Not Faster, but Better: Generative AI and Teacher Skill in Peru 2025
Baseline and Endline Surveys
Peru, 2025
Get MicrodataIdentification
PER_2025_GAITS_v01_M
Not Faster, but Better: Generative AI and Teacher Skill in Peru 2025
Baseline and Endline Surveys
GAITS 2025
| Name | Country code |
|---|---|
| Peru | PER |
This dataset accompanies the paper "Not Faster, but Better: Generative AI and Teacher Skill in Peru" (Glave, Lopez, Molina & Zarate). The data come from a randomized controlled trial conducted in 390 public primary schools in Metropolitan Lima, Peru, during the 2025 school year. The study evaluated a program providing sixth-grade teachers with a one-day training on generative AI, a Microsoft Copilot license, and weekly WhatsApp follow-up; the message sender (education authority vs. peer teacher) and content (general vs. teaching-at-the-right-level) were cross-randomized. The dataset is a teacher-level file combining administrative rosters, training attendance, a baseline survey, weekly Copilot usage records, and an endline survey with blind-graded prompt-quality measures for 1,269 teachers. Pre-registered as AEARCTR-0016336; IRB protocol #48010 (University of Toronto).
Sample survey data [ssd]
Teachers
Version
2026-07-03
Scope
The dataset covers: program take-up (training attendance, Copilot license activation and weekly usage); teachers' use of AI for professional and personal tasks; time allocation across seven core teaching activities; blind-graded quality of AI prompts (training-based and general rubrics); teachers' ability to evaluate AI-generated output (prompt scenarios); beliefs about AI, equity, and willingness to pay; a discrete choice experiment on hypothetical AI-use scenarios; and baseline teacher and school characteristics.
Coverage
School (institución educativa); randomization stratified by UGEL (local education district)
Sixth-grade primary school teachers in public schools in Metropolitan Lima, Peru, during the 2025 school year. The sample comprises all 390 public schools in Metropolitan Lima that offer sixth grade of primary education (196 treatment, 194 control) and their 1,269 sixth-grade teachers.
Producers and sponsors
| Name | Affiliation |
|---|---|
| Carla Z. Glave | University of Wisconsin–Madison |
| Carolina Lopez | Development Research Group, World Bank |
| Ezequiel Molina | World Bank |
| Román Andrés Zárate | University of Toronto |
| Name | Role |
|---|---|
| Human Capital Project (World Bank) | Primary Founder |
| Microsoft AI Economy Institute |
Sampling
The sample is a census of the 390 public schools in Metropolitan Lima offering sixth grade of primary education. Treatment was assigned in two stages. First, eligibility was randomized at the school-network level: 84 of 143 administrative networks were selected, re-running the randomization 1,000 times and keeping the allocation that maximized the minimum p-value across baseline school characteristics. Second, treatment was randomized at the school level within eligible networks, stratified by UGEL (local school district) and by whether baseline school math performance was above or below the median, using the same re-randomization procedure. Two components were cross-randomized within the treatment group: the sender of the follow-up messages (education authority vs. peer teacher) and their content (general support vs. teaching-at-the-right-level).
No weights are used. All estimates are unweighted; regressions include randomization strata fixed effects.
Survey instrument
Baseline Teacher Survey (Spanish, Baseline); Endline Teacher Survey (Spanish, Endline)
Data collection
| Start | End | Cycle |
|---|---|---|
| 2025-04-10 | 2025-05-11 | Baseline post training |
| 2025-04-01 | 2025-12-11 | Copilot usage records |
| 2025-12-01 | 2025-12-31 | Endline |
- Internet [int]
| Name |
|---|
| World Bank research team, in coordination with DRELM |
Survey data were collected through self-administered online questionnaires. The baseline survey was administered after the March 2025 training sessions through a form on DRELM's teacher platform. The endline survey was administered in Qualtrics, first during in-person sessions and subsequently distributed online to maximize response rates; the survey modality of each respondent (endline_online) is recorded in the dataset and controlled for in all survey-based analyses. In addition to survey data, the dataset incorporates administrative sources: school and teacher rosters from DRELM, attendance records from the March 2025 training sessions, and weekly Microsoft Copilot license usage reports . Open-ended prompt responses from the endline were blind-graded by evaluators using two rubrics; only the graded scores are included in the dataset.
Data processing
Data from the five sources were merged at the teacher level. De-identification: direct identifiers (names, national IDs, e-mails, phone numbers), Qualtrics metadata (IP, geolocation, timestamps), open-text responses, granular geography, and school-level variables matchable to the public school census were removed; school, district and teacher identifiers were replaced with anonymous codes assigned with fixed random seeds. Missing covariates are flagged and imputed within the analysis code.
Data Access
| Name | Affiliation | |
|---|---|---|
| Carolina Lopez | Development Research Group, World Bank | carolina_lopez@worldbank.org |
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | The data were collected under IRB protocol #48010 approved by the University of Toronto Research Ethics Board. All participants provided informed consent. Data have been de-identified: names and direct identifiers have been removed. Respondents are adult public school teachers. |
Access to this dataset requires signing a Data Use Agreement (DUA) with the authors. Users may not attempt to re-identify study participants, share the data with third parties, or use the data for purposes other than research. Publications using these data must acknowledge the original study and cite the accompanying paper.
Glave, Carla Z., Carolina Lopez, Ezequiel Molina, and Román Andrés Zárate. "Not Faster, but Better: Generative AI and Teacher Skill in Peru." Working paper.
Disclaimer and copyrights
The user of the data acknowledges that the original collectors of the data, the authorized distributors of the data, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses. The findings, interpretations, and conclusions expressed in any work using this dataset are entirely those of the authors and do not necessarily represent the views of the World Bank, its affiliated organizations, or the governments they represent.
© World Bank
Contacts
| Name | Affiliation | |
|---|---|---|
| Carolina Lopez | Development Research Group, World Bank | carolina_lopez@worldbank.org |
Metadata production
DDI_PER_2025_GAITS_v01_M
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | World Bank Group | Documentation of the survey |
Metadata version
Version 01 (July 2026)