{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:51:39Z","timestamp":1791525099568,"version":"4.3.4"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"\n Background<\/jats:title>\n University students experience substantial mental-health burdens, while access to timely support remains uneven. AI conversational agents may provide low-threshold support, but the evidence base has expanded rapidly and prior reviews have often combined different age groups, intervention types, outcome domains, and comparator conditions.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate the effects of AI-mediated conversational interventions on depressive and anxiety symptoms in university students, while distinguishing outcome domains, comparator intensity, intervention architecture, and the evidential requirements for standardized post-intervention effect estimation.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We updated the search in PubMed, Embase, PsycINFO, CENTRAL, and Web of Science to the revised search date and rebuilt an auditable cross-database record ledger. The updated workflow included 3,786 exported records, 451 DOI\/PMID duplicates, 41 confirmed title\/author duplicates, and 3,294 unique records screened. All title\/abstract records completed a two-reviewer screening and consensus chain. Forty-six update-search records entered research-level\/full-text assessment. Randomized controlled trials were eligible for causal quantitative synthesis; relevant feasibility, acceptability, usability, and non-comparable controlled studies were retained narratively. Standard post-intervention analyses used Hedges g, REML random-effects models, and Hartung-Knapp adjustment, and were restricted to attributable two-group post-intervention data.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In the final locked non-active\/low-intensity comparator analysis, three depression comparisons favored conversational interventions (k=3; Hedges g=-0.413, 95% CI -0.557 to -0.268; I2\u00a0=\u00a00.0%) and four anxiety comparisons also favored them (k=4; Hedges g=-0.435, 95% CI -0.603 to -0.268; I2\u00a0=\u00a02.1%). In the separate active conversational-control analysis, two MYLO comparisons did not show a reliable incremental effect for depression (k=2; g=0.165, 95% CI -0.580 to 0.911) or anxiety (k=2; g=0.190, 95% CI -0.556 to 0.937). The evidence base remained small, clinically heterogeneous, and limited by attrition, self-reported outcomes, and uncertainty regarding generalizability.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n AI conversational interventions may improve depressive and anxiety symptoms relative to non-active or low-intensity comparators in university students, but the certainty of evidence is low and estimates are based on few comparisons. Evidence against active conversational controls is very uncertain. Outcome-domain differences and architecture-related claims should be treated as hypothesis-generating. Secure, consent-based, human-supervised implementation and objective outcome assessment are priorities for future research.<\/jats:p>\n <\/jats:sec>\n \n Systematic review registration<\/jats:title>\n \n https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420261323382<\/jats:ext-link>\n , identifier CRD420261323382.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1824378","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:29:56Z","timestamp":1791523796000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Chen","family":"Shao","sequence":"first","affiliation":[{"name":"School of Intelligent Engineering, Shandong Management University","place":["Jinan, 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