Long-term cognitive and skill impacts of pervasive AI assistance

Ascertain the long-term effects of widespread AI assistance and integration of generative AI tools on human learning, skill acquisition, and retention across educational and professional settings, particularly in contexts where individuals offload complex cognitive tasks to AI.

Background

The discussion highlights concerns that pervasive AI support and automation bias may alter how people learn and make decisions, drawing analogies to documented effects of digital tools such as GPS on spatial knowledge acquisition. The paper emphasizes the importance of maintaining independence between human and AI judgments to mitigate potential deskilling.

Understanding long-term impacts requires longitudinal and domain-specific evaluation of how AI usage patterns influence expertise development, error detection, and cognitive resilience, given the increasing integration of AI into everyday and professional workflows.

References

While the long-term effects of this phenomenon remain unclear, there is a growing concern about deskilling and learning in the age of AI \citep{natali2025ai, choudhury2024large, lee2025impact}.

— Beyond AI advice -- independent aggregation boosts human-AI accuracy  (2603.29866 - Berger et al., 31 Mar 2026) in Discussion

A central question becomes whether AI systems strengthen human capacity over time or quietly replace the effort through which that capacity is built.

— From Substitution to Scaffolding: Breaking the Self-Reinforcing Harm Cycle of AI in Education (and Beyond)  (2608.17451 - Favero et al., 18 Aug 2026) in Section 7, “Toward AI That Scaffolds”

Equally, the data cannot determine whether the No AI group retained more because they spent longer with the material. We therefore present time on task as a descriptive marker of the offloading pattern, consistent with the mental effort and retention results, and leave its causal separation from engagement depth to future work (\autoref{sec:limitations}).

— Your Programming Students' Cognition with ChatGPT: Higher Performance, Lower Retention, and Reduced Ownership  (2609.21194 - Bergh et al., 18 Sep 2026) in Section 4, Discussion, subsection “Interpreting Reduced Cognitive Load”

And what, if anything, do platforms owe the workers whose skills their task structures wear down?

— From Producing to Validating: How AI Is Deskilling Freelancers  (2608.26089 - Rajpal, 26 Aug 2026) in Section “Implications and Discussion Points”

Although the results suggest that trace-based support improves revision performance and usability, we do not yet know whether these benefits translate into durable learning or long-term skill development.

— TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking  (2608.25462 - Sun et al., 26 Aug 2026) in Section 6, Limitations and Future Directions

For HCI researchers and designers: how can we shape the platforms and task structures of gig work to preserve skill development as AI takes over production?

— From Producing to Validating: How AI Is Deskilling Freelancers  (2608.26089 - Rajpal, 26 Aug 2026) in Section “Implications and Discussion Points”

We also do not yet know how prolonged use of proactive recovery support would affect learning, trust calibration, or dependence on assistance over time.

— RegulAR: Graph-Grounded Error Recognition and Assistance for Procedural Tasks in AR  (2608.26715 - Ye et al., 27 Aug 2026) in Section 6, Limitations and Future Work

For example, it remains unknown whether practitioners who currently absorb'' the loss of generative joy will eventually experience long-term burnout or attrition, or whether the fearedskill atrophy'' will actually materialize and impact code quality over a 3-to-5-year period.

— The Psychological Costs of Artificial Intelligence Adoption in Software Engineering  (2609.03456 - Alami et al., 3 Sep 2026) in Section 8, “Limitations and Trade-offs”

If decision makers repeatedly accept polished outputs without comparing their grounds with subsequent evidence, the capacity to evaluate them may weaken. Organisational-learning theory identifies why substitution can change how organisations learn, rather than merely how quickly they process a task (Balasubramanian et al., 2022). Longitudinal effects remain a research question for this model.

— Executive Judgement in AI-Mediated Decision-Making Environments: A Process Theory of Formation, Qualification Attrition and Authorisation  (2609.39442 - Hill, 30 Sep 2026) in Section 5.3, “Reliance calibration”