SynthesisThe clinical teacher2025
STEM exam performance: Open- versus closed-book methods in the large language model era.
Synthesis in The clinical teacher, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- STEM exam performance: Open- versus closed-book methods in the large language model era.The clinical teacher · 2025Pooled it
- Education Research: Quality of Narrative Feedback Generated by a Large Language Model Compared With Expert Faculty for Case-Based Learning in Neurology Education.Neurology. Education · 2026Article
- Education Research: Creating Online Interactive Case-Based Learning Experiences From Educational Case Reports With Large Language Models: A Feasibility Study.Neurology. Education · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe COVID-19 pandemic accelerated the shift to remote learning, heightening scrutiny of open-book examinations (OBEs) versus closed-book examinations (CBEs) within science, technology, engineering, arts and mathematics (STEM) education. This study evaluates the efficacy of OBEs compared to CBEs on student performance and perceptions within STEM subjects, considering the emerging influence of sophisticated large language models (LLMs) such as GPT-3.
methodsAdhering to PRISMA guidelines, this systematic review analysed peer-reviewed articles published from 2013, focusing on the impact of OBEs and CBEs on university STEM students. Standardised mean differences were assessed using a random effects model, with heterogeneity evaluated by I
resultsAnalysis of eight studies revealed mixed outcomes. Meta-analysis showed that OBEs generally resulted in better scores than CBEs, despite significant heterogeneity (I DISCUSSION: Results suggest that OBEs assess competencies more aligned with current educational paradigms than CBEs. However, the emergence of LLMs poses new challenges to OBE validity by simplifying the generation of comprehensive answers, impacting academic integrity and examination fairness.
conclusionsWhile OBEs are better suited to contemporary educational needs, the influence of LLMs on their effectiveness necessitates further study. Institutions should prudently consider the competencies assessed by OBEs, particularly in light of evolving technological landscapes. Future research should explore the integrity of OBEs in the presence of LLMs to ensure fair and effective student evaluations.
Indexed as
Identifiers
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.