Background: The increasing prevalence of mental health issues among adolescents and young adults, coupled with barriers to accessing traditional therapy, has led to growing interest in artificial intelligence (AI)-driven conversational agents (CAs) as a novel digital mental health intervention. Despite accumulating evidence suggesting the effectiveness of AI-driven CAs for mental health, there is still limited evidence on their effectiveness for different mental health conditions in adolescents and young adults.
Objective: This study aims to examine the effectiveness of AI-driven CAs for mental health among young people, and explore the potential moderators of efficacy.
Methods: A total of 5 main databases (PubMed, PsycINFO, Embase, Cochrane Library, and Web of Science) were searched systematically dated from the establishment of the database to August 6, 2024. Randomized controlled trials comparing AI-driven CAs with any other type of control condition in improving depressive symptoms, generalized anxiety symptoms, stress, mental well-being, and positive and negative affect were considered eligible when they were conducted in young people aged 12-25 years. The quality of these studies was assessed using the Cochrane Risk of Bias tool. Data were extracted by 2 independent reviewers and checked by a third reviewer. Pooled effect sizes (Hedges g) were calculated using random effect models and visually presented in forest plots.
Results: A total of 14 articles (including 15 trials) were included, involving 1974 participants. The results indicated that, after adjustment for publication bias, AI-driven CAs had a moderate-to-large (Hedges g=0.61, 95% CI 0.35-0.86) effect on depressive symptoms compared to control conditions. However, their effect sizes adjusting for publication bias for generalized anxiety symptoms (Hedges g=0.06, 95% CI -0.21 to 0.32), stress (Hedges g=0.002, 95% CI -0.19 to 0.20), positive affect (Hedges g=0.01, 95% CI -0.24 to 0.27), negative affect (Hedges g=0.07, 95% CI -0.13 to 0.27), and mental well-being (Hedges g=0.04, 95% CI -0.21 to 0.29) were all nonsignificant. Subgroup analyses revealed that AI-driven CAs were particularly effective in improving depressive symptoms among subclinical populations (Hedges g=0.74, 95% CI 0.50-0.98).
Conclusions: The findings highlight the potential of AI-driven CAs for early intervention in depression among this population, and underscore the need for further improvements to enhance their efficacy across a broader range of mental health outcomes. Key limitations of the reviewed evidence include heterogeneity in therapeutic orientations of CAs and lack of follow-up measures. Future research should explore the long-term effects of AI-driven CAs on mental health outcomes.
Keywords: artificial intelligence; conversational agents; mental health intervention; meta-analysis; young people.
©Yi Feng, Yaming Hang, Wenzhi Wu, Xiaohang Song, Xiyao Xiao, Fangbai Dong, Zhihong Qiao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.05.2025.