Enhancing Psychotherapeutic Alliance in College: When and How to Integrate Multimodal Large Language Models in Psychotherapy

TL;DR

This study tackles the rising demand for campus mental health support by evaluating how multimodal large language models (MLLMs) can be integrated into psychotherapy in China. Through three studies with therapists and student clients, the authors show that MLLMs are most effective when used as adjuncts under human supervision, particularly for information gathering, triage, and real-time state recognition, while autonomous therapy raises substantial privacy, ethics, and emotional-depth concerns. Key findings highlight that acceptance is shaped by social identity and the perceived relative status of MLLMs, with anthropomorphic avatars and memory features enhancing engagement in daily counseling but raising new design and ethical considerations. The work provides design guidance and an expanded psychosocial lens for extending technology acceptance theory to AI-assisted mental health, informing the development of practical, user-centered campus mental health tools with three-party therapeutic alliances.

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.

Figures (7)

• Figure 1: The flowchart of the study design.
• Figure 2: Descriptive Statistics of the Factors of Interest.
• Figure 3: Statistics about psychotherapists' views of MLLM application scenarios in campus psychotherapy.
• Figure 4: Quantitative data about student clients' attitudes from the survey study.
• Figure 5: Quantitative data about student clients' expected functions and concerns in the interview.