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IZA Discussion Paper No. 18999
October 2026
Task Expansion with Generative AI: Experimental Evidence on Middle-Skilled, Early-Career Workers

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.

Communications
Mark Fallak
mark.fallak@liser.lu
+352 585-855-526
World of Labour
Olga Nottmeyer
olga.nottmeyer-ext@liser.lu
+352 585-855-501
Network Coordination
Christina Gathmann
christina.gathmann@liser.lu

The IZA@LISER Network is a global community of scholars dedicated to excellence in labor economics and related fields, now coordinated at the Luxembourg Institute of Socio-Economic Research (LISER) following its transition from Bonn.

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