Date
Publisher
arXiv
This paper introduces the "Trilingual Triad" framework, a model that explains how students learn to design with generative artificial intelligence (AI) through the integration of Design, AI, and Domain Knowledge. As generative AI rapidly enters higher education, students often engage with these systems as passive users of generated outputs rather than active creators of AI-enabled knowledge tools. This study investigates how students can transition from using AI as a tool to designing AI as a collaborative teammate. The research examines a graduate course, Creating the Frontier of No-code Smart Cities at the Singapore University of Technology and Design (SUTD), in which students developed domain-specific custom GPT systems without coding. Using a qualitative multi-case study approach, three projects - the Interview Companion GPT, the Urban Observer GPT, and Buddy Buddy - were analyzed across three dimensions: design, AI architecture, and domain expertise. The findings show that effective human-AI collaboration emerges when these three "languages" are orchestrated together: domain knowledge structures the AI's logic, design mediates human-AI interaction, and AI extends learners' cognitive capacity. The Trilingual Triad framework highlights how building AI systems can serve as a constructionist learning process that strengthens AI literacy, metacognition, and learner agency.
Study at a glance
- Question: How can students transition from passive users of AI tools into active creators of AI teammates?
- Setting and data: Singapore University of Technology and Design; graduate students and instructors across three student-built GPT project cases; project documents, interviews, and reflective essays.
- Method: Qualitative multi-case study using within-case and cross-case analysis.
Why it matters
Students frequently interact with generative artificial intelligence as passive consumers rather than active creators. Simply teaching learners to use existing AI tools is insufficient for preparing them for an AI-shaped world.
Key findings
- Effective human-AI collaboration emerged when domain knowledge structured the AI's logic, design mediated interaction, and AI expanded student cognitive capacity.
- Designing custom AI teammates served as a constructionist process that required students to turn implicit domain knowledge into explicit, teachable rules.
- Building and tailoring custom AI systems deepened learners' sense of autonomy, competence, and agency by moving them from tool consumers to technology architects.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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