What the evidence says
The claims on this page do not cover the stated topic. None of them studies people at clinical high risk for psychosis (CHR-P), and none studies CBT delivered by a clinician. All six come from one meta-analysis of 15 randomized trials of CBT-based AI conversational agents, with depression as the main outcome. For CHR-P, subgroup and moderator evidence is absent from these sources. Read the findings below as evidence about a different population and delivery format.
Within that meta-analysis, the moderators for depressive symptoms were design features and participant features. Agents supporting text, voice, and/or image interactions (multimodal) showed a larger effect than single-modality agents: g = 0.82 (95% CI [0.54, 1.09]). That comparison rests on only 2 multimodal studies. The authors say design and implementation factors likely confound it.
Agents without psychoeducational content showed a larger post-test effect on depression: g = 0.47 (95% CI [0.25, 0.69]). The authors call this preliminary because few studies were available. Both subgroup findings are associations between study features and effect size. They do not show that adding or removing a feature changes outcomes.
Two meta-regressions found moderators of the post-test effect on depression. Mean participant age had a coefficient of β = −0.02 (p < 0.05), so younger samples showed larger reductions. Study quality, assessed with the Cochrane Risk of Bias tool, had a coefficient of β = 0.11 (p < 0.05), so higher-quality studies reported larger effects. The authors read the quality result as a sign that true efficacy may be underestimated in the current literature.
Secondary outcomes are weaker. CBT-based agents decreased a negative-emotion outcome by a small amount, g = 0.21 (95% CI [0.02, 0.39]), and the authors say effects this small may not be meaningful in clinical practice. The generalized anxiety effect, g = 0.18 (95% CI [0.05, 0.31]), fell to g = 0.12 (95% CI [−0.03, 0.27]) after a publication-bias adjustment.
Where the evidence splits
The generalized anxiety result splits within the one source. The unadjusted estimate was g = 0.18 (95% CI [0.05, 0.31], p < 0.01). After trim-and-fill imputation of missing studies, it was g = 0.12 (95% CI [−0.03, 0.27], p > 0.05), and the interval includes zero. The difference comes from the publication-bias adjustment: the authors note that the initial estimate may have been inflated. The claims do not say which estimate is closer to the true effect.