Research on the Optimization Design and Practice Path of the AIGC+Medical Literature Retrieval Course

Corresponding author: zhanglan, 1419861417@qq.com

DOI: 10.12201/bmr.202504.00037

•  

  Abstract: Abstract Objective/Significance With the rapid advancement of generative artificial intelligence technology, new opportunities and challenges have been introduced to medical literature retrieval education. This study is conducted to explore the optimization design of combining AIGC with the Medical Literature Retrieval course to improve students comprehensive information literacy. Methods/Process A course optimization framework, centered on AI empowerment, has been proposed through the updating of teaching content, the innovative design of the curriculum, and the integration of practical case studies. A three-stage blended teaching model—comprising theoretical instruction, case-based practice, and reflective discussion—has been constructed.AIGC tools have been categorized, and their pedagogical roles have been clarified based on the specific requirements of medical literature retrieval. Concrete clinical scenarios and research tasks have been designed to illustrate the collaborative application of AIGC tools and traditional databases. A three-tier instructional framework has been developed to systematically cultivate students competencies. Additionally, a multidimensional evaluation system has been established to assess learning outcomes. Results/Conclusion The proposed framework has been shown to effectively enhance students information processing skills, critical thinking abilities, and ethical awareness. It is expected to serve as a valuable reference for the digital transformation of the curriculum, demonstrating high reliability and practical applicability.

  Key words: Medical Literature Retrieval; Course Optimization; Generative Artificial Intelligence; Information Literacy Education

  Submit time: 14 April 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• zhangsuping. Reform and Practice of Integrated Teaching Model Based on PAD Class -- Taking Medical Literature Retrieval Course as an Example. 2021. doi: 10.12201/bmr.202110.00024

  Meng Dongqing, LV Wenjuan. Research on the Path of Constructing Knowledge Services in Medical Libraries Empowered by Generative Artificial Intelligence. 2025. doi: 10.12201/bmr.202510.00039

  Exploration of Medical Video Creation Path Based on Generative Artificial Intelligence. 2025. doi: 10.12201/bmr.202508.00005

  TANG Quan, DOU Jun. An Empirical Study on Embedded Medical Information Literacy Education Based on the IDEA Model. 2024. doi: 10.12201/bmr.202408.00061

  Li Yuhang, Dai hui. Clinical Ethical Governance of Generative Medical Artificial Intelligence (GMAI): A Three-Dimensional Collaborative Pathway and Chinese Practices. 2025. doi: 10.12201/bmr.202511.00057

  Huiting, SHEN Yanni. Research and Design of Intelligent Text Retrieval Technology for Semantic Electronic Medical Records. 2025. doi: 10.12201/bmr.202512.00082

  shisenzhong. Analysis of the Risks and Governance Strategies for the Application of Generative Artificial Intelligence (GAI) in Primary Healthcare. 2024. doi: 10.12201/bmr.202408.00053

  XIANG AININGKUN, TIAN JINGXUE, HU DEHUA, LIU HAIXIA. Comparative Study on Response Efficacy of Generative Artificial Intelligence for Elderly Diabetes Mellitus. 2025. doi: 10.12201/bmr.202503.00019

  panxiaoxuan. Opportunity and Innovation: New Trends of medical Information literacy education. 2021. doi: 10.12201/bmr.202111.00008

  XIE Jing, LIU Jiuchang. Frontier Case Analysis of Artificial Intelligence Powered Medical Education Reform in the United States and Its Inspirations. 2025. doi: 10.12201/bmr.202503.00054

• ID	Submit time	Number	Download
  1	2025-03-13	10.12201/bmr.202504.00037V1	Download

• Public  Anonymous  To author only Submit Comments