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Using AI to Generate Option C Scaling Ideas: A Case Study in Early Education

Author

Listed:
  • Faith Fatchen
  • John A. List
  • Francesca Pagnotta

Abstract

In recent years, field experiments have reshaped policy worldwide, but scaling ideas remains a thorny challenge. Perhaps the most important issue facing policymakers today is deciding which ideas to scale. One approach to attenuate this information problem is to augment traditional A/B experimental designs to address questions of scalability from the beginning. List 2024 denotes this approach as “Option C” thinking. Using early education as a case study, we show how AI can overcome a critical barrier in Option C thinking – generating viable options for scaling experimentation. By integrating AI-driven insights, this approach strengthens the link between controlled trials and large-scale implementation, ensuring the production of policy-based evidence for effective decision-making.

Suggested Citation

  • Faith Fatchen & John A. List & Francesca Pagnotta, 2025. "Using AI to Generate Option C Scaling Ideas: A Case Study in Early Education," NBER Working Papers 33924, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:33924
    Note: EEE PE
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    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • C99 - Mathematical and Quantitative Methods - - Design of Experiments - - - Other

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