Actionable companion · Self-regulation & metacognition

Learning with agency: AΙ-Driven Interventions for Neurocognitive, Self-Regulation, and Adaptive Skill Development in Neurodevelopmental and Cognitive Disorders: A Systematic Review of Randomized Controlled Trials

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background: Artificial intelligence (AI) is increasingly being used in interventions among individuals with neurodevelopmental and cognitive disorders, offering personalized and adaptive approaches that advance traditional therapeutic practices. Although previous reviews have focused on symptom detection or alleviation, less attention has been paid to the impact of AI in fostering the acquisition of higher-order skills essential for being functional and independent. This review uniquely addresses this gap by synthesizing evidence from randomized controlled trials on AI-driven skill acquisition across multiple domains. Objectives: The objective of this systematic review is to synthesize evidence from randomized controlled trials evaluating the effectiveness of AI-driven interventions in promoting skillfulness. More specifically, it investigates the acquisition of neurocognitive, self-regulation, and adaptive and related skills among individuals with neurodevelopmental and cognitive disorders, including attention deficit and hyperactivity disorder, autism spectrum disorder, dyslexia, dyscalculia, and cognitive impairment. Methods: A systematic search, according to the PRISMA 2020 guidelines, was conducted, selecting randomized controlled trials published between 2019 and 2026. Eligible technologies included conversational agents, intelligent tutoring systems, adaptive training platforms, and machine learning-based interventions. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Results: Twenty-four randomized controlled trials met the inclusion criteria. The findings demonstrated improvements in a wide range of skills, such as attention, working memory, mental flexibility, metacognitive control, emotional regulation, inhibition control, and social and communication skills. Generative AI showed efficacy for language and communication skills, while machine learning-based systems demonstrated positive effects on attention regulation and self-regulation. Conclusions: This review concludes that artificial intelligence can effectively assist conventional interventions for individuals with neurodevelopmental and cognitive disorders. However, the heterogeneity in intervention designs, outcome measures, and participant populations limits generalizability and highlights the need for standardized assessment frameworks, larger-scale longitudinal trials, and mechanistic investigations to translate these preliminary gains into long-term functional improvements across diverse clinical and cultural contexts.

What kind of evidence is this?

Conversational & pedagogical agents · Evidence synthesis

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This systematic review was performed following the methodological standards outlined in the PRISMA 2020 framework [51], which offers structured recommendations for planning, identifying, screening, and integrating empirical evidence. The completed PRISMA checklist can be downloaded at Supplementary Materials. The central aim of the review was to assess whether AI-driven interventions enhance neurocognitive, self-regulatory, adaptive, and related functional skills among children, adolescents, and adults diagnosed with neurodevelopmental and cognitive disorders.

Limitations and uncertainty · source passage

Several limitations of this systematic review are acknowledged. Although the search strategy covered five major databases, it is possible that relevant studies were missed. The restriction to English-language publications may have introduced language bias, potentially excluding valuable research published in other languages. This limitation should be considered when interpreting the generalizability of our findings, as the evidence base may be disproportionately representative of research from English-speaking contexts.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Eleni Mitsea; Athanasios Drigas; Charalabos Skianis. AΙ-Driven Interventions for Neurocognitive, Self-Regulation, and Adaptive Skill Development in Neurodevelopmental and Cognitive Disorders: A Systematic Review of Randomized Controlled Trials. Healthcare (2026-09-20). https://doi.org/10.3390/healthcare14183102

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.