JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI), particularly large language models (LLMs), is increasingly proposed to deliver Socratic dialogue at scale in medical education. However, the gap between conversational fluency and genuine pedagogical effect remains underexamined.

Objective:

This viewpoint argues that while AI holds promise for scalable Socratic tutoring, two critical problems—the mimicry trap and the Panopticon Paradox—require urgent attention. It also proposes a responsible integration framework and defines limits on AI delegation.

Methods:

This is a conceptual viewpoint and argument synthesis. The author critically reviews current evidence (including systematic reviews, randomized controlled trials, quasi-experimental studies, and proof-of-concept systems), identifies structural failure modes, and proposes a three-pillar framework for integration.

Results:

Current evidence shows AI improves engagement, self-efficacy, and satisfaction but lacks longitudinal or comparative trials demonstrating durable clinical reasoning outcomes. The mimicry trap describes how fluent Socratic-sounding questions may not produce genuine metacognitive gains. The Panopticon Paradox describes how surveillance required for personalization may erode psychological safety, pushing learners toward performative engagement. The paper proposes governance, curriculum, and faculty development as three interdependent pillars, and defines limits: AI should not be trusted for ethical deliberation, emotionally complex communication, or ambiguous clinical judgment.

Conclusions:

AI is best understood as a complementary technology, not a replacement. Responsible integration requires engineering around specific failure modes, empirical testing of the Panopticon Paradox, and clear limits on delegation. The research agenda includes longitudinal trials, equity audits, and governance experiments.