Generative AI Empowering Personalized Intervention Models for College Student Mental Health Education

Abstract

The increasing prevalence of mental health issues among college students has become a pressing concern for higher education institutions worldwide. Traditional mental health education models, characterized by one-size-fits-all curricula and reactive crisis intervention, often fail to address the diverse and dynamic needs of individual students. The emergence of Generative Artificial Intelligence (GAI) offers unprecedented opportunities for transforming mental health education from a standardized approach to a personalized, proactive, and scalable system. This study addresses the gap between the potential of GAI and its application in college mental health education by conceptualizing a personalized intervention model. Drawing on a synthesis of existing literature on mental health education, personalized learning, and AI-driven intervention, the study proposes a four-component model comprising: (1) multidimensional student profiling for personalized needs assessment; (2) GAI-powered personalized content generation; (3) adaptive intervention pathways with tiered support levels; and (4) continuous feedback loops for model optimization. The study further analyzes three core mechanisms through which GAI enables personalization: dynamic learner modeling, contextualized intervention design, and empathetic conversational interaction. The proposed model shifts the paradigm from reactive crisis management to proactive psychological nurturing, from group-based instruction to individualized support, and from human-only delivery to human-AI collaboration. Theoretically, this study contributes a systematic framework for understanding GAI applications in mental health education. Practically, it offers actionable guidance for educators, counselors, and policymakers seeking to leverage GAI for improving college student mental health outcomes. Future research should empirically validate the model through longitudinal interventions and examine its effectiveness across diverse student populations.