ArticleBMC medical education2025
The role of generative AI tools in case-based learning and teaching evaluation of medical biochemistry.
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 9 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-supported case-based learning in medical education: a comprehensive scoping review.Frontiers in medicine · 2026Pooled it
- The effectiveness of GAI-assisted teaching methods in medical education: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates.JMIR medical education · 2026Article
- Performance of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
- Application Status and Intelligent Development Prospects of Case-Based Learning in Standardized Training for Obstetric Residents: A Narrative Review.Advances in medical education and practice · 2026Review
- AI Use for Medical Students: Impact on Clinical Skill Acquisition and Retention. A Systematic Review.Advances in medical education and practice · 2026Review
- Evaluation of a Hybrid Case-Based Learning and Small-Group Teaching Model Using Artificial Intelligence-Assisted Case Generation: An Innovative Dental Education Study.Advances in medical education and practice · 2026Article
- Transforming surgical ward rounds: constructivist pedagogy, structured models, and intelligent technologies.Frontiers in medicine · 2026Review
- Evaluation of DeepSeek-Generated Biochemistry Clinical Cases for Teaching Purpose.Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular BiologyArticle
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
Abstract
backgroundMedical biochemistry, a fundamental course in medical education, has a complex and expanding knowledge base. Traditional teaching methods often fail to meet students' needs for in-depth understanding and personalized learning. Students can become overwhelmed by the vast array of biochemical concepts, reactions, and molecular structures.
objectiveThis study aims to explore the potential of generative AI tools as teaching assistants in medical biochemistry, particularly in CBL (Case-Based Learning) settings where their application is currently limited.
methodsWe conducted a comparative study involving a control group (N = 40) and an experimental group (N = 39) to assess the impact of AI tools on CBL learning. We analyzed students' performance and compared evaluations of their work by both teachers and AI tools. Additionally, a questionnaire was used to gauge the effects of AI tools on case study learning.
resultsThe experimental group using AI tools showed significantly better performance than the control group. The former completed case assignments faster (2.6 h vs. 5.5 h, P < 0.05) and achieved higher exam scores (77.3 ± 4.3 vs. 66.5 ± 5.4, P < 0.05). AI-based grading on students' assignments closely matched teachers' evaluations on them (P > 0.05), demonstrating reliability in assessment. Students rated AI highly for basic knowledge acquisition (Q4M = 9.18) but noted limitations in complex clinical reasoning (Q11M = 4.20) and innovative thinking (Q12M = 3.90). Key concerns of using AI included that AI reduced interaction between teachers and students (Q1M = 7.17) and standardized AI outputs led to homogenized learning (Q6M = 6.56). Despite these drawbacks, students' acceptance of AI increased significantly after the trial (5.5 to 7.6, P < 0.05).
conclusionGenerative AI tools have significantly enhanced learning efficiency and performance in CBL teaching of medical biochemistry, shortened task completion time and improved examination scores. Although there are limitations in the cultivation of innovative thinking and interaction between teachers and students, students' acceptance of AI has increased. Therefore, AI should serve as a supplement to traditional teaching to balance the learning efficiency and creative thinking of students.
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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.