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Related Experiment Videos

Conversational Artificial Intelligence in Medical Education: A Scoping Review.

Tasniya Aktar1, Robert J Farquhar1, Chris Jacobs1,2,3

  • 1Faculty of Life Sciences and Medicine, King's College London, London, GBR.

Cureus
|May 22, 2026
PubMed
Summary

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Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context.

JMIR medical education·2025

Conversational artificial intelligence (AI), including chatbots and large language models (LLMs), shows promise for medical education. While engaging and useful for practice, further research is needed to ensure reliability and effective integration.

Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Digital Learning Tools

Background:

  • Conversational artificial intelligence (AI), including chatbots and large language models (LLMs), is increasingly explored in medical education.
  • These AI tools offer interactive learning through simulated patient encounters and adaptive feedback.
  • Existing evidence on the educational impact of conversational AI in medicine is fragmented.

Purpose of the Study:

  • To conduct a scoping review of conversational AI in undergraduate medical education.
  • To examine the literature across three key domains: educational utility, technology usability, and fidelity.
  • To assess the current evidence base and identify gaps in research.

Main Methods:

  • A comprehensive literature search was performed across PubMed, Scopus, and Web of Science in August 2025.
Keywords:
artificial intelligence in medicinechatbotsconversational artificial intelligencemedical education curriculummedical education technology

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  • 496 unique studies were screened, with 20 studies meeting the inclusion criteria for the review.
  • Methodological rigour was evaluated using a validated framework for medical education research.

    • Main Results:

    • Conversational AI demonstrates potential for enhancing engagement, self-directed learning, and practical skill development.
    • Learners generally perceive these AI systems as intuitive and motivating, with reported benefits for clinical reasoning and communication.
    • Limitations include issues with reliability, realism, technical accuracy, inconsistent outcome measures, and modest methodological rigour.

    Conclusions:

    • Conversational AI is a promising adjunct, not a replacement, for traditional medical teaching methods.
    • Effective integration requires careful design, validated evaluation, and human oversight.
    • Future research should standardize assessment, explore beyond user satisfaction, and address fidelity and responsiveness for safe implementation.