Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Higher-order multiple primary cancers and persistent cross-site risk across human papillomavirus-related anatomical sites in the United States: a population-based Surveillance, Epidemiology, and End Results study.

Preventive medicine reports·2026
Same author

AMI-SSS01 portable phonocardiographic examination with AI-assisted assessment for detecting heart failure exacerbations in home-based medical care in Japanese primary care clinics: a study protocol for a randomised controlled feasibility trial.

BMJ open·2026
Same author

Testing and evaluating updated variants of the rurality index for Japan: a nationwide methodological study.

BMJ open·2026
Same author

A Decentralized, Academically Integrated Training Model for Rural General Practice in Japan: A Descriptive Program Evaluation.

Journal of general internal medicine·2026
Same author

Association Between Duration of Usual Source of Care and Patient-reported Primary Care Quality in Japan: A Nationwide Cross-sectional Study.

Journal of primary care & community health·2026
Same author

Practice-Based Research Network Activities and Future Nationwide Plans in Japan.

Journal of general and family medicine·2026

Related Experiment Video

Updated: May 7, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

1.2K

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.

Advances in Physiology Education
|May 5, 2026
PubMed
Summary

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

More Related Videos

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

10.5K
Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

16.1K

Related Experiment Videos

Last Updated: May 7, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

1.2K
Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

10.5K
Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

16.1K

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.