AI Support for Human Tutors to Address Latin America’s Teacher Shortage

Last registered on September 29, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

AI Support for Human Tutors to Address Latin America’s Teacher Shortage

RCT ID

AEARCTR-0016870

Initial registration date

September 24, 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

September 29, 2025, 10:52 AM EDT

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

Locations

Country

Peru

Region

Latin America

Primary Investigator

Name

Salome Aguilar Llanes

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Andres Salazar

PI Affiliation

MIT

Contact Investigator

PI Name

Alok Prakash

PI Affiliation

MIT-SMART

Contact Investigator

Additional Trial Information

Status

In development

Start date

2025-10-06

End date

2026-01-12

Keywords

Education, Labor

Additional Keywords

JEL code(s)

J24, I25

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We conduct a randomized controlled trial (RCT) of an online math tutoring program in Peru to evaluate whether AI tools can augment human tutors. Our study investigates whether AI can (i) lower barriers to recruiting volunteer tutors, (ii) improve tutoring quality as measured by student learning outcomes and perceptions, (iii) increase tutor retention, and (iv) affect both tutor and student motivation. In parallel, we systematically map tutor tasks to identify which activities are most amenable to AI support. Unlike approaches that seek to replace human teachers with AI, our intervention emphasizes AI as a complement to human tutoring, enhancing communication and instructional effectiveness. Beyond estimating causal effects, our study generates practical insights on best practices for AI-assisted math tutoring and provides a repository of tutor- and AI-generated instructional content, offering a scalable model for addressing teacher shortages in resource-constrained settings.

External Link(s)

Registration Citation

Citation

Aguilar Llanes, Salome, Alok Prakash and Andres Salazar. 2025. "AI Support for Human Tutors to Address Latin America’s Teacher Shortage." AEA RCT Registry. September 29. https://doi.org/10.1257/rct.16870-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2025-10-13

Intervention End Date

2025-12-12

Primary Outcomes

Primary Outcomes (end points)

AI literacy questionnaire
Math evaluation for students to assess quality of teaching
Tutor evaluations by students
Tutor retention
Tutor conversion from expressing interest to actually participating
Lesson plans to assess quality of teaching
Class recordings to assess quality of teaching
Mapping tutor tasks to prompts

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Tutors are volunteer university students recruited from a university in Peru. Information about the tutoring opportunity will be disseminated through the university’s communication channels. Tutors will indicate interest in participating in the RCT by signing up to receive additional information.

Stage 1: Initial Randomization (AI Information Exposure)

Tutors will be randomly assigned to one of two groups:
AI-Information Group: Tutors are informed that they will receive training to prepare their classes, as well as training in AI tools to assist in tutoring activities. All tutors in this group will receive the AI tools training.

No-AI Information Group: Tutors are informed that they will receive training to prepare their classes, with no mention of AI tools.

We will first compare conversion rates, which is the proportion of tutors who sign up and start teaching—between these two groups.
Stage 2: Student and Tutor Assignment
Students will be randomly assigned to groups, and groups will in turn be randomly assigned to tutors. All tutors will begin teaching math classes.

Stage 3: Nested Randomization of AI Training
Tutors in the No-AI Information Group will undergo a second randomization into two subgroups:
No-AI Training: Tutors receive standard tutor training with no explicit mention of AI.

AI Training: Tutors receive a version of the standard training that incorporates instruction in AI tools and AI literacy.

We will compare outcomes—including student learning, student perceptions, tutor retention, and tutor/student motivation—between these two subgroups and against the AI-Information Group.

Covariates
All analyses will control for tutor and student demographic variables, including university major and campus (the university has several campuses).

Was the treatment clustered?
The main outcomes won’t be clustered. The randomization will be done at the tutor level. The math learning outcomes will be clustered at the tutor level.

Subgroup analysis will be performed by gender and major, by baseline differences in teaching styles and quality.

Experimental Design Details

Randomization Method

Randomization done in office by computer

Randomization Unit

Individual for tutors and cluster-level for the students

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

Students will be randomized into groups of about 5, and will be randomly assigned to tutors. Tutors will be randomly assigned to treatment and control groups. This means we plan to have 1000 clusters.

Sample size: planned number of observations

Tutors: 1000 Students: 5000

Sample size (or number of clusters) by treatment arms

Three treatment arms.
AI-Information Group: Tutors are informed that they will receive training to prepare their classes (1/3tutors)
AI Training: Tutors receive a version of the standard training that incorporates instruction in AI tools and AI literacy (1/3 tutors)
No-AI Training: Tutors receive standard tutor training with no explicit mention of AI(1/3 tutors)

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB