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.

Inclusion Criteria:

• Medical students currently enrolled in the Faculty of Medicine (Medical Surgeon Program), UNAM-FES Iztacala.
• Age between 18 and 30 years.
• Able to provide informed consent.
• Willing to participate in all study phases (pre-test, intervention, post-test).

Exclusion Criteria:

• Prior participation in the DIALOGUE pilot study.
• Previous formal training in diagnostic communication beyond the standard medical curriculum.
• Incomplete availability for scheduled sessions.
• Refusal or inability to provide informed consent.

De-identified individual participant data (IPD) will be shared, including rubric-based performance scores from pre-test and post-test evaluations, self-reported confidence questionnaires, satisfaction survey responses, and AI versus human evaluator ratings. Demographic data (age, sex, academic year) will also be included in anonymized form. No personally identifiable information will be shared.

IPD and supporting documents (study protocol, SAP, ICF, analytic code) will be made available beginning 6 months after publication of the primary results and for a period of at least 5 years thereafter.

De-identified IPD and supporting documents will be available to qualified researchers upon reasonable request. Requests must include a methodologically sound proposal and will require a data use agreement. Access will be provided through direct communication with the Principal Investigator (Dr. Héctor Iván Saldívar Cerón, UNAM-FES Iztacala).

Participant, Investigator, Outcomes Assessor