ReviewFrontiers in artificial intelligence2026
Artificial intelligence and the transformation of education: a systematic narrative review towards adaptive epistemic ecosystems and post-linear pedagogy.
Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.