The study, conducted by Omoniyi Ogheneruemu Olagunju and Ayomide Teslim Ogundele of the Department of Architecture at Covenant University in Ota, Nigeria, set out to test a deceptively simple architectural proposition: that a school campus could be organized as a graduated sequence of spaces, running from high-stimulation public zones at one end to quiet, restorative settings at the other, with transition zones acting as sensory buffers between them. The idea originated in earlier unpublished postgraduate design work, and the researchers were careful to treat it not as evidence but as a hypothesis — an a priori proposition to be audited against what the literature actually shows.

To do that, the team followed Whittemore and Knafl’s integrative-review methodology, embedding a systematic search across six databases — Scopus, Web of Science Core Collection, ERIC, PsycINFO, MEDLINE/PubMed and the Avery Index to Architectural Periodicals — covering January 2000 through 15 July 2026. Study selection was reported using PRISMA 2020 principles. Across two identification streams, 73 reports were assessed and 26 evidence sources were included: 15 empirical and review sources from the systematic database stream, plus two theoretical sources, seven Nigeria- and Ota-specific contextual sources, and two practice-oriented sources identified through supplementary searching. Primary studies were appraised with the Mixed Methods Appraisal Tool, and reviews with item-level judgments drawn from AMSTAR 2, with methodological quality deliberately kept separate from how directly each source matched secondary-school-age neurodivergent learners.

The central finding is a rejection. The evidence did not support treating stimulus intensity as a single high-to-low axis. Environmental demands — acoustic, visual, movement, social, thermal, olfactory and tactile — vary independently of one another. A school cafeteria is plausibly high in social, movement and acoustic demand simultaneously while remaining only moderate in visual complexity; a science laboratory may be high in olfactory and tactile demand and task predictability but moderate in social demand. Collapsing both into one stimulus score would misrepresent them both. Worse, a strict gradient risks recreating the very homogenization of neurodivergence the review critiques: hyposensitive learners may actively seek stronger input, while hypersensitive learners need attenuation, so the desirable level of stimulation is learner-dependent, not spatially fixed.

In place of the single axis, the researchers reformulated their framework as a multidimensional demand-profile network. Each setting is characterized by a configuration of demands across separable dimensions, and the framework adds three constructs the original proposition lacked: transition magnitude, defined as the degree of change in demand encountered when moving between adjoining settings; predictability and legibility, treated not as another demand but as a cross-cutting characteristic describing how well a learner can anticipate and prepare for those demands; and learner-perceived control, elevated to a central moderating mechanism. The evidence for control is among the strongest in the review: experiments with autistic children aged 4 to 12 showed increased attention and fewer repetitive behaviors when the children could manipulate multi-sensory-environment equipment, and studies of autistic pupils aged 11 to 15 found that self-directed use of such environments supported wellbeing and the management of sensory overwhelm.

The review is transparent about where its evidence is strongest and weakest. Direct, participatory studies of autistic adolescents in mainstream secondary schools — including a synthesis of 36 qualitative studies identifying environments that suit me and agency to meet my needs as core themes, and photo-voice work in which students described the sensory environment as unnatural — anchor the framework firmly in autism research. By contrast, direct built-environment evidence for ADHD is comparatively limited, and evidence for dyslexia and dyspraxia rests largely on practice-oriented sources about glare, high-contrast lighting and daylight, which the authors classify as low-confidence illustrative context rather than tested design rules. The framework is therefore described as autism-anchored in evidential support and neuroinclusive in ambition, with transfer to other neurodivergent groups treated as provisional.

The researchers also mapped their contribution against existing frameworks rather than assuming novelty. Mostafa’s Autism ASPECTSS Design Index names acoustics, spatial sequencing, escape space, compartmentalization, transition spaces, sensory zoning and safety, and has been tested through post-occupancy evaluation; Universal Design for Learning addresses instructional rather than spatial variability; Attention Restoration Theory, operationalized through biophilic design, supplies a restoration mechanism but was not developed for neurodivergent adolescents. What the Sensory-Gradient Framework adds, the authors argue, is not any single design quality but the combination of campus-scale adjacency and sequencing, transition magnitude as an explicit and measurable design variable, and learner-perceived control as a central moderating mechanism, applied simultaneously across campus, building and room scales — and formalized as seven testable propositions rather than fixed prescriptions.

What makes the study unusual is where it lands the framework. Ota, in Ogun State, sits in Nigeria’s tropical wet-and-dry climate zone, where mechanical climate control cannot be assumed and passive design is a necessity rather than a preference. Nigerian studies confirm that orientation, shading and cross-ventilation improve classroom thermal conditions, while Ota-specific rainfall measurements document high rain rates that interact directly with acoustic exposure. The framework’s contextual application therefore surfaces genuine trade-offs: cross-ventilation that reduces cooling load also admits traffic and rain noise; generous daylighting that reduces dependence on flickering artificial light risks glare and high contrast; verandas and covered walkways needed for rain protection can become high-noise corridors unless deliberately differentiated with pause points and staggered circulation.

Resource constraints shape the application further. A survey of 47 staff across three Lagos special-needs schools found gaps in spatial flexibility, accessibility and sensory design, with sensory and physical impairments rated the most severe student challenge, and studies of Nigerian public buildings corroborate a persistent gap between inclusive-education policy commitments and physical-infrastructure delivery. The authors are explicit that architecture alone cannot deliver neuroinclusive outcomes when staffing, maintenance or space-management practices undermine it — a retreat space cannot function as a regulatory resource if it is routinely repurposed as overflow teaching space. In cash-constrained schools, they propose low-cost operational equivalents: furniture-based zoning, temporary screens, timetabling that staggers high-density transitions, low-maintenance planting for shade and visual buffering, and route choice created through supervision and scheduling rather than new construction.

The authors stress that the resulting framework is evidence-informed and testable rather than empirically validated, and they frame a research agenda accordingly: co-design with neurodivergent Nigerian secondary-school students, post-occupancy evaluation of schools that operationalize the framework, direct measurement of acoustic, lighting, thermal, movement and social-demand conditions in Ota-area schools, and prospective testing of whether transition magnitude, predictability and perceived control predict regulatory outcomes. For a field that has largely been built from evidence gathered in high-income, temperate-climate settings, the study’s insistence that sensory design must be reconciled with passive cooling, daylight, rain noise and institutional reality in sub-Saharan Africa may prove its most consequential contribution — a reminder that inclusive architecture is not a universal template but a negotiation between evidence, climate and constraint.

Subject of Research: A sensory-gradient framework for neuroinclusive secondary-school design tested against evidence and contextualized for tropical Nigeria

Article Title: Toward a sensory-gradient framework for neuroinclusive secondary-school design: an integrative review and contextualization for a tropical Nigerian setting

Article References: Toward a sensory-gradient framework for neuroinclusive secondary-school design: an integrative review and contextualization for a tropical Nigerian setting. (n.d.). https://doi.org/10.3389/fbuil.2026.1964960

Image Credits: AI Generated

DOI: 10.3389/fbuil.2026.1964960

Keywords: neuroinclusive design, autism, ADHD, dyslexia, dyspraxia, sensory processing, school architecture, Nigeria, passive design, integrative review, transition magnitude, learner control

Metacognition, in the framework the researchers adopted, covers both knowledge about one’s own cognition and the regulation of cognitive activity through planning, monitoring, evaluation, and strategic control. Digital environments are natural arenas for these processes because they flood learners with choices—multiple pathways, abundant resources, automated feedback—and demand that learners judge whether their strategies are working. Technologies can be designed to scaffold regulation directly: prompts can nudge goal setting, dashboards can make progress visible, learning diaries can structure reflection, and feedback systems can highlight the gap between intended and actual performance. With the arrival of intelligent tutoring systems, learning analytics, and now generative AI capable of open-ended explanation and critique, the repertoire of possible support has expanded dramatically. But more capable systems can also perform regulatory work on the learner’s behalf, raising a question that runs through the entire review: does the technology strengthen independent regulation, or quietly absorb it?

To answer such questions rigorously, the team followed PRISMA 2020 reporting standards and searched five major databases—Web of Science, Scopus, ERIC, IEEE Xplore, and PsycINFO—completing searches on 9 July 2026 with no lower publication-year limit. The initial sweep returned 5,326 records. After removing 2,532 duplicates, 2,794 unique records underwent title-and-abstract screening, and 168 reports were assessed in full text. Thirty-three studies ultimately met all eligibility criteria: each had to involve students in formal K–12 or post-secondary settings, compare a technology-supported intervention against a control or business-as-usual condition, and report a quantitative metacognitive outcome from which a standardised effect size could be derived. Two researchers screened and coded everything independently, achieving Cohen’s kappa values between 0.84 and 0.94 on categorical variables and intraclass correlations above 0.94 on continuous quantities—agreement figures that signal unusually disciplined extraction.

The statistical engine of the study was a three-level random-effects meta-analysis, a model chosen because the 33 studies contributed 94 effect sizes and multiple effects from the same study cannot be treated as independent. Level 1 of the model captured sampling variance, Level 2 captured heterogeneity among effect sizes within studies, and Level 3 captured heterogeneity between studies. The pooled estimate of g = 0.653 was highly significant, but the heterogeneity was substantial: total I² reached 72.7 percent, and virtually all of the excess variance sat at the between-study level, with the within-study component estimated at effectively zero. A 95 percent prediction interval stretching from roughly −0.02 to 1.32 drives the point home. In a comparable future implementation, the true effect could plausibly be negligible—or large. The average, in other words, describes a scattered landscape rather than a uniform intervention effect.

Perhaps the most revealing part of the analysis is a descriptive map of what the interventions actually did. Evaluation and reflection were explicitly supported in 29 of 33 studies, or 87.9 percent; monitoring and strategic control each appeared in 26 studies, or 78.8 percent; but planning was represented in only 19 studies, or 57.6 percent. The pattern suggests that most digital interventions intervene after learning is under way—prompting learners to inspect progress, self-assess, and revise—while far fewer help them set goals and select strategies before starting. Because goals and task analysis provide the foundation on which monitoring and evaluation depend, the authors argue that future designs should attend to the full regulatory cycle rather than its back half.

The outcome evidence was even more lopsided. Metacognitive regulation accounted for 77 of the 94 effect sizes, or 81.9 percent, while metacognitive knowledge contributed just 11 effects and composite measures six. More striking still, 90 of 94 effects—95.7 percent—rested on self-report instruments. Performance-based, judgement-based, or calibration indicators produced only three effects, trace or process data contributed one, and not a single study used researcher-rated task products. This means the pooled estimate primarily summarises changes in what learners say about their own planning, monitoring, and reflection, not independently demonstrated regulatory skill. Self-reports are legitimate data, and learners’ perceptions of their strategy use can genuinely change in response to instruction, but perceived regulation is not equivalent to accurate or effective regulation enacted during performance—a distinction long emphasised in the metacognition literature.

The exploratory moderator analyses generated the study’s most newsworthy, and most qualified, findings. Three implementation features cleared the conventional unadjusted significance threshold: interventions with explicit technology-use guidance outperformed unguided ones (g = 0.760 versus 0.479), immediate feedback beat delayed feedback (g = 0.744 versus 0.408), and system-directed learner control beat learner-directed control (g = 0.780 versus 0.499). But none of these survived Benjamini–Hochberg false-discovery-rate correction across the eight moderator tests, with each adjusted p value landing at 0.095. Crucially, the three features overlapped heavily in practice—14 of the 20 guided studies also provided immediate feedback—so their apparent effects may reflect a bundled instructional style rather than three separable design principles. The authors explicitly frame these patterns as hypothesis-generating rather than as established moderator effects.

Technology type, notably, did not significantly moderate outcomes. Generative-AI interventions showed the largest descriptive subgroup estimate at g = 0.831, compared with 0.643 for non-AI technologies and 0.346 for non-generative AI, but those subgroups contained only nine and four studies respectively, drawn from heterogeneous systems. The findings therefore do not support any claim that AI, or generative AI in particular, is intrinsically superior for building metacognition. Educational level, subject area, and intervention duration also failed to explain meaningful variation. The robustness of the headline result, by contrast, was impressive: cluster-robust inference reproduced the same point estimate, imposing assumed within-study correlations from 0.30 to 0.90 shifted the pooled estimate only between 0.640 and 0.651, leave-one-study-out estimates ranged from 0.625 to 0.675, and excluding the two studies rated at serious risk of bias left the association intact at g = 0.624.

The risk-of-bias picture tempers enthusiasm further. Among the 33 studies, only nine used randomised controlled designs; 22 quasi-experimental studies were judged at moderate risk under ROBINS-I, two at serious risk, and no study at all received a low overall risk rating. A cluster-robust Egger-type precision test found no statistically detectable evidence of small-study effects, though the authors interpret this as an absence of evidence rather than evidence that publication bias is absent. These caveats, combined with the self-report dominance, mean the pooled effect is best read as a positive average association in reported metacognitive regulation rather than proof of uniformly improved metacognitive capability.

The practical upshot is a design philosophy rather than a shopping list. The authors argue that technology selection should begin with the regulatory problem to be solved—goal setting, monitoring, evaluation, or strategy adaptation—rather than with the technological label, and that AI systems in particular should be judged by which regulatory decisions remain with the learner and how support is faded over time. For researchers, the priorities are larger controlled studies, transparent descriptions of what each design feature is meant to elicit, and multimethod assessment combining self-reports with calibration judgements, performance indicators, and behavioural traces. As generative AI spreads through classrooms, the difference between a tool that cultivates independent thinkers and one that induces what other researchers have called metacognitive laziness may depend on exactly those choices. This synthesis gives educators a genuine, if qualified, reason for optimism—and a precise agenda for finding out what actually works.

Subject of Research: Effects of technology-supported educational interventions, including AI tools, on learners' metacognition

Article Title: Technology-Supported Interventions and Learners’ Metacognition: A Three-Level Meta-Analysis

Article References: Yuan, Y., & Du, Y. (2026). Technology-Supported Interventions and Learners’ Metacognition: A Three-Level Meta-Analysis. Educational Psychology Review, 38(1), Article 130. https://doi.org/10.1007/s10648-026-10227-3

Image Credits: AI Generated

DOI: 10.1007/s10648-026-10227-3

Keywords: metacognition, educational technology, meta-analysis, self-regulated learning, generative AI, artificial intelligence in education, learning analytics, feedback, scaffolding, self-report measures, educational psychology, learner control