Outlet Title

International Journal of Science, Engineering and Technology

Document Type

Article

Publication Date

Summer 7-15-2026

Abstract

AI copilots are increasingly embedded in knowledge-intensive work environments, yet their long-term consequences for human expertise development remain inadequately theorized and empirically underexplored. This study introduces the concept of human-AI skill drift — defined as the gradual, often imperceptible change in worker cognitive capability and domain expertise resulting from sustained reliance on AI-assisted task performance. Drawing on a six-month longitudinal field experiment spanning 225 participants across three knowledge work contexts (consulting analysis, software development, and academic writing), employees were randomly assigned to five levels of AI copilot support: no assistance (control), minimal, balanced, full, and adaptive. The study measures productivity, output quality, independent task performance, error detection, learning retention, metacognitive accuracy, and AI over-reliance across three measurement waves. Analysis of covariance reveals that full AI support conditions produce the largest short-term productivity and quality gains (η²p = 0.46 and 0.41, respectively) but simultaneously generate the most severe skill drift (SDI = –0.61, p < .001) and the sharpest decline in independent task performance, error detection, and learning retention. Critically, the adaptive AI condition — in which AI assistance intensity is dynamically calibrated to real-time skill signals — achieves near-equivalent productivity and quality outcomes while preserving independent skill profiles comparable to minimal-assistance conditions. Qualitative analysis of 36 participant interviews yields five themes illuminating the experiential mechanisms of skill drift, including invisible skill erosion, calibration failure, and accountability diffusion. The study contributes a validated Skill Drift Index (SDI), an AI-mediated learning theory, and a Responsible Augmentation Design Framework to the human-computer interaction and future-of-work literatures.

Download

Share

COinS