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PRWP

Reproducibility package for Not Faster, But Better: Generative AI And Teacher Skill In Peru

2026
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Reference ID
RR_PER_2026_695
Author(s)
Carolina Lopez, Carla Z. Glave, Ezequiel Molina, Román Andrés Zárate
Collections
World Bank Policy Research Working Papers
Metadata
JSON
Created on
Jul 13, 2026
Last modified
Jul 21, 2026
Page views
22
Downloads
5
  • Project Description
  • Downloads
  • Overview
  • Reproducibility Package
  • Description
  • Scope and coverage
  • Disclaimer
  • Access and rights
  • Contacts
  • Information on metadata
  • Overview

    Abstract

    Generative AI is being deployed across the public sectors of developing countries on the premise that it makes workers faster. We test this in a randomized trial with 1,269 sixth-grade teachers across 390 public schools in Lima, Peru. A low-cost program—a one-day training, an AI license, and eight months of light-touch follow-up—raised adoption sharply yet saved teachers no time on any of their core tasks, with precisely estimated nulls throughout. The gains lay elsewhere: treated teachers wrote markedly better prompts, including on a rubric designed independently of the training, and were sharper at catching weak AI outputs. These gains were largest for older teachers, who began furthest behind, narrowing rather than widening skill gaps. Taken together, the results indicate that AI training primarily develops the skills needed to generate and evaluate high-quality AI outputs, rather than reducing the time teachers spend on their work.

    Reproducibility Package

    Scripts
    Readme Get Reproducibility Package
    Link: https://reproducibility.worldbank.org/catalog/608/download/1817/README.pdf
    Reproducibility package for Not Faster, But Better: Generative AI And Teacher Skill In Peru
    File name
    RR_PER_2026_695
    Zip package
    RR_PER_2026_695.zip
    Title
    Reproducibility package for Not Faster, But Better: Generative AI And Teacher Skill In Peru
    Date
    2026-07
    Dependencies
    Stata dependencies are listed in the ado folder.
    Instructions
    See README in reproducibility package.
    Notes
    Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.
    Source code repository
    Repository name URI
    Reproducible Research Repository (World Bank) https://reproducibility.worldbank.org
    Software
    Stata
    Name
    Stata
    Version
    19.5 MP

    Reproducibility

    Technology environment

    Paper exhibits were reproduced on a computer with the following specifications:
    • OS: Windows 11 Enterprise
    • Processor: INTEL(R) XEON(R) GOLD 6226R CPU @ 2.90 GHz (2 processors)
    • Memory available: 16.0 GB

    Technology requirements

    Run time: ~ 5 minutes

    Reproduction instructions

    To reproduce the findings in this paper, a replicator must:

    1. Secure Access to Data: Access the datasets not included in the package. See the Datasets section for more details.
    2. Run the Package:
    • Update the working directory in line 38 of the do-file "00_master.do", and run it.

    Since not all the data is included, the package includes the results produced by replicators. These files can be used to review the results presented in the paper.

    Data

    Datasets
    Not Faster, but Better: Generative AI and Teacher Skill in Peru 2025
    Name
    Not Faster, but Better: Generative AI and Teacher Skill in Peru 2025
    Note
    Data collected March–December 2025. Teacher-level analysis dataset combining school and teacher rosters, baseline survey, weekly Microsoft Copilot usage records, endline survey, and school-level diagnostic scores for 1,269 sixth-grade teachers across 390 public schools in Metropolitan Lima, Peru. The dataset has been de-identified; school, district, and teacher identifiers have been replaced with anonymous sequential codes. The data is currently under embargo and will become accessible once the paper is published in a peer-reviewed journal. File location: data/ai4t_merged.dta.
    Access policy
    Data is embargoed until paper publication.
    License
    Licensed
    License URL
    https://microdata.worldbank.org/terms-of-use
    Data URL
    https://microdata.worldbank.org//catalog/8514
    Citation
    Glave, C.Z., Lopez, C., Molina, E., and Zárate, R.A. 2026. "Not Faster, but Better: Generative AI and Teacher Skill in Peru" [dataset]. AEA RCT Registry: AEARCTR-0016336. Forthcoming in the World Bank Microdata Library.
    Data statement

    All data is temporarily embargoed by the authors. The data will be made available as licensed files through the World Bank Microdata Library.

    Description

    Output
    Not Faster, But Better: Generative AI And Teacher Skill In Peru
    Type
    Working Paper
    Title
    Not Faster, But Better: Generative AI And Teacher Skill In Peru
    Description
    Policy Research Working Papers (PRWP)
    Authors
    Author Affiliation Email
    Carolina Lopez World Bank carolina_lopez@worldbank.org
    Carla Z. Glave University of Wisconsin-Madison carla.glave@wisc.edu
    Ezequiel Molina World Bank molina@worldbank.org
    Román Andrés Zárate University of Toronto ra.zarate@utoronto.ca
    Date of production

    2026-07-13

    Scope and coverage

    Geographic locations
    Location Code
    Peru PER
    Keywords
    Generative Artificial Intelligence Teachers Teacher Training Randomized Controlled Trial Educational Technology Prompt Quality Primary Education Peru
    Topics
    ID Topic Parent topic ID Vocabulary Vocabulary URI
    C93 Field Experiments C9 Journal of Economic Literature (JEL)
    I21 Analysis of Education I2 Journal of Economic Literature (JEL)
    J24 Human Capital • Skills • Occupational Choice • Labor Productivity J2 Journal of Economic Literature (JEL)
    O33 Technological Change: Choices and Consequences • Diffusion Processes O3 Journal of Economic Literature (JEL)

    Disclaimer

    Disclaimer

    The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.

    Access and rights

    License
    Name URI
    MIT License https://opensource.org/license/mit
    World Bank IGO Rider https://github.com/worldbank/metadata-editor/blob/main/WB-IGO-RIDER.md

    Contacts

    Contacts
    Name Affiliation Email
    Carolina Lopez Development Research Group, World Bank carolina_lopez@worldbank.org
    Reproducibility WBG World Bank reproducibility@worldbank.org

    Information on metadata

    Producers
    Name Abbreviation Affiliation Role
    Reproducibility WBG DECDI World Bank - Development Impact Department Verification and preparation of metadata
    Date of Production

    2026-07-13

    Document version

    1

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