ArticleBMC medical education2025
Generating learning guides for medical education with LLMs and statistical analysis of test results.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: a controlled study.BMC medical education · 2025Trial
- From traditional classroom to AI-enhanced flipped classroom: a three-year pedagogical evolution for international students in pharmacology.BMC medical education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe Progress Test Medizin (PTM) is a formative test for medical students issued twice a year by the Charité-Universitätsmedizin Berlin. The PTM provides a numerical feedback based on a global view of the strengths and weaknesses of students. This feedback can benefit from more fine-grained information, pinpointing the topics where students need to improve, as well as advice on what they should learn in light of their results. The scale of the PTM, taken by more than 10,000 participants every academic semester, makes it necessary to automate this task.
methodsWe have developed a seven-step approach based on large language models and statistical analysis to fulfil the purpose of this study. Firstly, a large language model (ChatGPT 4.0) identified keywords in the form of MeSH terms from all 200 questions of one PTM run. These keywords were checked against the list of medical terms included in the Medical Subject Headings (MeSH) thesaurus published by the National Library of Medicine (NLM). Meanwhile, answer patterns of PTM questions were also analysed to find empirical relationships between questions. With this information, we obtained series of questions related to specific MeSH terms and used them to develop a framework that allowed us to assess the performance of PTM participants and compose personalized feedback structured around a curated list of medical topics.
resultsWe used data from a past PTM to simulate the generation of personalized feedback for 1,401 test participants, thereby producing specific information about their knowledge regarding a number of topics ranging from 34 to 243. Substantial knowledge gaps were found in 14.67% to 21.76% of rated learning topics, depending on the benchmarking set considered.
conclusionWe 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.