利用大型语言模型模拟心理治疗客户互动:客户101的开发和可用性研究
Daniel Cabrera Lozoya1, Mike Conway1, Edoardo Sebastiano De Duro2
1School of Computing and Information Systems, The University of Melbourne, Gratham St, Parkville VIC, Melbourne, 3010, Australia, 61 90355511.
JMIR medical education
|July 31, 2025
概括
大型语言模型 (LLM) 创建现实的心理健康客户端聊天机器人用于培训. 虽然有效,但心理语言差异突出显示了客户101.1.的改进领域.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 临床培训 临床培训
背景情况:
- 大型语言模型 (LLM) 展示了先进的类似人类的文本生成能力.
- LLM 在模拟心理健康培训的客户方面提供了潜在的应用.
- 客户端101是一个新的网络平台,为此目的使用LLM驱动的聊天机器人.
研究的目的:
- 开发和评估一个基于网络的对话心理治疗培训工具.
- 该工具具有LLM驱动的聊天机器人,模拟心理健康状况的客户.
- 评估这些人工智能客户端的现实性和训练实用性.
主要方法:
- 开发了GPT-4驱动的聊天机器人,模拟抑郁症和通用焦虑症.
- 与16名心理健康专业人员进行了29次模拟治疗.
- 使用LIWC进行了会议后调查并分析了心理语言特征,与人类客户数据进行了比较.
主要成果:
- 调查显示,参与者对聊天机器人现实性的满意度很高 (93%对焦虑).
- 在3/8焦虑和4/9抑郁特征中,聊天机器人和人类成绩单之间发现了显著的心理语言差异.
- 关键的区分特征包括否定,家庭,负面情绪,感觉和健康/疾病术语.
结论:
- GPT-4聊天机器人显示出作为有效的心理健康培训工具的希望.
- 参与者的反验证了它们在模拟现实的客户互动中的实用性.
- 已识别的心理语言变异为增强聊天机器人的现实性和培训效率提供了具体的目标.
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