Generative AI-Based Simulation for Diagnostic Communication in Type 2 Diabetes (DIALOGUE-DM2)

Overview

This randomized controlled trial evaluates the effectiveness of a generative artificial intelligence (AI)-based simulation program in improving diagnostic communication skills among medical students. The study is conducted at the Faculty of Higher Studies Iztacala, National Autonomous University of Mexico (UNAM). A total of 120 medical students are randomized to either an intervention group using the DIALOGUE-DM2 AI simulation platform or a control group following traditional educational methods. Participants complete a pre-test, receive training according to group assignment, and then undergo a post-test evaluation. The primary outcome is improvement in diagnostic communication skills, measured by standardized patient scenarios and validated rubrics. Secondary outcomes include self-reported confidence, communication domains, and inter-rater agreement between faculty evaluators and AI scoring. This trial aims to provide high-quality evidence on the potential of generative AI to enhance communication training in medical education, specifically in the context of type 2 diabetes diagnosis.

This study builds on a prior pilot trial (published in 2024) that demonstrated the feasibility of using generative artificial intelligence (AI) to train medical students in diagnostic communication. The current trial extends that work with a randomized, blinded, controlled design and a larger sample size. Design: The study is a randomized, blinded, parallel-group, controlled trial conducted at the Faculty of Higher Studies Iztacala (FES Iztacala), UNAM. A total of 120 medical students are enrolled and randomized (1:1) into either the intervention group (AI-based simulation training) or the control group (traditional training with standardized patients and faculty feedback). Intervention: * Intervention group: Students interact with the DIALOGUE-DM2 platform, which provides generative AI-driven simulated patients. They complete multiple diagnostic disclosure scenarios and receive immediate feedback on performance, based on standardized communication rubrics. * Control group: Students receive standard training, including lectures and supervised practice with peer role-play and faculty-guided feedback. Assessments: * Pre-test: All students complete one standardized patient scenario with faculty and AI evaluation prior to intervention. * Training phase: Participants complete their assigned training (AI vs. standard). * Post-test: Students complete a standardized diagnostic disclosure scenario. Independent faculty evaluators (blinded to group assignment) and the AI platform score performance. Outcomes: * Primary outcome: Change in diagnostic communication performance score from pre-test to post-test, measured by validated rubrics (Kalamazoo framework, MRS). * Secondary outcomes: * Student self-assessment of communication confidence. * Domain-specific improvements (information delivery, empathy, risk explanation, shared decision-making). * Agreement between human evaluators and AI scoring. Ethics and Oversight: The study has been reviewed and approved by the Research Ethics Committee of FES Iztacala, UNAM (Approval Number CE/FESI/042025/1915). Risks are minimal, as the intervention is educational and non-invasive. Significance: This is the first randomized controlled trial in Mexico to evaluate a generative AI-based simulation for diagnostic communication. Results will inform the integration of AI-driven training tools into medical education curricula and could contribute to scalable innovations in the training of healthcare professionals for chronic disease management, starting with type 2 diabetes.