JMIR Publications

ABSTRACT

Background:

Technology-enhanced healthcare 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 healthcare settings. Yet little is known about how health professions students combine their own SRL with co-regulation from human and AI-based supports in such environments.

Objective:

This study adopted a person-centered approach to identify regulatory profiles based on SRL and co-regulation with near-peer teachers (NPTs) and generative artificial intelligence (GenAI), and to examine how these profiles related to interprofessional learning outcomes.

Methods:

Health professions students (N = 136) enrolled in a technology-enhanced IPE completed an SRL questionnaire and the Interprofessional Collaborative Competency Attainment Survey (ICCAS) at the beginning of the programme. They rated co-regulation from NPTs at mid programme and co-regulation from GenAI at the end, together with ICCAS communication and collaboration subscales and learning satisfaction. A two-step cluster analysis, using SRL and co-regulation scores as indicators, was employed to identify distinct regulatory profiles. Profile differences in interprofessional communication, collaboration, and learning satisfaction were examined using independent samples t-tests.

Results:

A two-profile solution provided the best fit to the data. The Positive human-NPT-AI regulation profile (36.5%) was characterized by moderately high SRL, high co-regulation with NPTs, and above-average co-regulation with GenAI. The Negative human-NPT-AI regulation profile (62.5 % of students) showed the opposite pattern by having moderately low SRL, low co-regulation with NPTs, and below average co-regulation with GenAI. Students in the Positive profile reported significantly higher interprofessional communication and collaboration and greater learning satisfaction than those in the Negative profile, with small to moderate effect sizes.

Conclusions:

These findings provide person-centered evidence that self and co-regulation from human and AI agents form distinct regulatory configurations in technology-enhanced IPE and that a richer “regulatory ecology”—combining stronger SRL with greater engagement with near-peer and GenAI supports—is associated with more favourable 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.