#185: Self-organizing Interdisciplinary Team in Generative AI-Supported Collaboration Context

We report two-year design-based research (2024: N = 14, 2025: N = 19) on interdisciplinary research practice by doctoral students supported by generative AI (GenAI). The purpose was to explore necessary supports for interdisciplinary team formation by investigating how learners self-organized teams in this community. In both years, participants formed five teams to prepare interdisciplinary research proposals. Their video recordings data were analyzed. Findings revealed that AI-enabled exploratory collaboration experiences functioned as a collaboration history to build students’ networks for the final team decision. We further identified three boundary-crossing patterns (e.g., Wide-Range Explorers). Through these diverse interactions, the community-level interdisciplinary distance converged toward a balance between novelty and mutual understandability. A more nuanced understanding of interdisciplinary team formation process in GenAI-supported setting, as well as the development of a visualization system displaying interdisciplinary distance, will benefit interdisciplinary team formation process.