COMPARING AI ROLE-PLAY AND PEER ROLE-PLAY FOR INFORMED CONSENT TRAINING IN ENDOSCOPY: A RANDOMIZED CONTROL TRIAL

BACKGROUND AND RATIONALE Generative artificial intelligence (AI), particularly large language models (LLMs), has recently enabled the development of interactive, customizable virtual patient simulations that support experiential learning in healthcare communication. These AI-driven platforms can simulate realistic patient encounters, incorporating variable demographics, emotional states, and clinical complexity. They also offer scalable access to individualized feedback and repeated practice, circumventing the logistical limitations of traditional peer or actor-based simulations. Preliminary studies suggest that trainees perceive these tools as high in realism and educational value; however, there remains a lack of empirical evidence directly comparing the educational efficacy of generative AI-based simulations to established pedagogical approaches. This is particularly relevant for teaching complex communication tasks such as obtaining informed consent for gastrointestinal endoscopy-an interaction that demands clarity, empathy, and a detailed explanation of procedural risks, benefits, and alternatives.

STUDY AIM The primary objective of this study is to compare the effectiveness of generative AI-based role play and traditional peer role play in training healthcare trainees to obtain informed consent for endoscopic procedures. Specifically, the study will assess differences in trainee's ability to obtain informed, communication skills in obtaining informed consent and self-reported confidence (self-efficacy) following simulation-based training using a randomized controlled trial (RCT) design.

METHODS Study Design This is a single-blinded, parallel-group randomized control trial. A total of 40 medical trainees will be recruited. Participants will be randomized in a 1:1 ratio to either the intervention group (generative AI-based role-play) or the control group (traditional peer role-play). Randomization will be conducted using a computer-generated sequence, with group assignments placed in sealed envelopes distributed by an independent study team member. Due to the nature of the interventions, participant blinding is not feasible; however, outcome assessors will be blinded to group allocation.

Participants Participants will include medical trainees enrolled in a University of Toronto medical training program. Exclusion criteria include prior experience obtaining informed consent for endoscopy 25 or more times in either clinical or simulated settings.

Baseline Assessment All participants will complete a baseline questionnaire collecting demographic data, self-efficacy in obtaining informed consent, and attitudes toward AI in healthcare education.