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

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  • Faith Fatchen
  • John 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 List & Francesca Pagnotta, 2025. "Using AI to Generate Option C Scaling Ideas: A Case Study in Early Education," Framed Field Experiments 00822, The Field Experiments Website.
    • Handle: RePEc:feb:framed:00822
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    References listed on IDEAS

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    1. Glenn W. Harrison & Morten I. Lau & E. Elisabet Rutström, 2007. "Estimating Risk Attitudes in Denmark: A Field Experiment," Scandinavian Journal of Economics, Wiley Blackwell, vol. 109(2), pages 341-368, June.
    2. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
    3. Christopher S. Cotton & Brent R. Hickman & Joseph P. Price, 2022. "Affirmative Action and Human Capital Investment: Evidence from a Randomized Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 40(1), pages 157-185.
    4. Alison Andrew & Orazio P. Attanasio & Raquel Bernal & Lina Cardona Sosa & Sonya Krutikova & Marta Rubio-Codina, 2024. "Preschool Quality and Child Development," Journal of Political Economy, University of Chicago Press, vol. 132(7), pages 2304-2345.
    5. Fiorina, Morris P. & Plott, Charles R., 1978. "Committee Decisions under Majority Rule: An Experimental Study," American Political Science Review, Cambridge University Press, vol. 72(2), pages 575-598, June.
    6. Burgess, Simon & Metcalfe, Robert & Sadoff, Sally, 2021. "Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments," Economics of Education Review, Elsevier, vol. 85(C).
    7. Marco Castillo & John A. List & Ragan Petrie & Anya Samek, 2024. "Detecting Drivers of Behavior at an Early Age: Evidence from a Longitudinal Field Experiment," Journal of Political Economy, University of Chicago Press, vol. 132(12), pages 3942-3977.
    8. Gary Charness & Brian Jabarian & John A. List, 2025. "The next generation of experimental research with LLMs," Nature Human Behaviour, Nature, vol. 9(5), pages 833-835, May.
    9. Flavio Cunha & James J. Heckman, 2008. "Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    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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