Scaffolded AI-Supported Problem-Based Learning for Medical Interns: Exploratory Retrospectively Registered Randomized Controlled Evaluation With a Voluntary Feasibility Follow-Up.

Background: High-quality problem-based learning (PBL) during internship is resource-intensive and difficult to scale without consistent facilitation. Although generative AI is increasingly used in health professions education, many applications remain on-demand answer tools that may not reproduce core PBL processes. Objective: This study aimed to develop and evaluate the Multiagent PBL Environment for Clinical Reasoning (MAPLE-CR), an AI-supported environment that uses generative AI as a process-oriented scaffold rather than an answer-delivery aid. The system supports cognitive mechanisms through reasoning prompts, social-interactional mechanisms through simulated tutor and peer roles, and regulatory mechanisms through structured workflows and feedback loops. We separately evaluated implementation and repeated-use feasibility, including learner experience, short-term outcomes versus self-study, and preliminary framework-aligned process evidence. Methods: We conducted a 2-stage study. In an exploratory randomized evaluation (N=52; intervention: n=26; control: n=26), interns completed parallel pretests and posttests around a standardized case. The intervention group engaged in asynchronous, scaffolded PBL in MAPLE-CR, whereas controls completed case-matched self-study using materials derived from the same case and learning objectives. We summarized transcript-derived process dimensions and postsession learner experience in the intervention group. In a voluntary follow-up, 18 participants completed 1 MAPLE-CR case per week for 4 additional weeks to examine repeated use feasibility and patterns across novel cases. Results: Baseline pretest scores were comparable between groups ( Conclusions: MAPLE-CR was feasible and showed high learner acceptability. Its use was associated with larger short-term CR knowledge gains than case-matched self-study, while transcript analyses provided preliminary framework-aligned process evidence. However, the post hoc sensitivity analysis did not establish prospective power sufficiency, and the confidence interval could not exclude smaller educationally meaningful effects. These findings suggest that AI-supported, process-oriented PBL may provide a scalable formative supplement for CR practice when facilitator capacity and small-group scheduling are constrained, but the study did not isolate specific scaffolding effects. Further research should confirm these findings using larger, prospectively powered trials with stronger active comparators and longer-term outcome measures.