When AI Reshapes Learning

Abstract

Artificial intelligence (AI) is embedded in the work of junior professionals in Knowledge-Intensive Services (KIS), where learning occurs through performing cognitive tasks, making mistakes, and receiving feedback. While AI is discussed in terms of efficiency, its implications for early-career learning remain less clear. This study examines how AI use intensity relates to perceived professional development, and whether this relationship can be understood through job demands and job control. Drawing on the Job Demands-Control model and experiential learning theory, it was expected that higher AI use would reduce job demands and job control, and that professional development would be strongest when both were high. A quantitative cross-sectional survey was conducted among 106 junior professionals in KIS sectors. The hypotheses were tested using regression and moderation analyses. The results showed no significant direct relationship between AI use intensity and job demands, job control, or perceived professional development. However, job demands and job control interacted significantly. Contrary to expectations, job demands were positively associated with perceived professional development when job control was low or average, but not when job control was high. The findings suggest that AI does not automatically harm junior development, but that learning depends on how AI-integrated work is designed.