Is generative artificial intelligence (GenAI) a game changer for interdisciplinary collaborative learning in higher education?

The advancement of generative artificial intelligence (GenAI) has presented unprecedented challenges in determining what and how undergraduate students should be taught, yet empirical research in this area remains limited. This study involved 176 university students and employed a 2 × 2 between-subjects factorial design with four intervention groups: same-discipline (SDCL) with GenAI, cross-discipline (CDCL) with GenAI, same-discipline (SDCL) without GenAI, and cross-discipline (CDCL) without GenAI. The results indicate that (a) CDCL performed significantly better than SDCL in design thinking and confidence. (b) There was no significant difference in individual creativity between students with and without GenAI; however, students without GenAI support demonstrated higher group creativity—particularly in relevance, elaboration, and originality—than those with GenAI support. (c) Students with GenAI adopted a deep learning approach, while students without GenAI adopted a surface learning approach. (d) With GenAI support, SDCL students experienced higher flow than those in the CDCL group. These findings suggest that while GenAI may not be a “game changer” for interdisciplinary learning, it does have tremendous potential to improve interdisciplinary learning in higher education.