<![CDATA[This integrative narrative review synthesizes research published primarily between 2010 and 2026 on artificial intelligence (AI) in higher education and its potential implications for university students' well-being. Student well-being is conceptualized across emotional, psychological, social, academic, and digital dimensions, and evidence is interpreted by distinguishing direct mental-health outcomes from indirect pathways involving academic self-efficacy, access and help-seeking, social connection, agency and digital well-being, and institutional inclusion. The evidence indicates potential benefits, including adaptive learning support, low-threshold psychoeducation, and improved navigation of support, but these benefits are unevenly demonstrated and often rely on short-term, self-report, feasibility, or indirect outcomes. Risks include technostress, clinical ambiguity, privacy breaches, dependency, algorithmic bias, surveillance, and social displacement. Building on, but conceptually distinct from, recent systematic reviews of AI and college student mental health, this review develops an author-generated, context-sensitive pathway framework that makes positive and negative branches, contextual moderators, human oversight, and feedback processes explicit. Future research should prioritize longitudinal and comparative designs, direct well-being outcomes, culturally responsive evaluation, equity, and responsible non-use.]]>
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