aceA generative AI teaching assistant for personalized learning in medical education
Medical education faces a scalability crisis, where rising class sizes strain individualized instruction, while students increasingly adopt unvalidated Generative AI (GenAI) tools for individualized learning support. This study investigated how medical students integrate constrained GenAI systems into their self-directed learning practices using Retrieval-Augmented Generation (RAG), which limits large language model responses to instructor-curated materials, thereby reducing hallucinations while maintaining peda…
Medical students used a RAG-based teaching assistant for self-directed learning, seeking clarification on foundational concepts with continuous availability and reduced hallucinations from instructor-curated materials.
The same RAG constraints that ensured accuracy limited broader inquiries, creating tension between reliability and comprehensiveness that shaped study routines.
Knowledge-base constraints that ensured accuracy also limited broader inquiries, creating a reliability versus comprehensiveness trade-off.
Evidence
- Peer-reviewednpj Digital Medicine2025-11-04
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Truvace Impact Record TRV-2026-0459, v1: “A generative AI teaching assistant for personalized learning in medical education.” Truvace, 2026-07-20. /record/TRV-2026-0459 (accessed at citation time). sha256 d2dc3f63ad6a20d6…
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