Can Large Language Models Support University Counseling? Evidence from Perceived Counseling Alliance, Disclosure Willingness, and Risk Recognition
TL;DR
It is suggested that LLM-based agents are most effective as front-end support tools that facilitate engagement and emotional expression rather than as autonomous crisis detectors in Chinese university counseling settings.
Abstract
The rapid expansion of large language models (LLMs) has created new opportunities for university mental health services, particularly in contexts where counseling demand exceeds available professional resources. This study examined the application of an LLM-based intelligent agent in Chinese university counseling settings, focusing on three key outcomes: perceived counseling alliance, disclosure willingness, and risk recognition. Using a randomized between-subjects experimental design, 388 valid responses were collected from university students and assigned to either an LLM-based counseling condition or a control condition. The LLM-based agent significantly improved perceived counseling alliance and disclosure willingness, and perceived counseling alliance partially mediated the relationship between condition and disclosure. Scenario-based analyses further showed that the LLM-based agent improved general risk recognition and slightly enhanced sensitivity to high-risk cues, although performance remained more limited for crisis-level disclosures. These findings suggest that LLM-based agents are most effective as front-end support tools that facilitate engagement and emotional expression rather than as autonomous crisis detectors. In the context of Chinese university counseling, the study supports a complementary human–AI model in which intelligent agents lower barriers to help-seeking while trained counselors retain responsibility for risk assessment and intervention. Overall, the results contribute to digital mental health research by clarifying both the relational benefits and safety boundaries of LLM-supported counseling.