Abstract Natural language processing (NLP)-enabled artificial intelligence (AI) conversational agents (CAs) are increasingly adopted in digital mental health interventions, yet the efficacy of such CAs grounded in cognitive behavioral therapy (CBT) remains unclear. This study aims to examine the intervention effectiveness of CBT-based NLP-enabled AI CAs in various mental health problems. A total of 15 randomized controlled trials with 1737 participants were included in the analysis. The results indicated that CBT-based NLP-enabled AI CAs showed a small to moderate effect on depressive symptoms and a small effect on negative affect; while the effects on generalized anxiety, stress, and positive affect were not significant after adjusting for publication bias. Subgroup analyses provided preliminary evidence that multi-modal CAs may be more effective than single-modality CAs in reducing depressive symptoms, and that the absence of psychoeducational content was associated with larger post-test effect sizes. Notably, meta-regression revealed that higher-quality studies reported larger effect sizes, suggesting that the true efficacy of these interventions may be underestimated in the current literature. In addition, younger age was associated with a greater reduction in depressive symptoms. These findings underscored the potential of CBT-based NLP-enabled AI CAs in addressing certain mental health issues and in certain populations.
Hang et al. (Fri,) studied this question.