investigates the pedagogical impact of generative AI (GenAI) when spontaneously adopted by graduate students in human–computer interaction (HCI) courses for interactive device design. Drawing on 12 post-class focus groups, behavioral coding, and pattern induction, we develop the first taxonomy of GenAI-mediated co-design behaviors—identifying four prototypical usage patterns. Results indicate that GenAI substantially enhances prototyping efficiency and ideational divergence but risks promoting superficial learning during problem framing and reflective synthesis. Crucially, *how* GenAI is used—not the task type—determines learning outcomes. Based on these findings, we propose “creative co-design” as a novel pedagogical paradigm for HCI education and provide empirically grounded, actionable instructional interventions to align curricula with GenAI-era design practice and competency development.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative Artificial Intelligence (GenAI) is transforming Human-Computer Interaction (HCI) education and technology design, yet its impact remains poorly understood. This study explores how graduate students in an applied HCI course used GenAI tools during interactive device design. Despite no encouragement, all groups integrated GenAI into their workflows. Through 12 post-class group interviews, we identified how GenAI co-design behaviors present both benefits, such as enhanced creativity and faster design iterations, and risks, including shallow learning and reflection. Benefits were most evident during the execution phases, while the discovery and reflection phases showed limited gains. A taxonomy of usage patterns revealed that students' outcomes depended more on how they used GenAI than the specific tasks performed. These findings highlight the need for HCI education to adapt to GenAI's role and offer recommendations for curricula to better prepare future designers for effective creative co-design.

Problem

• Exploring GenAI's role in HCI education and design
• Assessing benefits and risks of GenAI in design workflows
• Developing strategies for GenAI integration in HCI curricula

Innovation

• GenAI integration in HCI design workflows
• Taxonomy of GenAI usage patterns
• GenAI enhances creativity and iteration speed