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

December 26, 2025 updated by: Héctor Iván Saldívar Cerón, Universidad Nacional Autonoma de Mexico

Generative AI Simulation for Diagnostic Communication in Type 2 Diabetes: A Randomized Controlled Trial (DIALOGUE-DM2)

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Interventional

Enrollment (Actual)

120

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

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

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

Yes

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Triple

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-Based Simulation Training (DIALOGUE-DM2)
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)
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.
Active Comparator: Traditional Training
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
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Diagnostic Communication Performance Score
Time Frame: Approximately 12 weeks (from pre-test to post-test per participant).
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Student Self-Reported Confidence in Diagnostic Communication
Time Frame: Approximately 12 weeks (from pre-test to post-test per participant).
Change in students' self-reported confidence when disclosing a diagnosis of type 2 diabetes, measured through a structured questionnaire using a 5-point Likert scale (1 = very low confidence, 5 = very high confidence). Higher scores indicate greater self-perceived 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)
Time Frame: Approximately 12 weeks (from pre-test to post-test per participant).
Improvement in specific communication domains - information delivery, empathy, risk explanation, and shared decision-making - evaluated using the Kalamazoo Essential Elements Communication Checklist and the Medical Communication Rating Scale (MCRS). Each domain is scored from 0 to 100, with higher scores indicating better performance.
Approximately 12 weeks (from pre-test to post-test per participant).
Agreement Between Human Evaluators and AI Scoring
Time Frame: Assessed at post-test, approximately 12 weeks after baseline per participant.
Level of concordance between blinded human evaluators and AI-based scoring of diagnostic communication performance, assessed using Cohen's kappa coefficient (κ). Scores range from -1.0 to +1.0, where values closer to +1.0 indicate stronger agreement between evaluators.
Assessed at post-test, approximately 12 weeks after baseline per participant.
Student Satisfaction With the Assigned Training Method
Time Frame: Assessed immediately after completion of the post-test, approximately 12 weeks after baseline per participant.
Satisfaction with the assigned training method (AI-based simulation vs. traditional training), measured using a structured 5-point Likert satisfaction survey (1 = very dissatisfied; 5 = very satisfied). Higher scores indicate greater satisfaction with the training method.
Assessed immediately after completion of the post-test, approximately 12 weeks after baseline per participant.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Universidad Nacional Autonoma de Mexico

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 22, 2025

Primary Completion (Actual)

December 18, 2025

Study Completion (Actual)

December 20, 2025

Study Registration Dates

First Submitted

October 2, 2025

First Submitted That Met QC Criteria

November 18, 2025

First Posted (Estimated)

November 26, 2025

Study Record Updates

Last Update Posted (Actual)

December 29, 2025

Last Update Submitted That Met QC Criteria

December 26, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • UNAM-DIALOGUE-DM2-2025

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

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 Sharing 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.

IPD Sharing 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).

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ICF
  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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