Background: Artificial intelligence (AI) is rapidly reshaping evidence-based medicine, yet preclinical medical curricula rarely equip students with the skills to integrate AI meaningfully into the patient–clinician encounter. Gastroenterology (GI), with its heavy reliance on imaging, endoscopic interpretation, and longitudinal patient data, offers an ideal early clinical context in which to introduce AI-augmented decision-making while preserving the therapeutic relationship at the center of care.

Methods: We designed a multi-modal preclinical session anchored in Kolb's experiential learning cycle—Experiencing, Reflecting, Thinking, and Doing—and deliberately mapped to seven learner profiles (visual, auditory, kinesthetic, reading/writing, interpersonal, intrapersonal, and logical). Pedagogical strategies include multimedia concept maps of AI clinical pipelines, faculty-led lectures on evidence appraisal, GI-focused case simulations, hands-on hackathon-style workshops with AI decision-support tools, analysis of contemporary research papers, structured debates on ethical and regulatory implications, and reflective journaling on the physician's evolving role in algorithm-assisted care.

Results: The session enables preclinical students to (1) articulate the mechanisms and evidentiary basis of AI tools relevant to GI practice, (2) critically appraise AI-generated recommendations against traditional evidence hierarchies, (3) apply AI outputs within a simulated patient encounter while preserving communication, empathy, and shared decision-making, and (4) reflect on the ethical, regulatory, and relational dimensions of integrating algorithmic tools into everyday clinical reasoning.

Conclusions: By coupling AI literacy with humanistic patient-encounter training at the preclinical stage, this session offers a scalable, learning-style-inclusive model for preparing the next generation of clinicians. The framework is transferable across specialties and provides a replicable template for embedding AI competency into evidence-based medicine curricula.