Generating learning guides for medical education with LLMs and statistical analysis of test results
We designed and tested a method to generate student feedback covering up to 243 medical topics defined by MeSH terms. The feedback generated with data from students in later stages of their studies was more detailed, as they tend to face more questions matching their knowledge level.
Authors
- Miriam Sieg (ORCID: https://orcid.org/0000-0001-9724-3129)
- Mihaela Todorova Tomova (ORCID: https://orcid.org/0000-0003-1554-7239)
- Maren März (ORCID: https://orcid.org/0000-0002-2661-5076)
- Iván Roselló Atanet (ORCID: https://orcid.org/0000-0003-0981-656X)
- Patrick Mäder (ORCID: https://orcid.org/0000-0001-6871-2707)
- Victoria Sehy (ORCID: https://orcid.org/0000-0003-1179-7249)
Institutions
- Technische Universität Ilmenau (DE)
- Humboldt-Universität zu Berlin (DE)
- Freie Universität Berlin (DE)
- Friedrich Schiller University Jena (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- BMC Medical Education
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1186/s12909-025-06978-2
- Citations
- 4
- Primary Topic
- Innovations in Medical Education
- Type
- article
- Field-Weighted Citation Impact
- 7.51
Funders
- Bundesministerium für Bildung und Forschung
- Charité – Universitätsmedizin Berlin