基于自然语言处理（NLP）技术的药学专业个性化学习平台开发与应用

Development and application of a personalized learning platform for pharmacy disciplines based on NLP technology

摘要

在全球化与科技革命的双重驱动下，药学教育快速发展，而传统教学模式却因循守旧，暴露出诸多不适应现代需求的缺陷。如教学内容针对性不足、学习效率较低等问题日益凸显。针对药学专业课程内容复杂、学科交叉程度高的特点，笔者基于自然语言处理（NLP）技术开发了药学专业个性化学习平台。平台集成智能问答、学习资源推荐、自适应测试和学习行为分析等功能，通过语义分析、文本挖掘和机器学习等技术，为学生提供精准的学习资源推荐和个性化学习体验。本研究旨在提升学生学习效率和效果，同时，为教师优化教学策略提供数据支持，为药学教育的智能化与个性化发展提供新思路。未来，将进一步优化平台功能，拓展其应用领域，以促进药学教育的全方位革新。

Abstract

Driven by globalization and technological revolution, pharmacy education has developed rapidly. However, traditional teaching models have become outdated and exposed numerous defects inadequate for modern demands, such as insufficient targeted teaching content and low learning efficiency. Addressing the characteristics of complex pharmaceutical professional course content and high interdisciplinary integration, the authors have developed a personalized learning platform for pharmacy disciplines based on Natural Language Processing (NLP) technology. The platform integrates intelligent Q&A system, learning resource recommendation, adaptive testing, and learning behavior analysis. By employing techniques such as semantic analysis, text mining, and machine learning, the platform provides accurate learning resource recommendations and personalized learning experiences for students. This research aims to enhance students' learning efficiency and effectiveness, while providing data support for teachers to optimize teaching strategies, and offering new insights for intelligent and personalized development of pharmacy education. Future efforts will focus on further optimizing platform functions and expanding its applications to promote comprehensive reform in pharmacy education.

关键词

自然语言处理（NLP） / 药学教育 / 个性化学习 / 智能问答系统 / 学习资源推荐 / 自适应测试 / 学习行为分析 / 教学策略优化

Key words

Natural Language Processing (NLP) / pharmacy education / personalized learning / intelligent question-answering system / learning resource recommendation / adaptive testing / learning behavior analysis / teaching strategy optimization

参考文献

[1] 李现文, 全金玉, 李春玉. Journal club 在护理专业研究生培养中的应用与实践[J]. 中国卫生事业管理, 2013(1): 64-66.

[2] 徐洋, 崔莹, 郭庆峰, 等. 第二课堂在普通外科研究生教育中的应用探索[J]. 继续医学教育, 2021, 35(12): 84-86.

[3] 张璐, 郑淑美, 苑鸿雯, 等. 中医学专业英语教学融入课程思政教育的思考[J]. 继续医学教育, 2021, 35(12): 82-84.

[4] 郑姣琳. PBL 教学法支撑下的医学研究生教学改革[J]. 高教学刊, 2021, 7(20): 143-146, 151.

[5] 王伟, 李明. 基于多学科交叉的医学研究生培养模式探讨[J]. 中国医学教育杂志, 2022, 42(6): 567-570.

[6] 张红梅, 赵刚. 国内外临床医药学研究生教育模式比较与启示[J]. 医学教育探索, 2020, 39(8): 754-759.

[7] 马跃美, 闫群. 高等医学院校在构建医学终身教育体系中的主要职能[J]. 继续教育, 2008, 22(11): 19-21.

[8] 张晓燕, 王婷. 多学科交叉在医学教育中的应用与实践[J]. 医学教育研究与实践, 2021, 29(3): 45-48.

[9] 李明, 王伟. 跨学科教育在医学研究生培养中的应用研究[J]. 中国高等医学教育, 2019, 38(4): 23-27.

[10] Han, Songlee, Leegayoung. A case study on the development of a media literacy education program for university faculty in the post-COVID-19 era[J]. Culture and Convergence, 2020, 42(12): 583-610.

[11] 刘强， 李娜. 多学科交叉与医学研究生教育改革[J]. 高等医学教育研究, 2019, 31(2): 89-92.

[12] 刘霞， 周斌. 医学研究生创新能力培养的研究进展[J]. 中国医学创新, 2022, 45(7): 113-117.

[13] Wang Q W, Li C H, Liu Y, et al. An adaptive framework embedded with LLM for knowledge graph construction[J]. Ieee Transactions on Multimedla, 2025, 27: 2912-2923.

[14] Gu G H, Xue Y, Wu Z Q, et al. InstructSee: Instruction-aware and feedback-driven multimodal retrieval with dynamic query generation[J]. Sensors, 2025, 25(16): 98-104.

[15] Hoti K, Weidmann A E, Stewart D, et al. Learnings from research on artificial intelligence enabled solutions in clinical pharmacy practice and education[J]. International Journal of Clinical Pharmacy, 2025, 47(4): 917-920.