When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders
On the map: Multimodal LLMs / vision-language models → Multimodal emotion understanding
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal emotion understanding, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 13 of its 110 references that are in the map. First posted on arXiv 1 Feb 2025.
Builds on
13 of 110 references.
References · 13
- 2022Emergent Abilities of Large Language Modelscited by 2,483
- 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
- 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
- 2023Turning large language models into cognitive modelscited by 66
- 2024A Computational Framework for Behavioral Assessment of LLM Therapistscited by 61
- 2024Large Language Models in Mental Health Care: a Scoping Reviewcited by 34
- 2018Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressionscited by 21
- 2024LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devicescited by 21
- 2024Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMscited by 19
- 2024PATIENT-Psi: Using Large Language Models to Simulate Patients for Training Mental Health Professionalscited by 19
- 2024Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Modelscited by 3
- 2024Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Modelscited by 1
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- 2025Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning
- 2023Rethinking Large Language Models in Mental Health Applications
- 2025Multimodal Large Language Models Meet Multimodal Emotion Recognition and Reasoning: A Survey
- 2025MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance
- 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support
- 2024GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
- 2026Mind Companion: An Embodied Conversational Agent for Process-Based Psychotherapy
- 2025Independent Clinical Evaluation of General-Purpose LLM Responses to Signals of Suicide Risk