通讯作者: 张兰, 1419861417@qq.com

DOI：10.12201/bmr.202504.00037

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

• 摘要：目的/意义 随着生成式人工智能技术的迅猛发展，医学文献检索教学面临新机遇与挑战。本研究旨在探讨人工智能生成内容与医学文献检索课程相结合的优化设计，以提升学生综合信息素养。方法/过程 通过更新教学内容、创新课程设计、引入实践案例，提出以“AI赋能”为核心的课程优化设计。构建“理论讲授-案例实操-反思讨论”三阶递进式混合教学模式；根据医学文献检索需求，将AIGC工具分类并明确教学定位；设计具体临床场景与科研任务，呈现AIGC工具与传统数据库协同实践路径；设计三级教学框架，实现系统性能力培养；构建多维度评价体系，评估学生学习效果。结果/结论 该框架能有效提升学生信息处理能力、批判性思维及伦理意识，为课程数字化转型提供参考，具有较高可靠性和实用性。

  关键词： 医学文献检索；课程优化；生成式人工智能；信息素养教育; ; ; 

   

  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

  提交时间：2025-04-14

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
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• 序号	提交日期	编号	操作
  1	2025-03-13	10.12201/bmr.202504.00037V1	下载

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