Date
Publisher
arXiv
This study examines how students integrate generative artificial intelligence (AI) into design projects through the lens of the SuperSkillsStack framework, which identifies four key human competencies for effective human-AI collaboration: Agency, Domain Knowledge, Imagination, and Taste. As generative AI increasingly transforms creative practice, design education must consider how human capabilities are cultivated alongside technological tools. Using qualitative thematic analysis, this study analyzes reflective writings from 80 student design teams participating in a human-centered design course. The findings show that students primarily used AI during the early stages of the design process, including brainstorming, information synthesis, and problem framing. However, students consistently relied on human judgment to interpret contextual information, validate AI-generated outputs, and refine design solutions. Domain knowledge derived from field observations enabled students to detect inaccuracies in AI suggestions, while taste played a critical role in evaluating and selecting meaningful ideas. The results suggest that generative AI functions primarily as a cognitive accelerator rather than a replacement for human creativity. The study highlights the importance of cultivating higher-order human capabilities to support effective human-AI collaboration in design education.
Study at a glance
- Question: How do design students use and evaluate generative AI tools during collaborative projects?
- Setting and data: Human-centered design thinking and innovation course, reflective writings from 80 student design teams across diverse real-world contexts.
- Method: Qualitative thematic analysis with framework-based coding and frequency counts
Why it matters
Generative AI can generate design suggestions quickly, but empirical research examining how students actually integrate AI into design workflows and balance automated outputs with human contextual knowledge and evaluative judgement remains limited.
Key findings
- Students primarily used AI during early design phases like brainstorming and information synthesis, treating AI-generated ideas as starting points rather than final solutions.
- Taste appeared in every team reflection, with students relying on human evaluative judgement to filter, rewrite, and refine AI outputs for feasibility and contextual relevance.
- Domain knowledge derived from fieldwork enabled students to detect and correct convincing but inaccurate AI-generated insights that failed to match real-world physical and social settings.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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