The review’s central conceptual move is to distinguish two kinds of assistance. Instrumental or representational assistance helps learners generate options, translate, format, retrieve, summarise or re-express material. Judgement-bearing assistance goes further: it evaluates correctness, relevance, quality, persuasiveness, ethical acceptability or evidential sufficiency. The two categories can overlap, since even a summary selects what matters, but judgement-bearing assistance is normatively far more demanding because it delegates not only the production of work but the standards by which that work is assessed. A learner may use AI repeatedly yet remain epistemically agentic if the interaction prompts questioning, comparison and revision; another may use it once but treat its answer as decisive in a high-stakes task. The analytic boundary, the authors insist, is not use versus non-use but the relation between assistance and judgement.

To operationalise that boundary, the review proposes six diagnostic criteria separating productive reliance from harmful dependence: contestability, recoverability, transfer, traceability, distributed responsibility and epistemic plurality. Dependence becomes problematic when the relation is difficult to contest, obscures its evidential basis, weakens the learner’s capacity to reconstruct or transfer judgement, or allocates responsibility to people who lack meaningful control. Conversely, frequent AI use can remain productive when it provokes comparison, makes uncertainty visible and leaves the learner more capable of independent and collaborative judgement. The criteria are offered as diagnostic questions for research, design and pedagogy rather than a psychometric scale, and no single criterion is decisive in every context; the pattern matters, as does the importance of the delegated judgement.

The heart of the paper is an analysis of four sociotechnical pathways through which AI affordances and institutional conditions can slide into harmful dependence. The first is fluent authority. Large language models produce grammatically polished, coherent and often confident responses, and fluency is epistemically persuasive because users can mistake ease of processing for reliability. Anthropomorphism, social presence and personalisation can amplify this effect: conversational systems occupy roles associated with human epistemic others, such as respondent, tutor, editor and evaluator, and a supportive tone with apparent memory may encourage the transfer of interpersonal trust to a system that lacks human understanding, responsibility or commitment to the learner. The risk is not error alone but the alignment of error, confidence and immediacy, especially for learners with limited domain knowledge.

The second pathway is frictionless delegation. Generative AI compresses searching, reading, comparing, synthesising and drafting into a single request-response cycle. Cognitive offloading can be adaptive when it frees limited resources for planning, reflection or complex problem solving, but adjacent research on internet search suggests that persistent access to external information can alter memory, confidence and future search behaviour. When intermediate epistemic actions disappear from the workflow, learners may complete tasks without practising the very actions the tasks were intended to develop. Assessment incentives intensify the problem: if institutions reward polished products while leaving process and justification invisible, delegating epistemic work becomes rational, and AI shifts from scaffold to substitute, particularly in feedback and evaluation.

The third pathway is opaque synthesis. Conversational AI often presents an integrated answer without making its source selection, weighting, exclusions or uncertainty inspectable. Traditional search was never transparent or neutral, but it commonly exposed multiple documents, authors and domains, even if learners evaluated them poorly. Conversational synthesis can hide that plurality behind one voice, transforming the learner’s task from selecting among visible sources to recovering the evidential structure of an answer that appears already complete. This can weaken verification while improving the surface quality of the product, making epistemic deficits harder for teachers to detect. It also threatens disciplinary reasoning, since a generic synthesis may flatten the distinct standards by which historical, scientific and philosophical claims are warranted.

The fourth pathway is institutionalised dependence. Universities and schools shape reliance through procurement, platform integration, assessment design, timetabling, policy and professional development, while commercial systems encode objectives concerning engagement, speed, cost and data capture. Once AI is built into learning management systems, writing environments and feedback workflows, it may become the default route into academic work rather than a discrete tool chosen by the learner. Institutionalisation also redistributes authority and responsibility: a student may be held accountable for claims generated through an institutionally licensed but opaque system, a teacher may be expected to police use without access to system logs, and a university may depend on vendor assurances that cannot be independently audited. The review argues that responsibility should track control, knowledge and benefit across learners, educators, institutions and providers, rather than being dumped on the end user.

This institutional dimension brings epistemic justice to the centre of the analysis. Generative systems draw on unequal knowledge infrastructures and may reproduce dominant languages, classifications and perspectives while marginalising local, minoritised or experiential knowledge. Drawing on Miranda Fricker’s account of epistemic injustice and on recent work on formative epistemic injustice, the authors argue that learning arrangements can wrong students by restricting the knowledge, practice and accurate self-assessment through which they develop as knowers. Repeated reliance may also reshape learner identity, fostering a self-conception of being unable to write, understand or judge without AI. Productive reliance should expand participation and capability over time; harmful dependence makes learners and institutions more fragile when the system is absent, changes its terms or fails particular communities.

Against these risks, the review proposes relational epistemic agency as the normative aim: the capacity to question, verify, compare, justify and take responsibility for knowledge claims within human, technological and institutional relations. This is not independence from the machine, and it is not an anti-dependence position. Education has always involved dependence on teachers, peers, texts, instruments and institutions, and ideals of self-sufficient knowing are both unrealistic and exclusionary. The relevant test is functional and developmental: does the human-technology relation enlarge the learner’s capacity to participate in epistemic practice, or merely deliver a product? A calculator supports mathematical agency when the learner understands when and why its operations are appropriate; a generative model supports inquiry when it expands hypotheses, reveals alternatives and prompts verification. The same tools bypass agency when they become non-contestable authorities.

The practical implications are concrete. Designers should provide claim-level provenance where feasible, distinguish retrieved evidence from model-generated synthesis, represent uncertainty through alternatives and explicit unknowns, preserve user control over prompts and outputs, and surface disagreement and culturally diverse sources. Pedagogy should move from policing AI use to teaching AI-mediated judgement, with routines for lateral reading, source triangulation and claim verification, and assignments that require learners to annotate AI responses, identify unsupported assumptions and document why they accepted or rejected particular suggestions. Assessment should make process, judgement and transfer visible through staged drafts, oral defence, source maps and reflective decision logs, without becoming surveillance-heavy. Institutions, meanwhile, should evaluate procurement for provenance, bias, auditability and data governance, and specify which learning outcomes must remain demonstrably human. The empirical agenda that follows is equally clear: researchers must track which epistemic actions are preserved, transformed or displaced, using process evidence such as interaction traces and think-aloud protocols, and must examine organisations and markets, not only learners. The future of knowing, this review suggests, depends less on how often students use AI than on whether the systems and institutions around them are configured so that judgement survives the delegation.

Subject of Research: Epistemic dependence and learner agency in AI-mediated education

Article Title: Epistemic dependence in AI-mediated learning

Article References: Du, Y., & Yuan, Y. (2026). Epistemic dependence in AI-mediated learning. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03294-1

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03294-1

Keywords: generative AI, epistemic dependence, epistemic agency, AI in education, cognitive offloading, epistemic justice, evaluative judgement, large language models, human-AI interaction, assessment, educational governance, AI & Society