AI for Campus Mental Health: From Implementation to Symbiotic Ecosystem

Abstract

Due to the combined influence of multiple factors such as academic pressure and employment anxiety, mental health issues among college students have become increasingly prominent, and the demand for mental health services has risen accordingly. However, traditional mental health service models in higher education institutions—which primarily rely on in-person appointments, psychological screenings, and passive human intervention—are constrained by practical challenges such as time and location limitations, uneven resource allocation, and slow response times. As a result, these models are struggling to meet the personalized and immediate mental health needs of today’s college students. Leveraging its unique strengths in data analysis and mental state monitoring, AI technology offers new insights for the transformation and upgrading of university mental health services. Currently, AI technology in this field is at a critical juncture, transitioning from exploration to practical application; however, core challenges such as insufficient implementation and an incomplete ecosystem are gradually emerging during the rollout process. This paper focuses on the precision of technology implementation and the synergy of ecosystem collaboration. It provides an in-depth analysis of the current challenges facing AI-enabled mental health services in higher education and proposes targeted solutions. The aim is to offer practical references and feasible strategies for mental health services, thereby facilitating the deep integration of AI technology with university mental health services and fostering ecosystem synergy.