Generative Artificial Intelligence and the Future of Human Expertise: A Narrative Review of Cognitive Offloading, Deskilling, Learning, and Capability Renewal
Kwan Hong TAN *
School of Business, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore.
*Author to whom correspondence should be addressed.
Abstract
Aims: This chapter reviews how generative artificial intelligence (GenAI) is changing the relationship between immediate task performance and the long-term development of human expertise across workplace and educational settings. It integrates evidence on cognitive offloading, automation, learning science, professional skill formation and human-AI collaboration to examine when AI assistance augments capability and when it risks weakening it.
Study Design: Traditional narrative review.
Methodology: The review synthesises peer-reviewed literature and formally accepted articles using a traditional narrative approach. Targeted scholarly searches, searches of publisher and journal platforms, and citation chaining were used to integrate foundational studies of external cognition, automation and expertise with recent field experiments and observational studies of GenAI in work and education. The analysis is organised around mechanisms rather than a formal systematic-review protocol.
Results: Evidence consistently shows substantial short-run productivity benefits from GenAI, often strongest for less experienced users. For example, a three-company randomised study involving 4,867 software developers reported a 26.08% increase in completed tasks with AI assistance, while a high-school mathematics experiment found that unguarded GPT-4 access could improve practice performance yet reduce subsequent unaided performance. Assisted performance and independent competence are therefore not equivalent outcomes. Risks arise when AI replaces effortful retrieval, self-explanation, error diagnosis, independent judgement and participation in apprenticeship pathways. Conversely, carefully scaffolded tutoring, active collaboration, partial automation, deliberate practice and AI-generated simulations can strengthen learning.
Conclusion: The central policy and design problem is not whether cognition should be offloaded, but which cognitive activities may be safely delegated, at what stage of learning, and with what mechanisms for preserving calibration, transfer and recovery. Organisations and educators should evaluate AI systems against dual objectives: present performance and future unaided capability.
Keywords: Generative artificial intelligence, cognitive offloading, expertise, deskilling, learning, human-AI collaboration, capability renewal