Teacher-Designed AI for Personalized Learning: Evidence from Roma Students in Spain

Last registered on November 25, 2025
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Pre-Trial

Trial Information

General Information

Title
Teacher-Designed AI for Personalized Learning: Evidence from Roma Students in Spain
RCT ID
AEARCTR-0017234
Initial registration date
November 18, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 25, 2025, 7:31 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Laia Navarro-Sola
Affiliation
Stockholm University IIES
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Other Primary Investigator(s)

PI Name
Lucía Cobreros
PI Affiliation
ESADE
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PI Name
Antonio Roldán-Monés
PI Affiliation
ESADE
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Additional Trial Information

Status
In development
Start date
2025-11-16
End date
2027-03-30
Keywords
Education
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates whether empowering teachers to design and use artificial intelligence (AI) tutoring tools can improve student learning and engagement in after-school educational programs in Spain. Despite sustained investment in remedial education, teachers continue to face challenges in tailoring instruction to students’ diverse learning levels and needs. Through a no-code platform, teachers in treatment centers receive training and coaching to create and adapt AI tutoring chatbots aligned with curricular goals and student learning profiles. The chatbots provide adaptive practice and feedback during after-school sessions, complementing, rather than replacing, human instruction. The study will use a cluster-randomized controlled trial across multiple sites and regions in Spain, with randomization at the teacher-group level to prevent spillovers. It will measure impacts on student achievement, as well as engagement and effort, and self-efficacy.
External Link(s)

Registration Citation

Citation
Cobreros, Lucía, Laia Navarro-Sola and Antonio Roldán-Monés. 2025. "Teacher-Designed AI for Personalized Learning: Evidence from Roma Students in Spain." AEA RCT Registry. November 25. https://doi.org/10.1257/rct.17234-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Educators in after-school programs for Roma students in Spain receive structured training, ongoing coaching, and access to a no-code platform that allows them to design and adapt AI tutoring chatbots for their students. Through the platform, teachers specify prompts, materials, and pedagogical rules so that chatbots can deliver guided practice and feedback aligned with curricular goals and students’ needs, preferences, and identities. Teachers in the control group continue standard after-school instruction without training or platform access.
Intervention Start Date
2025-11-23
Intervention End Date
2026-06-30

Primary Outcomes

Primary Outcomes (end points)
Standardized test scores in math, reading, and English
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Student-level outcomes: Engagement, self-efficacy, trust in AI, and sense of belonging
Teacher-level outcomes: Self-efficacy, stress, time use, and adoption of personalized pedagogical strategies
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our baseline sample consists of approximately 1,700 students enrolled in after-school programs across 60 centers in 13 regions of Spain. The study randomizes 120 teacher-block clusters, defined as one or more educators who co-teach or share students within the same group.

Clusters are randomly assigned with equal probability to treatment or control. Treatment teacher-blocks receive structured training, ongoing coaching, and access to a no-code AI platform that allows them to design and adapt tutoring chatbots for their students. Control teacher-blocks continue standard after-school programming without access to the platform or training. Randomization is stratified by after-school program type, site or region, and baseline cluster achievement.

Our main empirical specifications will estimate intention-to-treat effects, reflecting the causal impact of assignment to each treatment arm on our outcomes of interest.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Clustered randomization occurs at the teacher-block level, defined as one or more educators who co-teach or share students within the same after-school group, to prevent spillovers between teachers working with the same students.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Approximately 120 teacher-block clusters
Sample size: planned number of observations
Approximately 1,700 students across 60 centers in 13 regions in Spain
Sample size (or number of clusters) by treatment arms
60 teacher-block clusters treatment, 60 teacher-block clusters control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Esade’s Committee on the Use of Human Subjects in Research (CUHSR)
IRB Approval Date
2025-07-17
IRB Approval Number
025/2025
Analysis Plan