Is AI making us stupid?

Trent N. Cash, Megan O. Kelly, Brooke N. Macnamara, Evan F. Risko (2026) — Trends in Cognitive Sciences (Cell Press), Science & Society. Available online 9 July 2026. doi:10.1016/j.tics.2026.06.004.

Summary

A 3-page perspective (opinion/review, not an empirical study) addressing whether AI use erodes human cognition. The authors' answer: not inherently — but the risk is real and follows the cognitive-psychology principle of cognitive offloading. When people delegate reasoning, writing, memory, or problem-solving to AI, they forgo the mental practice that builds and maintains those capacities. The threat is use-dependent, not intrinsic to the technology: AI that augments thinking preserves the underlying processes; AI that replaces them outsources exactly the practice that builds expertise.

Core distinction: skills vs. basic cognitive abilities

The article's organizing framework separates two facets of our cognitive systems:

Skills — learned, domain-specific behaviors supported by knowledge (arithmetic, flying, diagnosis, writing, programming). Acquired and maintained through practice [4]. Offloading practice to AI "will almost certainly compromise skill acquisition" [5] and can cause skill decay [5,7].

Basic cognitive abilities — foundational, domain-general capacities (working memory, selective attention) that underlie skills. May be more resilient to erosion: cognitive-training research shows gains are highly task-specific rather than broad [9], so basic abilities appear "stubbornly resistant to substantial change." Open question whether long-term or developmental-stage offloading could still shift them [11,12].

Evidence cited

Math learning (high school) [6]: Students with an AI that let them fully offload solving practice problems scored higher on practice but performed worse on a later no-AI test than students who never had AI. A third condition — a custom AI tutor that probed knowledge and filled gaps (rather than giving answers) — performed no worse than no-AI students, indicating the targeted skill was still acquired.

Endoscopy skill decay [8]: After an AI detection tool was introduced, adenoma detection rates fell from 28.4% → 22.4% in cases where the AI was unavailable — evidence of deskilling when the tool was withdrawn.

Knowledge acquisition (Box 1) [2,3]: Offloading to an external store during learning reduces later retrieval; an AI-summary study found advice was "briefer, less unique, and rated less helpful," with learners spending less time and feeling less ownership — shallower depth of learning than web search.

The "how we use AI matters" argument

Whether skills survive offloading depends on the form the offloading takes [13]:

Completely offloading the task ("student has the AI submit the answer") → harm.

Having the AI provide an explanation, a suggestion, or act as a collaborator giving feedback [14], or emulating a thoughtful tutor [15] → can preserve or even boost skill despite reduced effort ("Coach not crutch" [15]).

Staying "in the proverbial cognitive loop" mitigates costs; costs are likely limited to the specific skills offloaded, not basic abilities.

Concluding remarks & open questions (Box 2)

"It is far too early to say with certainty" the long-term effects. Open questions: prolonged offloading over years/decades; developmental-stage effects (children?); decay as a function of initial skill level and skill type; refresher-training strategies; designing AI to discourage harmful offloading (policy levers); whether people can learn to strategically offload; impacts on metacognition and source-monitoring (misattributing AI output as one's own); effects on dispositions toward thinking.

Notably, the article cites the PNAS guardrails paper (Bastani et al. 2025, generative-ai-guardrails-harm-learning) and the "Coach not crutch" preprint (Lira et al. 2025) — making the offloading/crutch mechanism a shared thread across the wiki.

Why this matters for the wiki

The canonical cognitive-offloading citation cognitive-offloading needed; this perspective synthesizes the mechanism and the skills-vs-basic-abilities resilience distinction.

Conceptual bookend to the empirical RCTs already in the wiki: generative-ai-guardrails-harm-learning (unguarded tutor cut exam scores via crutch/offloading), generative-ai-reduced-study-time-math (population "cognitive surrender"), and contrasts with ai-generated-feedback-higher-ed (well-architected AI feedback matched teachers — the "coach not crutch" design).

Reinforces over-reliance, metacognition (source-monitoring), self-regulated-learning (deliberate vs. passive offloading), and ai-literacy (using AI to augment, not replace).

cognitive-offloading — The core mechanism; this article is the key anchor

generative-ai-guardrails-harm-learning — PNAS RCT cited by this paper; unguarded AI tutoring harms learning via offloading

generative-ai-reduced-study-time-math — Population-scale "cognitive surrender" evidence

cognitive-offloading-speedup-illusion — Perceptual bias driving passive offloading

efficiency-gain-illusion-ai-overreliance — Why users underestimate offloading costs

ai-generated-feedback-higher-ed — Contrast: well-architected AI feedback matched teachers ("coach not crutch")

over-reliance — Passive dependency as the downstream risk

metacognition — Source-monitoring / misattribution of AI output

self-regulated-learning — Strategic vs. passive offloading

ai-literacy — Using AI to augment rather than replace thinking

genai-performance-vs-learning — Performance gains vs. durable learning

Citation

APA: Cash, T. N., Kelly, M. O., Macnamara, B. N., & Risko, E. F. (2026). Is AI making us stupid? Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2026.06.004