Research Progress of Large Language Models in Case Generation,Personalized Teaching and Assessment

doi:10.3870/j.issn.1672-8009.2026.01.014

Abstract

Abstract: Large language model(LLM)offers new opportunities for medical education.This paper examines its technical foundations and applications in case generation,personalized teaching,and intelligent assessment.By selecting appropriate models and prompts,and incorporating multimodal integration and academic validation,LLM can generate simulated teaching cases for clinical reasoning training.By combining student profiles and learning behaviors,LLM enables path recommendations and content customization,enhancing teaching adaptability.LLM also helps build multidimensional scoring systems focusing on medical accuracy,logical structure,and causal reasoning,providing personalized feedback.Despite challenges like hallucinations and outdated knowledge,future improvements should focus on human-machine collaboration,data security,and standardized resources to promote intelligent and standardized medical education.

Key words: large language model, case generation, personalized teaching, teaching assessment, medical education

CLC Number:

CONG Shang, BAI Wohan, CHEN Zhong, SU Yu, YI Yuexiong. Research Progress of Large Language Models in Case Generation,Personalized Teaching and Assessment[J]. Journal of Medical Molecular Biology, 2026, 23(1): 97-106.

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