Doctoral students’ interdisciplinary research proposal development supported by generative AI

A challenge in interdisciplinary learning arises from the disciplinary boundaries brought to the collaboration contexts. Studies have designed technology- and artificial intelligence (AI)-mediated interdisciplinary learning environments to break disciplinary boundaries. However, these studies did not fully address instructional design for students with high-level disciplinary backgrounds. To address this challenge, we highlighted the role of generative AI (GenAI) as a boundary broker. We designed an instructional approach that enabled boundary crossing using GenAI in interdisciplinary research development and investigated its impacts on collaboration. We designed a workshop in which 14 doctoral students from various fields formed interdisciplinary teams to develop research proposals collaboratively. Firstly, they participated in exploratory idea-exchange activities using GenAI to identify potential collaborators. They then received further support from GenAI for developing interdisciplinary research proposals. We analyzed the discourse and AI usage records of two high-performing teams to understand how GenAI was used in interdisciplinary research development, contributed to their idea improvement, and affected their boundary-crossing patterns. The results indicate that GenAI (1) provides adaptive support at both the group and individual levels, (2) supports interdisciplinary discourse by offering potential boundary objects and promotes the emergence of epistemic agency, and (3) transforms boundary-crossing patterns, enabling individuals to leverage their expertise in interdisciplinary collaboration more effectively. We conclude that GenAI contributed to students breaking their disciplinary boundaries by providing a boundary object. Our design and analysis methods can be applied to other GenAI-supported collaborative learning contexts in which learners have different levels of knowledge, skills, and attitudes.