The Emerging Roles of AI in Self-Directed Stress-Management: a Systematic Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Stress is widespread and carries substantial mental health, social, and economic burdens. Yet access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)–enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. </sec> <sec> <title>OBJECTIVE</title> This systematic review synthesises empirical evidence on how AI-enabled technologies are used for self-directed stress management. We map the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. </sec> <sec> <title>METHODS</title> We conducted a PRISMA-compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed/MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). </sec> <sec> <title>RESULTS</title> Out of 3,008 records identified, 35 studies met the inclusion criteria. AI-supported stress management operates through five core functions, including psychological intervention, behavioural support, psychoeducation, emotional companionship, and stress monitoring and triage, collectively enabling users to identify stress, regulate responses, and engage in self-directed coping outside formal clinical care. </sec> <sec> <title>CONCLUSIONS</title> AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks. </sec>

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