Dr. GPT in Campus Counseling: Understanding Higher Education Students' Opinions on LLM-assisted Mental Health Services

TL;DR

This study explores college students' opinions on using Large Language Models in mental health services, revealing varied acceptance and highlighting potential benefits and concerns to guide AI implementation in higher education counseling.

Contribution

It provides initial insights into student perspectives on LLM-assisted mental health support, emphasizing scenario-specific acceptance and considerations for empathetic AI design.

Findings

Students see benefits like proactive engagement and personalized follow-up.

Concerns include data limitations and lack of emotional support.

Acceptance varies across different counseling scenarios.

Abstract

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.