The AI Colleague: Reimagining Interprofessional Collaboration and Education in Medicine

Abstract

Abstract AI is no longer confined to optional decision support; it is becoming a routine presence in clinical workflows, shaping diagnostic hypotheses, triage priorities, risk estimates, and documentation. Yet most educational responses still treat AI as a tool operated by an individual clinician. This framing underestimates how AI reshapes the actual unit of practice: the interprofessional team. We propose the concept of AI-expanded interprofessional collaboration (AI-IPC), in which AI systems function as consequential participants in team cognition—not as moral agents, but as sources of recommendations, uncertainty, and constraints that reorganize communication, authority gradients, and accountability. Building on interprofessional education (IPE) theory, situated learning, and distributed cognition, we argue that “AI literacy” alone is insufficient; learners must be trained to coordinate human-AI-human collaboration in realistic clinical settings. We outline a pragmatic, theory-aligned approach for AI-expanded interprofessional education (AI-IPE): clarifying boundary conditions for AI participation, mapping AI-specific subcompetencies onto established IPE frameworks, and evaluating performance at the level of team behaviors rather than knowledge recall. We present the Interprofessional Education Collaborative (IPEC)–aligned evaluation scaffold with concrete learning activities and assessment approaches, and we outline how the approach can be tailored across undergraduate, postgraduate, and continuing education settings. We further emphasize that implementation requires digital infrastructure, institutional governance, and educator capacity that bridges AI, clinical workflow design, and IPE facilitation. Finally, we address the deliberate use of the “AI colleague” metaphor—not to anthropomorphize AI, but to make the relational and coordinative demands of AI integration visible and teachable. The pedagogical aim is calibrated, critical engagement: teams that verify, question, and, when warranted, override AI contributions rather than defer to them. AI will not replace interprofessional collaboration; it will change what collaboration requires. Education should make that change explicit, rehearsable, and assessable.