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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. 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).
    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. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
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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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