Exploring GenAI‑enabled Interprofessional Education: Development and Initial Evaluation

Interprofessional education must increasingly prepare health professions students to collaborate while using generative artificial intelligence (GenAI) critically and responsibly. However, limited evidence explains how interprofessional student teams collectively evaluate and incorporate GenAI-generated information. This study described a GenAI-enabled cancer care module and explored teams’ experiences of using GenAI during collaborative care planning. A qualitative descriptive study was conducted within a cancer interprofessional education simulation module co-designed by two partner universities—one in Hong Kong and one in Mainland China—and delivered at the Hong Kong university. The module involved 316 students from six health-related disciplines, organised into 30 interprofessional teams. Teams first developed a cancer care plan without GenAI and subsequently used a GenAI tool of their choice to review and refine it. The activity explicitly required critical appraisal, contextual adaptation and collective approval of AI-supported content. All 30 teams consented to research use of their joint reflections. Twenty-seven records contained substantive reflective accounts, while three were marked “NIL.” The reflections were examined using reflexive thematic analysis. The findings indicated a form of critically engaged partnership between teams and GenAI. Students perceived GenAI as useful for widening the scope of care planning, identifying previously overlooked concerns and organising contributions from different professions. These perceived benefits depended on active team involvement, including formulating and refining prompts, checking outputs against clinical guidelines and disciplinary knowledge, and negotiating whether suggestions were appropriate for the care plan. Acceptance of GenAI was conditional, as teams identified risks related to accuracy, insufficient localisation, limited clinical detail and an inability to address emotional and relational aspects of care. Responsibility for final decisions remained with human professionals. In this module, students perceived GenAI as most useful when its outputs were treated as provisional contributions to collective deliberation rather than authoritative recommendations. GenAI-enabled interprofessional education should therefore incorporate unaided planning, shared scrutiny, verification and explicit professional accountability. As the findings were derived from brief team reflections from a single delivery site, they represent students’ negotiated perceptions and do not demonstrate improvements in reasoning, collaboration or care-plan quality.