调研学生对AI心理咨询的接受度，发现场景不同态度差异大。
Dr. GPT in Campus Counseling: Understanding Higher Education Students' Opinions on LLM-assisted Mental Health Services
- 通过5种虚构场景访谈10名学生，考察LLM在心理服务中的应用
- 学生认可主动提醒与个性化跟进，担忧数据局限和情感支持不足
- 适合关注AI心理健康服务设计的研究者与高校心理工作者
针对大学生日益突出的心理健康问题，本研究通过与十名不同背景的学生进行试点访谈，探讨了他们对大型语言模型（LLMs）在心理支持中应用的看法。研究围绕五个虚构场景展开：通用信息查询、初步筛查、重塑医患关系、长期照护及随访护理。结果表明，学生对LLM的接受程度因场景而异，既认可其在主动干预和个性化随访方面的潜力，也指出训练数据局限性和情感支持能力不足等关切。这些发现为如何设计和实施能有效增强学生心理福祉的AI技术提供了依据，尤其强调在补充传统方法的同时，保持共情与尊重个体偏好。
原文摘要 · Abstract (English)
In response to the increasing mental health challenges faced by college students, we sought to understand their perspectives on how AI applications, particularly Large Language Models (LLMs), can be leveraged to enhance their mental well-being. Through pilot interviews with ten diverse students, we explored their opinions on the use of LLMs across five fictional scenarios: General Information Inquiry, Initial Screening, Reshaping Patient-Expert Dynamics, Long-term Care, and Follow-up Care. Our findings revealed that students' acceptance of LLMs varied by scenario, with participants highlighting both potential benefits, such as proactive engagement and personalized follow-up care, and concerns, including limitations in training data and emotional support. These insights inform how AI technology should be designed and implemented to effectively support and enhance students' mental well-being, particularly in scenarios where LLMs can complement traditional methods, while maintaining empathy and respecting individual preferences.
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