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

NCT07252193 | Completed

• 1 other identifier: UNAM-DIALOGUE-DM2-2025
• Study Type: interventional
• Target: 120
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Type 2 Diabetes Mellitus; Medical Education; Diagnostic Communication; Artificial Intelligence Simulation

Keywords

Generative AI; Simulation-Based Education; Medical Students; Diagnostic Communication Skills; Artificial Intelligence in Healthcare; DIALOGUE-DM2

Outcome Measures

Primary Outcomes (1)

• Change in Diagnostic Communication Performance Score

  Improvement in diagnostic communication skills, measured using validated rubrics - the Kalamazoo Essential Elements Communication Checklist and the Medical Communication Rating Scale (MCRS) - applied to standardized patient scenarios. Independent blinded faculty evaluators and AI scoring will be used. Scores range from 0 to 100, with higher values indicating better diagnostic communication performance.

  Approximately 12 weeks (from pre-test to post-test per participant).

Secondary Outcomes (4)

• Change in Student Self-Reported Confidence in Diagnostic Communication

  Approximately 12 weeks (from pre-test to post-test per participant).

• Change in Domain-Specific Diagnostic Communication Scores (Kalamazoo Framework and Medical Communication Rating Scale)

  Approximately 12 weeks (from pre-test to post-test per participant).

• Agreement Between Human Evaluators and AI Scoring

  Assessed at post-test, approximately 12 weeks after baseline per participant.

• Student Satisfaction With the Assigned Training Method

  Assessed immediately after completion of the post-test, approximately 12 weeks after baseline per participant.

Study Arms (2)

AI-Based Simulation Training (DIALOGUE-DM2)

EXPERIMENTAL

Medical students assigned to this arm will receive training using the DIALOGUE-DM2 platform, which provides generative AI-driven simulated patients. Participants will engage in multiple diagnostic disclosure scenarios focused on type 2 diabetes and receive immediate feedback generated by the AI system. Feedback is aligned with validated communication frameworks (Kalamazoo, MRS). Training is conducted over several sessions prior to the post-test evaluation.

Behavioral: AI-Based Simulation Training (DIALOGUE-DM2)

Traditional Training

ACTIVE COMPARATOR

Medical students assigned to this arm will receive traditional communication skills training. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. Participants will complete the same number of training sessions as the intervention group before the post-test evaluation.

Behavioral: Traditional Training

Interventions

AI-Based Simulation Training (DIALOGUE-DM2) BEHAVIORAL

Medical students interact with the DIALOGUE-DM2 platform, a generative AI-based simulation system. The platform delivers virtual patient encounters focused on type 2 diabetes diagnostic disclosure. Students complete multiple simulated scenarios and receive immediate AI-generated feedback aligned with standardized communication rubrics (Kalamazoo, MRS). Training aims to enhance diagnostic communication skills prior to post-test evaluation.

AI-Based Simulation Training (DIALOGUE-DM2)

Traditional Training BEHAVIORAL

Medical students receive traditional training in diagnostic communication. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. The training duration and number of sessions are matched to the intervention group.

Traditional Training

Eligibility Criteria

• Age: 18 Years - 29 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• Universidad Nacional Autonoma de Mexico: lead

Study Sites (1)

Universidad Nacional Autónoma de México, Faculty of Higher Studies Iztacala (FES Iztacala)

Tlalnepantla, Mexico

Location

Related Publications (1)

• Suarez-Garcia RX, Chavez-Castaneda Q, Orrico-Perez R, Valencia-Marin S, Castaneda-Ramirez AE, Quinones-Lara E, Ramos-Cortes CA, Gaytan-Gomez AM, Cortes-Rodriguez J, Jarquin-Ramirez J, Aguilar-Marchand NG, Valdes-Hernandez G, Campos-Martinez TE, Vilches-Flores A, Leon-Cabrera S, Mendez-Cruz AR, Jay-Jimenez BO, Saldivar-Ceron HI. DIALOGUE: A Generative AI-Based Pre-Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios. Eur J Investig Health Psychol Educ. 2025 Aug 7;15(8):152. doi: 10.3390/ejihpe15080152.

  PMID: 40863274 BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
• Masking Details: Participant, Investigator, Outcomes Assessor
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator, FES Iztacala, UNAM

Model Details: Two-arm randomized, blinded, controlled trial comparing AI-based simulation training with traditional training in medical students facing type 2 diabetes disclosure scenarios.

Study Record Dates

• First Submitted: October 2, 2025
• First Posted: November 26, 2025
• Study Start: September 22, 2025
• Primary Completion: December 18, 2025
• Study Completion: December 20, 2025
• Last Updated: December 29, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, ANALYTIC CODE
• Time Frame: 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.
• Access Criteria: 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).

Locations

ME (1)