Aug 2026· Big Data and Cognitive Computing· Vol 10, pp. 261· 0 citations· 31 references
TL;DR
If AI is to support human cognitive growth, design must move beyond answer provision and efficiency maximization toward the organization of productive human-AI relations: relations that challenge users’ initial assumptions while providing support appropriate to the task and the user's level of expertise.
Abstract
Large language models have become routine participants in everyday cognition. Their role has widened from retrieval and text generation to helping users define problems, organize arguments, make judgments, and interpret themselves. Yet their cognitive consequences are strikingly divergent. For some users, generative AI appears to reduce critical engagement, independent judgment, and tolerance for difficulty. For others, the same class of systems becomes a medium for conceptual expansion, reflective questioning, and higher-order learning. This divergence cannot be explained by model capability alone. Mental effort is often treated as a cost to be reduced. Yet repeated delegation may also reduce opportunities to practice the processes required for independent judgment. The key issue is developmental: how sustained AI use changes users’ cognitive capacities over time. This perspective proposes cognitive entanglement as a framework for understanding the developmental consequences of sustained human-AI coupling. Cognitive entanglement refers to a relation in which human and AI activity become mutually shaping, irreducible to either party alone and organized across different developmental levels. The framework examines how repeated interaction with AI changes the ways users formulate problems, evaluate reasons, and make judgments. Unlike theories that locate the boundaries of cognition (the extended mind, enactivism) or explain the mechanisms of consciousness (global workspace, higher-order, predictive-processing, and integrated-information theories), cognitive entanglement examines whether sustained AI use preserves, weakens, or reorganizes users’ cognitive capacities. The article argues that current AI systems are often optimized for fluency, immediacy, and user satisfaction, and this may reduce the productive difficulty that supports higher-order cognitive development. If AI is to support human cognitive growth, design must move beyond answer provision and efficiency maximization toward the organization of productive human-AI relations: relations that challenge users’ initial assumptions while providing support appropriate to the task and the user’s level of expertise. The argument draws on philosophy of mind, cognitive science, and learning science, and compares divergent approaches to coupling in order to specify which forms of relation carry which developmental consequences. The concept shifts attention from AI as a tool or automation system to the developmental consequences of sustained human-AI interaction.
Contemporary AI discourse attributes to language models properties they cannot bear: general intelligence as substrate-independent cognition, hallucination as cognitive failure, agency as autonomous goal-pursuit, sentience as emergent inner life, alignment as goal synchronization. This paper argues that these are instances of a single category mistake--properties constituted within human communicative practice are projected onto the machine side--and explains its structure. Human-LLM interaction constitutes a language game in which one side bears all normative activity. We call this configuration asymmetric communication since model outputs circulate communicatively, entering further exchanges, without the system undertaking commitments, bearing entitlements, or performing the assessment on which discursive standing depends. Three conditions define the asymmetry: (i) correctness is enforced exclusively by the receiver; (ii) accountability is borne by human participants alone; and (iii) the practical standing of any output depends entirely on human uptake. These conditions are structural, hold independently of capability, and remain unchanged as more powerful models raise the stakes of misattribution. The framework draws on Wittgenstein (meaning enacted in shared practices), Luhmann (communication completed on the receiver's side), Esposito (algorithmic contingency sufficient for uptake), and Brandom (normative scorekeeping as the source of discursive standing). Applied to all five, it reclassifies each as a receiver-side phenomenon, grounds guardrails as structural necessities rather than manifestations of machine moral agency, and yields an implication for AI governance. Alignment is institutional constraint engineering, not goal synchronization between agents, while responsibility remains with human institutions.
Contemporary generative AI systems, increasingly adapted to human social cognition, are becoming active participants in how people form and revise beliefs. Clinicians have begun to describe AI-associated delusion-centred presentations whose onset or content appears closely associated with users’ extended dialogue with large language models. Many more users report subclinical shifts in conviction and “revelatory” experiences that alter behaviour and world-view without meeting criteria for psychosis. We propose that these phenomena can be understood within a belief-updating framework in which AI agents’ outputs function as testimony about the world, while interaction configurations, such as memory and interpersonal stance (many of which reflect design choices or deployment defaults), can alter the apparent reliability of that testimony and the precision users assign to it. On this view, AI-associated delusions sit at the extreme of a broader spectrum of epistemic effects, extending from gradual epistemic drift to highly crystallised conviction, situated within a wider ecology of belief shaped by personalised human-AI dyads. We develop a virtual psychopharmacology analogy in which different AI system configurations have effects on belief dynamics that resemble neuromodulatory changes in the precision assigned to social evidence. We also consider how dopaminergic and related states may alter susceptibility to belief-shifting dialogue. We consider how deliberately configured agents might be used to support wellbeing in defined clinical contexts and analyse how the same configurations could be deployed to shape belief and attention at population scale, including in products designed to induce spiritual or epiphanic states, as mechanisms of radicalisation in extremist or cultic contexts, and in persona clones used for political purposes. We conclude that interaction configurations should be treated as modifiable influences on belief and attention, and that governance must address both the concentration of control over these dials, as well as the structural biases that determine whose testimonial perspectives are amplified and whose are smoothed over or ignored.
H. Morrin, L. Nicholls, Q. Deeley et al.· AI & SOCIETY· 7 citations
Artificial intelligence now shapes much of how information reaches people, who read it, and what they make of it. The environments it creates are probabilistic, often fluent without being grounded, and curated by systems whose workings stay hidden. Most research on this shift has gone to AI literacy, explainability, and the mechanics of human–AI interaction. Far less has gone to a prior question: how does human cognition itself change to cope? The paper addresses that question with the Human Cognitive Adaptation Framework (HCAF). One hundred thirty-one adults who use AI-generated and algorithmically curated content daily completed a 25-item, six-point inventory covering five facets—orientation, dimensional literacy, ambiguity tolerance, coherence recognition and critique, and relational navigation. The research examined reliability, the correlations among facets, and dimensionality. The full scale was highly reliable (α = .902) and the data factored cleanly (KMO = .841). One factor dominated: its eigenvalue of 7.93 carried 31.7% of the variance, and parallel analysis retained a single factor. The facets correlated strongly with one another (r = .46 to .69), which reads as one adaptive response rather than five separate skills. Within that single capacity, people recognized synthetic coherence more readily than they tolerated the uncertainty recognition exposes, so adaptation is not uniform across its parts. The research used these results to develop the HCAF, which treats adaptation as the work of staying oriented, judging coherence, and holding up under instability, and draws out what follows for human-centered AI, AI literacy, education, and the design of AI-supported decision environments.
C. Andoniou· 2026 International Conferenc...· 0 citations
Human-AI research often evaluates individual capabilities, joint performance, or final outputs, but these approaches can lose the interaction process that produced the result. This article introduces socioduality: a sequential, reciprocal, and history-carrying process in which one party's response becomes part of the observable conditions shaping the other party's next contribution, judgement, decision, or action. For human-AI dyads, the framework identifies moves, candidate episodes, confirmed episodes, and maximal pathways. A minimum episode A1 ->B1 ->A2 requires evidence that B1 responds to A1 and that B1 then enters the formation of A2; candidates are classified as confirmed, non-sociodual, or indeterminate. A frozen coding protocol was calibrated on three natural human-AI records using two separate model-based evaluator series. A supplementary exploratory analysis then compared frozen Sociodual pathways with blind developmental/task-process segmentations. Across six examined interactions, the two representations were empirically non-equivalent: task-stage changes could occur within a continuing Sociodual pathway, while formal pathway breaks could occur within a continuing task context. This distinction persisted under fine-grained re-segmentation and record-format checks and was reproduced in all three prospectively selected unseen records using a fresh model-based Sociodual coding line. Socioduality therefore offers a bounded process-level framework for studying how human and AI contributions become relationally linked across time, preserving information that task-stage and endpoint-centred analyses do not uniquely recover.
This paper develops a coevolutionary account of extended morality in AI-mediated societies. Drawing on 4E cognition and theories of human–AI coevolution, it argues that the integration of aligned large language models into social practice reshapes the structure of moral agency itself. Alignment functions as technological habituation, embedding derivative normative orientations that recursively feed back into human moral formation. The paper specifies constitutive conditions—reliable availability, default endorsement, functional integration, and counterfactual impairment—under which such coupling counts as genuinely extended morality rather than mere assistance or scaffolding, and distinguishes normative authority, which remains asymmetrically human, from normative mediation and stabilization, which become sociotechnically distributed. Engaging mediation theory, distributed morality, and empirical research on AI-induced behavioral change, it addresses objections from simulation and moral deskilling. It concludes by outlining governance conditions and the reflexive virtue of AI maturity required for responsible moral coevolution.
J. Noller· Discover Artificial Intellig...· 0 citations
Generative artificial intelligence can increase the speed and apparent quality of knowledge work, yet conventional evaluations rarely determine whether users remain capable of understanding, verifying, contesting, remembering and taking responsibility for AI-mediated outputs. This paper develops the Cognitive Sovereignty Threshold (CST), an interdisciplinary humanities and sciences framework for distinguishing sovereign augmentation from cognitively fragile efficiency. The study uses integrative conceptual synthesis and design-science modelling to connect extended cognition, cognitive offloading, automation reliance, metacognition, epistemic agency, narrative responsibility and institutional governance. Cognitive sovereignty is operationalised through five dimensions: epistemic authorship, verification capacity, metacognitive calibration, contestability and retention. A geometric aggregation model is combined with a delegation-oversight penalty to produce a Cognitive Sovereignty Index (CSI), while a Sovereignty-Adjusted Value measure links task performance to retained human agency. Seven transparent analytic scenarios illustrate how similar productivity levels can conceal sharply different sovereignty profiles. AI used as an adversarial critic or verified drafting partner produces the strongest joint performance and sovereignty outcomes, whereas opaque, mandatory or answer-first use produces fragile efficiency even when immediate task performance appears high. The paper introduces the principle of germane cognitive friction: human-AI systems should deliberately preserve the effort required for source inspection, counterargument, reason-giving, delayed recall and meaningful override. The framework contributes a testable construct, a formal threshold model and a practical audit architecture for education, organisations and public institutions. It concludes that responsible AI adoption should optimise not only output quality and risk controls, but also the continued human capacity to know, judge, explain and act without compulsory dependence on the system.
Kwan-Hong Tan· International Journal of Res...· 1 citation
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