DIALOGUE: A Generative AI-Based Pre–Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios

• Ricardo Xopan Suárez-García ^[1] ; Quetzal Chavez-Castañeda ^[1] ; Rodrigo Orrico-Pérez ^[1] ; Sebastián Valencia-Marin ^[1] ; Ari Evelyn Castañeda-Ramírez ^[1] ; Efrén Quiñones-Lara ^[1] ; Claudio Adrián Ramos-Cortés ^[1] ; Areli Marlene Gaytán-Gómez ^[1] ; Jonathan Cortés-Rodríguez ; Jazel Jarquín-Ramírez ^[1] ; Nallely Guadalupe Aguilar-Marchand ^[1] ; Graciela Valdés-Hernández ^[1] ; Tomás Eduardo Campos-Martínez ^[1] ; Alonso Vilches-Flores ^[1] ; Sonia Leon-Cabrera ^[1] ; Adolfo René Méndez-Cruz ^[1] ; Brenda Ofelia Jay-Jímenez ^[1] ; Héctor Iván Saldívar-Cerón ^[1]
  1. [1] Universidad Nacional Autónoma de México

     Universidad Nacional Autónoma de México

     México

     
• Localización: EJIHPE: European Journal of Investigation in Health, Psychology and Education, ISSN 2174-8144, ISSN-e 2254-9625, Vol. 15, Nº. 8, 2025, págs. 152-152
• Idioma: inglés
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• Resumen

  • DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre–post study, we evaluated whether DIALOGUE could improve students’ ability to disclose a type 2 diabetes mellitus (T2DM) diagnosis with clarity, structure, and empathy. Thirty clinical-phase students completed two pre-test virtual encounters with an AI-simulated patient (ChatGPT, GPT-4o), scored by blinded raters using an eight-domain rubric. Participants then engaged in ten asynchronous GenAI scenarios with automated natural-language feedback. Seven days later, they completed two post-test consultations with human standardized patients, again evaluated with the same rubric. Mean total performance increased by 36.7 points (95% CI: 31.4–42.1; p < 0.001), and the proportion of high-performing students rose from 0% to 70%. Gains were significant across all domains, most notably in opening the encounter, closure, and diabetes specific explanation. Multiple regression showed that lower baseline empathy (β = −0.41, p = 0.005) and higher digital self-efficacy (β = 0.35, p = 0.016) independently predicted greater improvement; gender had only a marginal effect. Cluster analysis revealed three learner profiles, with the highest-gain group characterized by low empathy and high digital self-efficacy. Inter-rater reliability was excellent (ICC ≈ 0.90). These findings provide empirical evidence that GenAI-mediated training can meaningfully enhance diagnostic communication and may serve as a scalable, individualized adjunct to conventional medical education.