Abstract
Background: Adults closest to children, including parents and caregivers, teachers, and therapists, are major determinants of child mental health outcomes. However, access to high-quality psychological training for these groups remains severely limited and inequitable. Artificial intelligence (AI) tools may offer a scalable, accessible, and low-cost route to training delivery. This review aimed to provide the first systematic synthesis of evidence on AI tools used to train caregivers, educators, and therapists/practitioners in psychological approaches relevant to child and adolescent mental health. Methods: A systematic review was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420261336167). Five databases, MEDLINE, PsycINFO, Embase, Web of Science, and ERIC, were searched from inception to March 2026, supplemented by reference hand-searching and forward citation tracking. Studies were eligible if they evaluated an AI-based training tool used with adults in caregiving, educational, or therapeutic roles involving children or adolescents aged 0–18 years, delivered a defined psychological approach, and reported at least one training outcome. Owing to substantial methodological and outcome heterogeneity, findings were synthesised narratively, and meta-analysis was not undertaken. Results: Twenty-four studies from nine countries, published between 2019 and 2026, met inclusion criteria. Studies were grouped into caregiver training (Group A, 5 papers), educator training (Group B, 3 papers), and therapist/practitioner training (Group C, 16 papers). Identified AI modalities included natural language processing (NLP)-based chatbots, generative AI/large language model (LLM) systems, AI-integrated virtual reality (VR), and AI-based feedback and analysis tools. Feasibility and acceptability findings were generally positive across groups. However, the evidence base was limited by pervasive methodological weaknesses, including small samples, with most studies enrolling fewer than 30 participants, reliance on unvalidated self-report outcomes, and the absence of follow-up data beyond one month. Conclusions: AI tools show early promise as scalable approaches to psychological training, particularly for procedural skill acquisition and enhancement of practitioner self-efficacy. However, the current evidence base is insufficient to support claims of effectiveness. A structural credibility–accessibility paradox characterises the field: tools with the strongest controlled evidence are the least scalable, while the most accessible tools have the weakest empirical support. Adequately powered, independent randomised controlled trials (RCTs) using validated outcomes, active comparators, and follow-up extending over multiple months are needed across all three population groups.
