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

The two researchers introduce a construct they call Epistemic Well-being at Work, or EWW, defined as the organisational condition under which individuals and collectives can exercise responsible professional judgement, effective epistemic agency, and recognised epistemic responsibility within structures of authority. In plain terms, EWW asks whether workers can still produce, evaluate, contest, and justify knowledge claims that actually influence consequential decisions, and whether they can answer for those decisions in terms of reasons rather than mere metric compliance. Unlike hedonic well-being, which tracks pleasure and satisfaction, or eudaimonic well-being, which tracks meaning, growth, and autonomy, EWW is not a psychological state at all. It is a structural property of the organisation, expressed through governance practices, process design, incentive systems, and criteria of legitimacy. A workplace can be emotionally positive and epistemically fragile at the same time.

The paper’s central critique targets how organisational research has traditionally positioned workplace conditions. Decades of scholarship on leadership, organisational climate, justice, human resource practices, and work design have treated these factors as antecedents, resources, or moderators that explain an individual’s affective, evaluative, or functioning outcomes. Within the influential Job Demands-Resources model, for example, well-being emerges from the balance between demands and resources, predicting engagement, burnout, and performance. The authors do not dispute the value of this tradition. Their point is narrower and more radical: these models rarely ask whether organisational arrangements are constitutive of whether a worker’s reasons can enter, challenge, and alter consequential decisions. The missing level of analysis, they argue, is not the organisation itself but the epistemic architecture of organisational authority, meaning the structures that determine whose knowledge counts and whose can be overruled.

What makes this gap urgent is the arrival of intelligent technologies whose outputs acquire practical standing in organisational life. The paper’s scope covers algorithmic management and performance analytics, predictive scoring and automated decision-making, professional decision-support systems, workplace surveillance and people analytics, and generative AI when its outputs are treated as knowledge claims or decision inputs. Crucially, the authors distinguish between the technologies themselves and what they call AI-mediated governance. A predictive model is a technical artefact; governance is the institutional arrangement through which organisations give that model’s outputs a role in structuring behaviour, allocating resources, evaluating performance, or authorising decisions. The same system, technically identical in two organisations, may support judgement in one where its output is advisory, contestable, and tied to identifiable human responsibility, and displace judgement in another where it becomes an unquestioned default reinforced by incentives and penalties.

Drawing on recent work by Sergeeva, Leonardi, and Faraj, the authors adopt the view that intelligent technologies do not merely supply information but generate knowledge claims grounded in computational inference, potentially establishing competing epistemic regimes within organisations. These regimes redefine what counts as valid evidence and who is authorised to challenge it. When a dashboard or an automated recommendation acquires a status of technical objectivity, it becomes difficult to contest, and authority becomes less visible yet no less consequential. Algorithmic governance, in this reading, does not simply coordinate tasks; it structures the architecture of legitimate knowledge. Professional deliberation can become subordinated to outputs whose internal logic remains partially inaccessible, and this subordination is often normalised in the name of efficiency without anyone examining its implications for the humans expected to live with the consequences.

The authors identify three structural components of EWW. The first is integrity of judgement: the capacity to formulate, sustain, and justify professional decisions on reasoned grounds, even under structural pressure, with dissent not incurring disproportionate organisational cost. The second is effective epistemic agency: thinking alone is insufficient, because organisational architecture must not systematically neutralise human judgement in favour of automated outputs. Agency is effective only when professional reasoning can interrogate knowledge claims and meaningfully influence the final decision. The third is recognised epistemic responsibility: actors must be able to answer for their decisions in terms of reasons, not solely in terms of metric compliance. None of these components is a psychological trait. They are structural properties, and their erosion can be gradual, silent, and invisible to conventional satisfaction surveys.

Indeed, one of the paper’s most striking claims is that the erosion of EWW may coexist with high levels of affective well-being. The warning signs are not stress or dissatisfaction but increasing epistemic dependence on automated recommendations, reduced professional disagreement due to the structural costs of contestation, uncritical internalisation of metrics where compliance replaces deliberation, and displacement of responsibility, with decisions attributed to systems rather than accountable actors. These dynamics are frequently framed as efficiency, alignment, or successful technological adaptation. From a structural perspective, however, they may signal the weakening of the epistemic infrastructure that sustains professional dignity and responsible judgement. An organisation may run credible programmes to reduce burnout while leaving intact a decision architecture in which system outputs are presumptively correct and human overrides are penalised.

To connect these ideas to broader questions of sustainability, the authors propose the Human Sustainability and Epistemic Well-being Framework, an integrative architecture linking four nodes: organisational algorithmic governance, human dignity at work, EWW, and sustainable organisational well-being. The framework advances a conditional logic rather than a validated causal chain. Algorithmic governance configures the decision environment; EWW determines whether responsible judgement remains structurally viable; human dignity depends on that viability, because dignity requires being recognised as a reasoning agent capable of deliberation and accountability; and sustainable organisational well-being becomes more plausible when these conditions align. The authors are careful to note that the framework does not claim technology is inherently detrimental, that human judgement is invariably superior, or that EWW alone secures every dimension of well-being. Human decisions can be biased and inconsistent, and intelligent systems may improve accuracy and reveal errors. The relevant question is not human versus machine but whether their interaction is governed through contestable knowledge claims and aligned responsibility.

The practical implications reach the level of decision architecture rather than another wellness intervention. The authors argue that consequential AI-mediated recommendations should remain open to reasoned review through explicit override, appeal, and escalation routes, and that a formal right to speak is insufficient unless an alternative judgement can actually affect the outcome without triggering disproportionate penalties. Metrics should function as evidence rather than substitutes for judgement, with performance systems recording justified deviations and evaluating the quality of reasoning rather than treating deviation itself as error. Traceability should be calibrated to role and consequence, giving workers actionable intelligibility about inputs, assumptions, uncertainty, and limits, rather than demanding complete technical transparency in every case. And where organisations require humans to sign off on AI-assisted decisions, they must provide the corresponding capacity to interrogate and revise those outputs, because accountability without epistemic authority creates a structurally incoherent arrangement in which responsibility is retained while judgement is displaced.

The paper is explicitly conceptual, and the authors acknowledge its limits: no empirical evidence yet shows that EWW predicts dignity, legitimacy, or sustainable well-being, and the construct will require qualitative research, scale development, and longitudinal designs to establish that it is distinct from autonomy, psychological safety, and justice. They also caution that EWW may be experienced unevenly across hierarchy, occupation, and employment status, and that cross-national comparison, including in emerging economies and Global South contexts, is essential to prevent the framework from assuming highly regulated settings as the default. Even so, the contribution lands at a moment when algorithmic systems increasingly participate in cross-border decision-making, performance evaluation, and resource allocation, often without explicit deliberation about their implications. The paper’s closing question deserves to travel far beyond organisational theory: when we evaluate AI-mediated work, we should ask not only how efficiently decisions are made, but whether those expected to answer for them retain the authority and conditions to judge.

Subject of Research: Epistemic well-being at work under algorithmic governance and AI-mediated decision-making

Article Title: Beyond efficiency: epistemic well-being at work under algorithmic governance

Article References: Salazar-Altamirano, M. A., & Ravina-Ripoll, R. (2026). Beyond efficiency: epistemic well-being at work under algorithmic governance. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03351-9

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03351-9

Keywords: epistemic well-being, algorithmic governance, artificial intelligence, workplace well-being, epistemic agency, professional judgement, algorithmic management, human dignity, human sustainability, automated decision-making, organisational theory, AI & Society

The review arrives at a moment when futures literacy has moved from a specialist concern to a mainstream educational agenda. UNESCO’s call for a renewed social contract for education places anticipatory thinking at the centre of learning, and science classrooms have become key arenas where climate adaptation, energy transitions, and pandemic preparedness are debated. Yet these are exactly the settings where generative systems now mediate inquiry. A model trained to predict the most probable next token is optimised for statistical plausibility, not truth, and the review stresses that fluency and epistemic warrant are decoupled at the level of mechanism. The surface marks of authority, coherence, confidence, idiomatic command, are generated directly, whereas in human discourse they are ordinarily by-products of justificatory work that earns them.

Küçükuncular specifies six conditions under which unsupported output counts as pollution rather than ordinary error. It persists after correction, echoing misinformation research showing that false claims continue to influence reasoning even once debunked. It circulates at scale, since generation is cheap, on demand, and personalised to each query. It contaminates downstream reasoning when a fabricated causal anchor is built into scenarios and arguments. It degrades shared credibility cues, because fluency produced without warrant devalues the very signals by which competent testimony is normally recognised. It obscures provenance, sometimes simulating it through fabricated citations. And it externalises verification costs, transferring the labour of establishing warrant from the producer of a claim to its recipients.

The philosophical core of the argument draws on Miranda Fricker’s account of epistemic injustice, the wrong done to someone specifically in their capacity as a knower. Classrooms, the review notes, are normative spaces where credibility is continuously allocated through routines of questioning, assessment, and praise. A generative system introduces a standing credibility excess that resists the correction mechanisms that normally discipline human overconfidence, because its fluency is constant and its errors arrive without the social signals through which human assurance is discounted. Crucially, the author is careful about terminology: the model is not an epistemic agent with beliefs or intentions, but one component within a classroom credibility infrastructure that includes interface design, procurement policy, teacher practice, and vendor claims. Responsibility, he insists, falls on the humans and institutions that design and govern these systems.

Three mechanisms could turn pollution into injustice. The first is credibility displacement: when chatbot output is treated as neutral science while a student’s contribution is dismissed as anecdote, the classroom quietly denies that student recognition as a knower. A constructed vignette illustrates the risk, with a teacher pressed for time adopting a machine’s overstated causal story about ocean circulation while sidelining a pupil’s situated knowledge of local drainage and land use. The second mechanism is hermeneutical narrowing, in which default explanations mirroring dominant discourses crowd out non-dominant interpretive frames, rendering indigenous knowledge or community-based science less intelligible as candidates for serious consideration. The third is unequal verification labour, since checking requires time, disciplinary understanding, linguistic competence, and access to reliable sources that are unevenly distributed across learners.

The review is explicit that these are conceptual hypotheses, not demonstrated classroom effects, and it states four conditions under which harm would become injustice rather than a burden borne equally. These include differential model performance across languages and dialects, verification positions that track existing educational advantage, naturalisation of dominant framings as neutral defaults, and institutional authorisation that grants machine output standing denied to situated learner knowledge. Each condition, the author argues, is empirically checkable, through audits disaggregated by learner background, measurement of who actually performs verification, analysis of the futures repertoires classrooms make available, and document analysis of assessment policy.

Can better engineering solve the problem? Only partly, the review concedes. Retrieval augmentation, citation systems, and calibrated confidence indicators reduce the frequency of unsupported claims, but transparency features are not self-interpreting: an output accompanied by references reads as more trustworthy whether or not those sources are real, relevant, or correctly represented, and fabricated citations are a documented failure mode. Provenance cues can convert a verification problem into a verification ritual. More fundamentally, the harms identified are distributional rather than aggregate. A system that is more accurate on average may still be less accurate for the learners with the least capacity to notice, and no error-rate improvement answers questions about who is believed and who bears the cost of checking.

In response, the paper advances five pedagogical design principles offered as regulative ideas rather than tested interventions. Provenance-first inquiry treats every AI output as a claim requiring warrant, with learners annotating sources that can actually be inspected and recording unverified claims as excluded from the evidential basis of their work. Collective verification distributes checking across groups with rotating roles, source finder, evidence checker, counterexample seeker, uncertainty auditor, and a shared claim ledger, so that verification becomes visible inquiry rather than a hidden prerequisite falling on the already advantaged. Epistemic role protection designs tasks requiring contributions a system cannot legitimately supply: local measurements, community narratives, and the learner’s own construction of arguments. Uncertainty discipline teaches students to distinguish scientific uncertainty in a phenomenon from uncertainty arising from unreliable generation, since a model may hedge a well-established claim and assert a fabricated one with equal poise. Plural futures safeguards require competing narratives developed in parallel, each held to standards of evidence, causal coherence, and explicit value assumptions.

The deeper stakes extend beyond any single lesson. The review warns that a system trained on the aggregated text of the past carries its own implicit imaginary of which futures are normal and which are marginal, dispensing that imaginary through ordinary acts of explanation. Sardar’s classic critique of colonising the future, in which one trajectory is presented as universal progress while others are cast as lagging or irrational, acquires a new technological vehicle. Whether generative AI narrows or broadens the range of futures learners can imagine, the author concludes, is a question about pedagogical and institutional design rather than about the technology as such. The research agenda he proposes, from discourse studies of credibility moves in classrooms to intervention trials of the five principles, aims to ensure that where fluent output tends toward the future that is easiest to say, teaching and governance hold open the plurality that inquiry requires.

Subject of Research: The role of AI hallucination as epistemic pollution and epistemic injustice in futures-oriented science education

Article Title: Hallucination as epistemic pollution: epistemic injustice and credibility in futures-oriented science education

Article References: Küçükuncular, A. (2026). Hallucination as epistemic pollution: epistemic injustice and credibility in futures-oriented science education. Discover Artificial Intelligence, 6(1), Article 1229. https://doi.org/10.1007/s44163-026-02318-5

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02318-5

Keywords: hallucination, epistemic injustice, epistemic pollution, generative AI, large language models, futures literacy, science education, credibility, epistemic agency, verification labour, UNESCO, pedagogy