开发和评估人工智能聊天机器人工具,用于自我照顾的书面沟通培训:药房学生和教师的经验
S Bakhaya1, E C Lehnbom1, M A de Carvalho Filho2
1Department of Pharmacy, University of Uppsala, Box 580, 751 23 Uppsala, Sweden.
Currents in pharmacy teaching & learning
|October 8, 2025
概括
大型语言模型 (LLM) 聊天机器人有效地培训药房学生进行书面沟通,以便进行自我护理咨询. 这种人工智能驱动的方法提供了现实的患者模拟和结构化的反,增强了现代药房实践的数字沟通技能.
科学领域:
- 药房 教育 教育 药房 教育
- 数字健康传播数字健康传播
- 医疗保健中的人工智能
背景情况:
- 在药房中,有效的沟通至关重要,特别是在自我护理咨询中.
- 在线药房的兴起需要培训数字书面沟通技巧.
- 大型语言模型 (LLM) 为开发对话代理提供了潜力,以支持技能获取.
研究的目的:
- 探索基于LLM的聊天机器人的实用性,以培训药学学生进行书面同步沟通,以进行自我护理咨询.
- 评估人工智能驱动的模拟患者互动的感知真实性和反质量.
- 识别学生和教师在药房教育背景下使用LLM聊天机器人的经验.
主要方法:
- 开发了三个聊天机器人模拟患者和基于LLM的反系统,用于自我护理场景.
- 十四名药学学生和教师与开发的聊天机器人的互动.
- 半结构面试和专题分析,以评估用户体验和反.
主要成果:
- 参与者发现模拟的患者互动是真实的,并注意到情感现实主义.
- 人工智能产生的反被认为是结构化的,详细的,公平的,并专注于沟通技巧.
- 教师们重视AI反的一致性和补充性;学生们注意到认知/情感需求和定制潜力.
结论:
- 基于LLM的聊天机器人是一种在教育上健全和可扩展的方法,用于提高药学学生在自我护理中的书面沟通技能.
- 这种人工智能工具可以成为虚拟患者基础设施的基础,并将通信理论融入数字药房教育中.
- 这种方法有望在制药程序中广泛采用,为在线和混合护理模式做准备.
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