Profiling Human-AI Regulatory Support in Technology-Enhanced Interprofessional Education Among Health Professions Students: Person-Centered Exploratory Study.

BACKGROUND: Technology-enhanced health care interprofessional education (IPE) places high demands on students' self-regulated learning (SRL) and their ability to work productively with others to prepare them for collaborative practice in health care settings. Yet, little is known about how health professions students perceive and combine their own SRL with coregulation from human and AI-based support in such environments. OBJECTIVE: This exploratory and observational study adopted a person-centered approach to identify regulatory profiles based on self-reported SRL and perceived coregulation with near-peer teachers (NPTs) and generative AI (GenAI), and to examine how these profiles were associated with interprofessional learning outcomes. METHODS: Health professions students (N=136) enrolled in a technology-enhanced IPE completed an SRL questionnaire at the beginning of the program. They rated coregulation from NPTs at midprogram and coregulation from GenAI at the end, together with communication, collaboration, and learning satisfaction. A 2-stage exploratory clustering approach using SRL and coregulation scores as indicators was used to identify distinct regulatory profiles. Profile differences in interprofessional communication, collaboration, and learning satisfaction were examined using independent samples 2-tailed t tests. RESULTS: A 2-profile solution provided the best fit to the data. The positive human-NPT-GenAI regulation profile (51/136, 37.5%) was characterized by moderately high SRL, high coregulation with NPTs, and above-average coregulation with GenAI. The negative human-NPT-GenAI regulation profile (85/136, 62.5%) showed the opposite pattern, with moderately low SRL, low coregulation with NPTs, and below-average coregulation with GenAI. Students in the positive profile reported significantly higher interprofessional communication (P=.005) and collaboration (P=.006) and greater learning satisfaction (P<.001) than those in the negative profile, with medium to medium-large effect sizes. CONCLUSIONS: These findings provide person-centered evidence that self-regulation and coregulation from human and AI agents form distinct perceived regulatory configurations in technology-enhanced IPE and that a richer "regulatory ecology," combining stronger SRL with greater perceived coregulation from near-peer and GenAI support, was associated with more favorable interprofessional outcomes. The study highlights the importance of deliberately designing near-peer teaching and GenAI-supported activities as complementary regulatory scaffolds in health professions education.