Previously submitted to: JMIR Medical Education (no longer under consideration since Jun 16, 2026)
Date Submitted: Mar 25, 2026
Open Peer Review Period: Mar 26, 2026 - May 21, 2026
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Lecturer-in-the-Loop Clinical Dialectic (LLCD): A Framework for AI-Mediated Socratic Simulation in Resource-Limited Settings
ABSTRACT
Traditional clinical simulation requires substantial infrastructure investment, limiting accessibility in resource-constrained settings. AI technologies hold promise for scalable simulation yet concerns about clinical accuracy and faculty displacement remain. We describe the “Lecturer-in-the-Loop” Clinical Dialectic (LLCD), a framework integrating AI-mediated Socratic simulation with faculty oversight and share our initial experience with final-year medical students at an academic hospital in Jamaica. We facilitated a teaching session with seven final-year medical students using Claude Opus (Anthropic). Students engaged with two sequential AI-generated pediatric cases: acute asthma exacerbation, then bronchiolitis. These clinical scenarios evolved dynamically based on student decisions. Through dialogue with the system, students asked questions, consolidated pathophysiology, proposed management plans, and requested clarification and elaboration on recommendations. Notably, students independently applied pharmacological reasoning from the asthma case to determine that bronchodilators were inappropriate for bronchiolitis, an unprompted transfer of mechanistic understanding across cases. Faculty provided continuous oversight: prompting students to articulate their clinical reasoning before committing to answers, reinforcing key learning points, and validating AI-generated content in real-time. When the AI generated equivocal or clinically inaccurate content, faculty insight transformed these moments into teaching opportunities about critical appraisal. The session ran approximately 3 hours with sustained student engagement. LLCD may represent a reproducible, low-cost approach to clinical simulation-based education that preserves the central role of faculty while leveraging AI’s dialogic capabilities. By positioning AI as a dialogic tool requiring expert validation rather than an autonomous teacher, the framework addresses safety concerns while enabling scalable simulation in resource-limited settings where high-fidelity simulation infrastructure remains inaccessible.
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