Aug 2026· Library Hi Tech News· 0 citations· 7 references
TL;DR
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.
Abstract
This study aims to explore how generative AI is transforming existing knowledge models (as evidenced through Ackoff’s DIKW [Data–Information–Knowledge–Wisdom] pyramid) and how it leads to cognitive offloading in users, with the potential to erode critical thinking as a vestigial human faculty. It also makes the case for an epistemic filter between information and knowledge, one that can refine raw material information into authentic knowledge rather than letting AI-mediated shortcuts collapse that distinction.
This study adopts a conceptual and theoretical approach, using Ackoff’s DIKW pyramid as the primary analytical framework, rather than relying on empirical data collection (surveys, experiments or interviews).
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. We need to put the emphasis back on human cognition, active evaluation, reasoning and judgement as the necessary mechanisms for converting filtered information into real knowledge, and ultimately wisdom, rather than accepting auto-generated results as facts.
This study’s originality lies in applying Ackoff’s DIKW pyramid, an established but static framework, to the emerging problem of generative AI-driven cognitive offloading, and in proposing the “epistemic filter” as a new conceptual layer between information and knowledge, one absent from Ackoff’s original model, aimed at preserving human cognition over passive reliance on AI-generated output.
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.
Jing Su, Zhuo Wang, Zhen Qiang et al.· Journal of Intelligence· 0 citations
Psychological wisdom research has shifted from characterizing rare exemplars and desired outcomes to specifying processes that support sound judgment under uncertainty. Yet it has advanced along two siloed research tracks: one on folk theories-cultural tools such as exemplars, narratives, heuristics (including proverbs and maxims), and standards of judgment-and the other on the mechanisms involved in wise judgment. This review bridges these research tracks using a situated metacognitive lens: Folk theories provide candidate attributes or strategies for action, whereas perspectival metacognition-the capacity to recognize epistemic limits, coordinate viewpoints, and track uncertainty and change-regulates their context-sensitive selection and use. We synthesize evidence connecting wisdom-related processes to emotional balance, relational well-being, cooperation, and reduced polarization, while noting boundary conditions. We show how this synthesis sharpens measurement trade-offs, highlighting the limits of global self-report and the advances in situated assessment. Finally, we summarize work on wisdom development and cultivation and consider the socioecological implications of wisdom research in an AI-shaped world.
Igor Grossmann, Nic M. Weststrate· Annual Review of Psychology· 0 citations
The proposition that agentic artificial intelligence may precipitate a depletion of collective cognitive capital has circulated with unusual velocity in both scholarly and public discourse. The present paper offers a deliberately heterodox reading of the dynamic model advanced by Acemoglu, Kong and Ozdaglar (2026). Rather than reconstructing the formal apparatus or replicating its notation, we reposition the argument within three underutilized scholarly streams: the cognitive ergonomics of human-machine collaboration, the institutional ecology of knowledge stewardship, and the developmental psychology of novice expertise formation. We introduce a phase-space taxonomy that maps commons trajectories as functions of effort elasticity and knowledge complementarity, and we advance a governance typology calibrated to distinct cognitive levels - declarative, procedural, causal, and metacognitive. Drawing upon recent experimental evidence on neural offloading (Kosmyna et al., 2025), educational neuroscience (Lodge and Loble, 2026), and critical-thinking erosion under AI assistance, we argue that the collapse narrative, while theoretically coherent, overstates uniformity and understates adaptive capacity. The paper supplies a governance matrix organized by cognitive level and institutional lever. We conclude that the salient policy challenge is not the prevention of an inevitable collapse but the design of polycentric stewardship regimes that render the commons robust to heterogeneity in human responsiveness.
The paper argues that practical imitation may bypass this barrier by relying on belief and perceived equivalence rather than authentic internalization rather than authentic internalization, and may help ensure that AI remains an auxiliary tool rather than becoming a governing influence over human thought and action.
It is argued that intelligence should be assessed not by output quality or efficiency alone, but by its impact on intellectual character, arguing that intelligence should be assessed not by output quality or efficiency alone, but by its impact on intellectual character.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
