Task Expansion with Generative AI: Experimental Evidence on Middle-Skilled, Early-Career Workers

Hanna Brosch, Christina Langer (), Philipp Lergetporer, Sandra Mueller, Harald Pfeifer and Nele Tschoepe

No 13045, CESifo Working Paper Series from CESifo

Abstract: Does generative artificial intelligence (AI) expand the set of tasks that middle-skilled, early-career workers can successfully perform? In a field experiment with 673 final-year IT apprentices at twelve German vocational schools, we randomly assign access to AI for completing occupation-specific tasks. To identify task expansion, we study tasks both within the apprenticeship curriculum and deliberately beyond it. AI raises productivity substantially across all task types, with gains tending to be larger on tasks where performance without AI is lowest. Crucially, productivity increases by 26–31 percentage points on tasks beyond apprentices’ formal training, providing direct evidence of AI-enabled task expansion. These productivity gains do not come at the cost of reduced immediate task comprehension. Treatment effects are generally larger among apprentices with above-median AI literacy.

Keywords: generative artificial intelligence; field experiment; productivity; task expansion; middle-skilled workers; apprenticeships (search for similar items in EconPapers)
JEL-codes: C93 I21 J24 O33 (search for similar items in EconPapers)
Date: 2026
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