Effectiveness of Student-Centered Fine-Tuning of Large Language Models for Mental Health Support

Abstract

We developed a large language model designed to explore student mental well-being support in conversational settings, aimed at providing accessible, empathetic, and accurate responses to students facing challenges such anxiety, stress, loneliness, and academic pressure. Many students face barriers to seeking traditional counseling, such as stigma, scheduling constraints or discomfort with face-to-face interactions. The system addresses these challenges by offering a potential accessible conversational support channel through natural, conversational interactions with a large language model. Our approach focused on two strategies. Firstly, we enhanced the model's communication style to reflect counseling best practices such as empathy, active listening, and emotional validation. The second strategy is to enhance the model's understanding of mental health scenarios using realworld text sources and instructions. The model was trained on diverse, anonymized datasets from real counseling transcripts, emotional support dialogue corpora, and peer-support forums. We integrated prompt engineering, fine-tuning, and an iterative self-reflection loop to identify potentially unsupported or hallucination-prone responses, with the goal to improve factuality and safety in generated responses. We find that fine-tuning on student-centered data consistently outperforms both baseline and mixed-data approaches, emphasizing the importance of domainspecific adaptation. The model shows potential for confidential support, suggesting possible use as an early stage aid for coping strategies, and connects students to campus resources, reducing barriers to help seeking and supporting academic performance.