Aug 2026· Journal of Intelligence· Vol 14, pp. 182· 0 citations· 68 references
Medicine
TL;DR
This work surveyed pre-service teachers, operationalizing the felt loss of cognitive ownership as concern over the erosion of teaching-design subjectivity (TSC)—the metacognitive appraisal that AI-assisted work is not genuinely one’s own and that independent capacity is declining.
Abstract
Cognitive offloading—delegating cognitive work to external tools—is basic to human cognition, but generative AI (GenAI) amplifies it radically: entire cognitive products can now be produced on request. This sharpens a question about agency over one’s own thinking: when cognition is habitually offloaded, does its product still feel like one’s own? We surveyed 239 pre-service teachers, operationalizing the felt loss of cognitive ownership as concern over the erosion of teaching-design subjectivity (TSC)—the metacognitive appraisal that AI-assisted work is not genuinely one’s own and that independent capacity is declining. Guided by the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, we tested whether AI anxiety/fear of missing out (affective) and impulsivity (self-regulatory) relate to this concern through behavioral GenAI dependency—habitual offloading. In a structural equation model with bias-corrected bootstrapping, dependency strongly predicted TSC and partially mediated the effect of AI anxiety; impulsivity raised dependency but showed a suppression pattern (a positive indirect effect offset by a null total effect), and the model explained 35% of the variance in the concern. Habitual offloading—that is, GenAI dependency—rather than generic AI use, accompanies the metacognitive loss of cognitive ownership.
The results indicate the necessity of an epistemic filter in the DIKW pyramid, filtering information from its disorders (misinformation, noise, unverified or low-quality outputs) before it can be allowed to enter knowledge.
Saleeq Ahmad Dar· Library Hi Tech News· 0 citations
Artificial intelligence is being adopted in educational settings faster than its consequences are understood. We argue that the central risk is misalignment: AI that eliminates human effort erodes the very capacities education is meant to build. We organize this risk into an integrative framework of four interrelated dimensions -cognition, agency, emotional well-being, and ethics- linked by a self-reinforcing cycle where cognitive offloading reduces effort, weakens agency, and compounds emotional and ethical harm. We ground the framework in the perspective of a small cohort of students: an exploratory analysis of 49 International Baccalaureate argumentative essays about the impact of AI reveals that learners perceive these risks, with $80\%$ of essays reporting that AI reliance reduces thinking. At the same time, the essays articulate a consistent vision of the AI the students want: systems that support rather than replace learning by withholding immediate answers, prompting recall, and encouraging reflection through questions instead of solutions. These desiderata closely align with established principles from the learning sciences. Building on these insights, we propose a single design principle, scaffold, do not substitute. We argue that this principle extends beyond education. It represents a broader challenge for the AI ecosystem: any system that mediates human thinking can either weaken human capabilities through substitution or strengthen them through scaffolding. We conclude by outlining a research agenda for developing AI systems that foster enduring human capacity, an imperative not only for learners but, ultimately, for democratic societies.
Lucile Favero, J. A. Pérez-Ortiz, Tanja Käser et al.· 0 citations
Generative artificial intelligence (GenAI) has rapidly entered educational settings, yet a fundamental question remains unresolved: does GenAI enhance learning or does it improve immediate performance while reducing the cognitive activity on which durable learning depends? This Hypothesis and Theory article offers a theoretical reading of apparently contradictory findings—improved academic products alongside qualitative, neuroscientific, and behavioral signs of reduced cognitive engagement—which we term the critical-thinking paradox of GenAI-integrated learning. These contrasts may also reflect genuine heterogeneity across tasks, populations, and tools; the proposed convergence is treated as a testable interpretation, not a fact. Drawing on four theoretical traditions—levels of processing, desirable difficulties, cognitive load theory and Load Reduction Instruction, and cognitive offloading research—we propose a differentiated three-level framework that maps AI-integration strategies onto surface, intermediate, and deep cognitive processing, specifying level-appropriate AI roles, primary risks, and boundary conditions. We adopt the emerging construct of cognitive debt: a potential cumulative reduction in metacognitive calibration and unaided higher-order performance that persists beyond an AI-assisted episode. Our contribution is to distinguish episodic offloading (deliberate and task-specific) from habitual offloading (routine and weakly monitored) and to map both patterns onto the three cognitive levels. The framework generates falsifiable hypotheses, centrally that unrestricted AI use on deep-processing tasks may yield a product–process dissociation: higher-rated assignments but lower unaided delayed transfer. We specify developmental stage, prior knowledge, and metacognitive monitoring accuracy as preregistered boundary conditions and outline a research program combining confirmatory experiments, interaction telemetry, and longitudinal measurement.
J. Lin, N. M. Al-Hada· Frontiers in Psychology· 0 citations
ResumenThe mass adoption of generative artificial intelligence in learning revives a classic question: when does the support of an external tool empower thinking and when does it replace it? This conceptual article, aimed at the construction of theory through theoretical synthesis, integrates three bodies of literature, cognitive download, desirable difficulties and metacognition, to theorize the tension between delegating cognition to AI and developing self-regulation. Its distinctive contribution in the face of close frameworks is to specify boundary conditions. The threshold model of metacognitive tension is proposed, which defines a mediating mechanism, the preservation against the suppression of the diagnostic cues of monitoring and the occupation of the metalevel, three moderators, including the learner's expertise, and a threshold conceived as a transition band in the plane of the locus of discharge and metacognitive posture. It derives a typology of four uses and seven propositions that can be contrasted with their evidential status, and identifies implications for pedagogical, tool, and research design.AbstractThe widespread adoption of generative artificial intelligence in learning revives a classic question: when does support from an external tool enhance thinking, and when does it replace it? This conceptual article, oriented toward theory building through theoretical synthesis, integrates three consolidated bodies of literature, cognitive offloading, desirable difficulties, and metacognition, to theorize the tension between delegating cognition to AI and developing self-regulation. Its distinctive contribution relative to neighboring frameworks is to specify boundary conditions. It proposes the Metacognitive Tension Threshold Model, which defines a mediating mechanism, the preservation versus suppression of the diagnostic cues of monitoring and the occupation of the metalevel, three moderators, including learner expertise, and a threshold located as a transition band within a plane of two determinants, the locus of offloading and metacognitive posture. The model derives a typology of four uses and seven testable propositions with their evidential status, and identifies implications for pedagogical design, tool design, and research.
R. Guerrero-Chirinos, M. Medina-Romero, Jean‐Marc Pierre et al.· Journal of Intelligent Decis...· 1 citation
Generative AI tools such as ChatGPT now perform core cognitive operations—reasoning, synthesis, evaluation, and creative generation—on users' behalf, raising urgent questions for educational psychology about how AI use relates to cognitive development. Yet research on cognitive offloading has largely treated AI use as a unidimensional phenomenon, obscuring a theoretically consequential distinction: whether AI substitutes for the user's own thinking or scaffolds it. Drawing on the autonomous–dependent help typology and self-determination theory, the present study introduces and examines a distinction between dependent cognitive offloading (delegating core thinking to AI) and autonomous cognitive offloading (using AI as a scaffold while retaining cognitive agency). In a three-wave time-lagged survey study (N = 589 university students and early-career knowledge workers), we tested a dual-pathway model linking the two offloading modes to four perceived downstream cognitive outcomes—autonomous capability, creativity, deep processing, and independent judgment—through cognitive agency transfer and intrinsic motivation. Dependent offloading was positively associated with cognitive agency transfer and negatively associated with intrinsic motivation, which in turn were linked to poorer perceived outcomes. Autonomous offloading was positively associated with intrinsic motivation and more favorable perceived outcomes. Metacognitive monitoring attenuated the link between dependent offloading and cognitive agency transfer but did not buffer the negative motivational association. Notably, both offloading modes yielded comparable immediate benefits despite divergent downstream correlates, suggesting that potentially maladaptive AI engagement may be difficult for users to detect from immediate experience. These findings highlight the manner of AI engagement—not merely its frequency—as a key factor in understanding its associations with perceived cognitive functioning, and point to practical strategies for educators, learners, and AI tool designers seeking to harness AI without undermining cognitive autonomy.
Qiuhan Zhu, Xiangnan Li, Yiang Dong et al.· Frontiers in Psychology· 1 citation
It is found that AI dependence does not have a significant direct negative effect on innovative behavior, and inhibits innovation exclusively through a full mediation pathway by eroding employee self-efficacy, indicating that the suppression of innovation is caused not by the technology itself, but by the “deprivation of mastery experiences” that accompanies over-dependence.
Byung‐Jik Kim, Yeon-Jun Choi, Julak Lee· Humanities and Social Scienc...· 0 citations