Citation

Yaacob, Noorayisahbe Mohd and Doheir, Mohamed and Algumaei, Ali and Al-Andoli, Mohammed Nasser and Alsayaydeh, Jamil Abedalrahim Jamil and Elzamly, Abdelrafe and Basari, Abd Samad Hasan (2026) AI-enabled and comparator digital interventions for adolescent mental health (2020–2025): early detection, human–AI collaboration, and ethical governance—a PRISMA-ScR scoping review. Frontiers in Child and Adolescent Psychiatry, 5. ISSN 2813-4540

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Abstract

Introduction: Mental health difficulties among adolescents and youth (ages 10–24) are increasing globally, yet early intervention remains impeded by stigma, service shortages, and cultural barriers. AI-enabled technologies including chatbots, automated screening tools, and machine learning-based predictive models have been proposed as scalable approaches to address these gaps, alongside non-AI digital interventions such as therapist-guided internet-delivered cognitive behavioral therapy (iCBT) and smartphone-based CBT applications, which are examined as comparator evidence given their stronger controlled-design evidence base.Methods: This scoping review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines and Joanna Briggs Institute (JBI) methodology. A systematic database search across six databases (PubMed, Scopus, ProQuest, SpringerLink, Web of Science, and ScienceDirect), supplemented by manual and forward-citation searching, yielded 10,962 records, of which 19 studies met the predefined inclusion criteria. A Human-AI Collaboration Role framework comprising a four-level continuum from fully human-led to fully automated delivery was applied as a predefined coding dimension across all included studies.Results: Most studies focused on depression and anxiety among mid-to-late adolescents in high-income countries (68.4%). Therapist-guided iCBT was the most rigorously studied format using randomized and controlled designs; AI chatbots, predictive models, and large language model (LLM)-based systems demonstrated feasibility or engagement outcomes in a limited number of studies and contexts, though randomized diagnostic chatbot trials specifically remain absent for participants under 18 years; at least one randomized non-diagnostic chatbot trial (a body-image intervention) has been conducted in this age group. Equity-related reporting was inconsistent: socioeconomic status was reported in only 10.5% of studies and cultural adaptation in 42.1%; restriction to English-language publications may further underrepresent non-Western and low- and middle-income-country evidence relevant to these equity findings. Key implementation challenges included inconsistent safeguarding protocols for minors, algorithmic bias, data privacy concerns, and insufficient integration with clinical and educational systems.Discussion: The evidence base remains preliminary and methodologically heterogeneous, insufficient to support conclusions about clinical effectiveness or generalizability to diverse adolescent populations. These findings represent a descriptive map of the current evidence landscape rather than an assessment of effectiveness. Future research should prioritize adequately powered adolescent-focused trials, participatory co-design approaches, culturally informed adaptations, and transparent ethical governance frameworks to ensure responsible development of AI-enabled digital mental health tools for youth. Because the included studies were heterogeneous and no formal risk-of-bias assessment was undertaken, this review does not establish comparative clinical effectiveness, safety, or superiority of AI-enabled interventions.

Item Type:	Article
Uncontrolled Keywords:	adolescent mental health, AI chatbots, cultural adaptation, digital mental health interventions, early detection, ethical governance, human–AI collaboration, PRISMA- ScR scoping review
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Oct 2026 06:57
Last Modified:	30 Oct 2026 06:57
URII:	http://shdl.mmu.edu.my/id/eprint/17053

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