Actionable companion · Feedback & scaffolding

Learning with agency: Artificial intelligence and the transformation of education: a systematic narrative review towards adaptive epistemic ecosystems and post-linear pedagogy

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:

Abstract Artificial intelligence is increasingly embedded in educational processes, yet much of the literature continues to examine individual tools rather than the relationships amongst pedagogy, curriculum, assessment, knowledge validation, institutional authority, and governance. This systematic narrative review examines these relationships whilst explicitly separating evidence-based patterns from conceptual interpretation. The review followed a PRISMA-informed protocol covering database selection, Boolean search strategy, eligibility criteria, staged screening, type-appropriate quality appraisal, structured extraction, citation auditing, and thematic synthesis. Scopus, Web of Science Core Collection, ERIC, and ScienceDirect formed the reproducible database core, whilst Google Scholar and backward and forward citation tracking were used as supplementary discovery mechanisms. After duplicate removal, title and abstract screening, full-text assessment, and quality appraisal, 124 studies were retained for synthesis. The review addressed four questions concerning how artificial intelligence is conceptualised in contemporary educational research; how it is associated with changes in pedagogy, curriculum, learning analytics, assessment, and governance; what recurring relationships connect adaptive feedback, curriculum sequencing, human-AI roles, epistemic validation, and institutional accountability; and what ethical, epistemological, and governance challenges accompany increasing human-AI participation. The synthesis indicates an uneven movement from isolated tool adoption towards more adaptive and data-informed arrangements, accompanied by substantial debate over technological determinism, platform power, teacher agency, language bias, data colonialism, and epistemic justice. Adaptive epistemic ecosystems and post-linear pedagogy are therefore advanced as synthesis-derived, integrative conceptual constructs rather than predetermined coding categories or universal empirical claims. Their contribution lies in specifying testable relationships amongst human-AI cognition, learning-data infrastructures, pedagogical adaptation, epistemic validation, governance, and contextual boundary conditions whilst preserving the distinction between observed patterns, interpretive synthesis, and future-oriented propositions. Keywords: adaptive epistemic ecosystems, adaptive learning, AI governance, artificial intelligence in education, educational transformation, intelligent tutoring systems, learning analytics, systematic narrative review

What kind of evidence is this?

Intelligent tutoring & adaptive learning · 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

The questions raised by artificial intelligence are not entirely new to educational theory. Long before the emergence of contemporary AI systems, educational scholars debated how knowledge is produced, mediated, and legitimised within social institutions (Javadinejad and Davari, 2026). Dewey conceptualised learning as an adaptive process grounded in experience, inquiry, and participation in democratic communities (English, 2023). Vygotsky emphasised the socially mediated nature of cognition and the role of cultural tools in extending human learning and development (Rigopouli et al., 2025). Freire challenged hierarchical models of knowledge transmission and argued for dialogical forms of education that position learners as active participants in knowledge creation rather than passive recipients of information (Harris and Roter, 2024). Similarly, Bourdieu’s analysis of cultural reproduction and Foucault’s examination of power–knowledge relations provide important perspectives for understanding how algorithmic systems may shape educational opportunity, access to knowledge, and institutional authority (Christensen, 2024). More recently, socio-constructivist and connectivist traditions have highlighted learning as a distributed process emerging through interactions amongst individuals, communities, technologies, and information networks (Lu et al., 2025). These theoretical perspectives collectively suggest that educational transformation is not simply a technological phenomenon but also an epistemological, social, cultural, and political process.

Limitations and uncertainty · source passage

The breadth of the review is integrative rather than encyclopaedic. The corpus was designed to connect evidence across pedagogy, curriculum, assessment, human-AI roles, institutional governance, and epistemic justice, but it does not provide equal representation of every educational level, region, technology, or research design. The corpus-level mapping reported above demonstrates this uneven evidence density. Accordingly, claims in comparatively lower-density domains are treated as emerging or provisional patterns rather than dominant or universally consistent findings. This calibration narrows the interpretive reach of the framework whilst retaining the cross-domain scope needed to examine systemic educational change.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
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.

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

Sholpan Saparbaykyzy; Zhibek Tajibayeva; Elmira Aitenova; Ayjan Nauruzbaeva; Gulnur Nakypova; Aydin Daukeeva; Timoth Mkilima. Artificial intelligence and the transformation of education: a systematic narrative review towards adaptive epistemic ecosystems and post-linear pedagogy. Frontiers in Artificial Intelligence (2026-09-11). https://doi.org/10.3389/frai.2026.1899576

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.