{
  "abstract": "Background: 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.\n\nObjective: 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.\n\nMethods: 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.\n\nResults: 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 = 0.0%) and four anxiety comparisons also favored them (k=4; Hedges g=-0.435, 95% CI -0.603 to -0.268; I2 = 2.1%). 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.\n\nConclusions: 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.\n\nSystematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261323382 , identifier CRD420261323382.",
  "abstract_shown": true,
  "affiliations": [
    "School of Intelligent Engineering, Shandong Management University , ,",
    "Library, Shandong Management University , ,"
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chen Shao"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaochen Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Shengnan Qiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Ang Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Shipeng Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanglong Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eehgkcn8wgfy6.bib",
  "cited_by": 0,
  "doi": "10.3389/fpsyt.2026.1824378",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690801/?tool=EBI",
  "id": "eehgkcn8wgfy6",
  "issns": [
    "1664-0640"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13690801",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3202|Digital Mental Health Interventions"
    ]
  ],
  "openalex": "W7221192465",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13690801/pdf/?tool=EBI",
  "pmcid": "PMC13690801",
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/eehgkcn8wgfy6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:59Z",
      "native_id": "PMC:PMC13690801",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13690801"
    },
    {
      "first_seen": "2026-10-30T12:20:18Z",
      "native_id": "10.3389/fpsyt.2026.1824378",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1824378/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1664-0640",
      "subfield": "psychiatry"
    }
  ],
  "title": "Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis",
  "title_text": "Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:18Z",
  "url": "https://research.draconitic.com/paper/eehgkcn8wgfy6",
  "venue": "Frontiers in psychiatry",
  "versions": []
}