AI-Based Mental Health Applications for College Students: A Literature Review of Qualitative Findings of User Needs

Abstract

College students worldwide face a rising burden of mental health challenges. AI-based mental health applications (including chatbots, adaptive self-help modules, and predictive mood trackers) are increasingly used to offer scalable support. However, successful deployment depends on aligning these technologies with student needs.This systematic literature review synthesizes qualitative evidence (2020–2025) regarding the needs, preferences, and concerns of college students using AI-based mental health applications. Five primary themes emerged: (1) personalization and adaptability; (2) privacy and confidentiality; (3) accessibility and usability; (4) emotional support and human-like interaction; and (5) integration with campus ecosystems. Students emphasized transparent data practices, co-design approaches, culturally sensitive content, and pathways connecting digital tools with campus counseling services. Gaps included limited longitudinal evidence, underrepresentation of low- and middle-income contexts, and little attention to intersectional differences. Designing AI-based mental health applications for college students requires centering student voices through participatory design, prioritizing privacy and explainability, and integrating digital supports with institutional services. Future research should include longitudinal, cross-cultural, and intersectional studies to assess sustained engagement and clinical impact.

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