Peer-reviewedLicence not statedMedical Science Educator
Making Generative AI Use Visible in a Foundational Health-Sciences Course: Effects on Learning, Verification and Attribution
- 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
Related
- Terminologia Anatomica Compliance in Generative Artificial Intelligence Outputs: A Structured Narrative Review and Terminology-Specific Verification Checklist.
- Evaluating the effectiveness of the SPICES model in enhancing students' learning experience in social medicine and public health field training at jiroft university of medical sciences, Iran.
- Similar discrimination, different behaviour under synthetic occlusion: confidence and attribution reproducibility of deep learning models for intraoral ultrasonography
- The analogy theorem in Hoare logic for formal verification of knowledge transfer in machine learning
- Ensemble SHAP Aggregation and Attribution Variability in Clinical Machine Learning: A COVID-19 Mortality Study
BibTeX
@article{li2026making,
title = {{Making Generative AI Use Visible in a Foundational Health-Sciences Course: Effects on Learning, Verification and Attribution}},
author = {Xinhui Li},
journal = {Medical Science Educator},
year = {2026},
month = jul,
doi = {10.1007/s40670-026-02848-8},
url = {https://doi.org/10.1007/s40670-026-02848-8},
}
RIS
TY - JOUR
TI - Making Generative AI Use Visible in a Foundational Health-Sciences Course: Effects on Learning, Verification and Attribution
AU - Xinhui Li
PY - 2026
DA - 2026/07/31
JO - Medical Science Educator
DO - 10.1007/s40670-026-02848-8
UR - https://doi.org/10.1007/s40670-026-02848-8
SN - 2156-8650
ER -