Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis
- 1School of Intelligent Engineering, Shandong Management University , ,
- 2Library, Shandong Management University , ,
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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261323382 , identifier CRD420261323382.
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BibTeX
@article{shao2026efficacy,
title = {{Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis}},
author = {Chen Shao and Xiaochen Chen and Shengnan Qiao and Ang Li and Shipeng Liu and Yanglong Chen},
journal = {Frontiers in psychiatry},
year = {2026},
month = oct,
doi = {10.3389/fpsyt.2026.1824378},
url = {https://europepmc.org/article/PMC/PMC13690801},
note = {CC BY},
}
RIS
TY - JOUR
TI - Efficacy of artificial intelligence-driven conversational agents (chatbots) for mental health promotion among university students: a systematic review and meta-analysis
AU - Chen Shao
AU - Xiaochen Chen
AU - Shengnan Qiao
AU - Ang Li
AU - Shipeng Liu
AU - Yanglong Chen
PY - 2026
DA - 2026/10/09
JO - Frontiers in psychiatry
DO - 10.3389/fpsyt.2026.1824378
UR - https://europepmc.org/article/PMC/PMC13690801
AB - 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261323382 , identifier CRD420261323382.
SN - 1664-0640
ER -