TY  - RPRT
        AU  - Brosch, Hanna
AU  - Langer, Christina
AU  - Lergetporer, Philipp
AU  - Mueller, Sandra
AU  - Pfeifer, Harald
AU  - Tschoepe, Nele
        TI  - Task Expansion with Generative AI: Experimental Evidence on Middle-Skilled, Early-Career Workers
        PY  - 2026/Oct/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 18999
        UR  - https://www.iza.org/publications/dp18999
        AB  - 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.
        KW  - generative artificial intelligence
KW  - field experiment
KW  - productivity
KW  - task expansion
KW  - middle-skilled workers
KW  - apprenticeships
        ER  -