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  1. Home
  3. Comparing Conventional And Generative Ai-assisted Task Performance In Physiology Education.
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  3. Comparing Conventional And Generative Ai-assisted Task Performance In Physiology Education.

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Comparing conventional and generative AI-assisted task performance in physiology education.

Kagemichi Nagao1, Masanari Umemura2, Yu Iida1

  • 1Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, Japan.

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|May 5, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Generative artificial intelligence (AI) did not significantly change medical students' physiology report scores compared to traditional methods. Effective AI use in education hinges on strong foundational knowledge, not AI reliance alone.

Keywords:
generative artificial intelligencemedical educationphysiology educationtask performance

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Area of Science:

  • Medical Education
  • Educational Technology
  • Physiology

Background:

  • Generative artificial intelligence (AI) presents new opportunities and challenges in medical education.
  • Understanding the impact of AI on student performance is crucial for effective curriculum integration.

Purpose of the Study:

  • To evaluate the educational impact of generative AI on medical students' performance in a physiology laboratory course.
  • To compare assignment scores from AI-assisted report writing with conventional methods.
  • To identify factors associated with effective generative AI use in this context.

Main Methods:

  • Medical students completed a physiology assignment using both conventional resources and generative AI tools.
  • Reports were scored using a keyword-based system to assess core conceptual elements.
  • Student perceptions and prior AI experience were gathered via questionnaires.

    • Main Results:

    • No significant difference was found between scores obtained using conventional methods versus generative AI (p = 0.54).
    • Scores from both methods showed a moderate positive correlation (r = 0.465).
    • Prior performance with conventional resources was the strongest predictor of AI-assisted outcomes.

    Conclusions:

    • Generative AI does not fundamentally alter task performance in this educational setting; it reflects existing learner understanding.
    • Effective integration of generative AI in physiology education requires prioritizing foundational knowledge.
    • Emphasis should remain on core concepts rather than solely relying on AI tools.