Deskilling and upskilling with AI systems

Introduction. Deskilling is a long-standing prediction of the use of information technology, raised anew by the increased capabilities of AI (AI) systems. A review of studies of AI applications suggests that deskilling (or levelling of ability) is a common outcome, but systems can also require new skills, i.e., upskilling. Method. To identify which settings are more likely to yield deskilling vs. upskilling, we propose a model of a human interacting with an AI system for a task. The model highlights the possibility for a worker to develop and exhibit (or not) skills in prompting for, and evaluation and editing of system output, thus yielding upskilling or deskilling. Findings. We illustrate these model-predicted effects on work with examples of current studies of AI-based systems. Conclusions. We discuss organizational implications of systems that deskill or upskill workers and suggest future research directions.