When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders

TL;DR

This study explores how multimodal large language models can support college psychotherapy, emphasizing their role as auxiliary tools, and highlights user expectations, ethical concerns, and social factors affecting acceptance.

Contribution

It provides empirical insights into integrating MLLMs into campus mental health services, emphasizing user-centered design and ethical considerations.

Findings

Users prefer MLLMs for triage and emotion recognition

Concerns about privacy and capabilities hinder independent MLLM therapy

Social identity influences acceptance of MLLMs in therapy

Abstract

As mental health issues rise among college students, there is an increasing interest and demand in leveraging Multimodal Language Models (MLLM) to enhance mental support services, yet integrating them into psychotherapy remains theoretical or non-user-centered. This study investigated the opportunities and challenges of using MLLMs within the campus psychotherapy alliance in China. Through three studies involving both therapists and student clients, we argue that the ideal role for MLLMs at this stage is as an auxiliary tool to human therapists. Users widely expect features such as triage matching and real-time emotion recognition. At the same time, for independent therapy by MLLM, concerns about capabilities and privacy ethics remain prominent, despite high demands for personalized avatars and non-verbal communication. Our findings further indicate that users' sense of social identity and perceived relative status of MLLMs significantly influence their acceptance. This study provides insights for future intelligent campus mental healthcare.