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

Faith Fatchen, John List and Francesca Pagnotta

No 33924, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C9 C90 C91 C92 C93 C99 (search for similar items in EconPapers)
Date: 2025-06
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