Evaluating a Large Language Model’s Ability to Synthesize a Health Science Master’s Thesis: Case Study
Our demonstration highlights the ease with which an LLM can synthesize research data, conduct scientific analyses, and produce credible research papers required for graduation from a master's program. A clear and well-written master's thesis, citing subject matter authorities and true to the expectations for academic writing, can no longer be regarded as solid proof of either extensive study or subject matter mastery. To uphold the integrity of academic standards and the value of university diplomas, we recommend that master's programs prioritize oral examinations and school exams. This shift is now crucial to ensure a fair and rigorous assessment of higher-order learning and abilities at the master's level.
Authors
- Pål Joranger (ORCID: https://orcid.org/0000-0001-6274-8039)
- Sara Rivenes Lafontan (ORCID: https://orcid.org/0000-0001-7382-3022)
- Asgeir Brevik (ORCID: https://orcid.org/0009-0009-8892-4673)
Institutions
- OsloMet – Oslo Metropolitan University (NO)
- Universidad Metropolitana (PR)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-05-15
- DOI
- https://doi.org/10.2196/73248
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65