Rapid Evaluation of the Impact of Gemini Guided Learning on Students’ Mathematics Scores in Junior Secondary Schools in Sierra Leone

Last registered on January 27, 2026
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Pre-Trial

Trial Information

General Information

Title
Rapid Evaluation of the Impact of Gemini Guided Learning on Students’ Mathematics Scores in Junior Secondary Schools in Sierra Leone
RCT ID
AEARCTR-0016651
Initial registration date
September 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
September 19, 2025, 10:20 AM EDT

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

Last updated
January 27, 2026, 5:10 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Sierra Leone
Region
Africa

Primary Investigator

Name
Natalia Valdes Aspillaga
Affiliation
Fab Inc
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Other Primary Investigator(s)

PI Name
María José Ogando Portela
PI Affiliation
Fab Inc
Contact Investigator
PI Name
Usman Khawar
PI Affiliation
Fab Inc
Contact Investigator

Additional Trial Information

Status
On going
Start date
2025-09-22
End date
2026-03-31
Keywords
Education
Additional Keywords
Artificial Intelligence, EdTech, AI Tutoring
JEL code(s)
I24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates the impact of Gemini Guided Learning, an AI-powered instructional experience within Gemini that provides personalised learning through guided questions, step-by-step scaffolding, and multimodal resources rather than supplying direct answers, on mathematics learning in Junior Secondary Schools (JSS1 and JSS2, ages 13–15). The trial randomly assigns classrooms either to continue with regular teaching or to use Gemini in approximately half of their mathematics lessons over an eight to nine (8-9) week period. The same teachers are responsible for both treatment and control classrooms; all will receive training on how to use the tool, though it will only be applied in treatment classes. Mathematics performance of students and teachers will be assessed at the beginning and end of the intervention. Teachers’ experiences with the tool will also be documented. The study will provide rigorous evidence on whether AI-supported guided learning can strengthen mathematics learning in junior secondary education.
External Link(s)
Introduction of Gemini Guided Learning

Registration Citation

Citation
Khawar, Usman, María José Ogando Portela and Natalia Valdes Aspillaga. 2026. "Rapid Evaluation of the Impact of Gemini Guided Learning on Students’ Mathematics Scores in Junior Secondary Schools in Sierra Leone." AEA RCT Registry. January 27. https://doi.org/10.1257/rct.16651-2.0
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Experimental Details

Interventions

Intervention(s)
Gemini Guided Learning is an AI-powered instructional tool within Gemini, designed to support students’ mathematics learning through guided questions, step-by-step scaffolding, adaptive feedback, and multimodal resources rather than providing direct answers. The intervention will be implemented in Junior Secondary School classrooms (JSS1 and JSS2, ages 13–15) over an eight to nine (8-9) week period, during which students in treatment classrooms will use Gemini in about half of their mathematics lessons. Teachers will receive training on how to use the tool, although it will only be applied in treatment classrooms.
Intervention Start Date
2025-10-06
Intervention End Date
2025-12-12

Primary Outcomes

Primary Outcomes (end points)
The primary outcome of interest is student mathematics achievement, measured using standardised assessments aligned with the JSS1 and JSS2 curriculum and administered at baseline and endline. Assessments are designed to capture gains in core mathematical competencies such as numeracy, problem-solving, and application of concepts.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study employs a cluster randomized controlled trial with classrooms as the unit of randomization. Randomization is stratified by school and grade level (JSS1 vs JSS2) using computer-generated random assignment with a pre-specified seed. This stratification approach ensures balanced allocation across key prognostic factors and prevents potential confounding between teacher effects and treatment effects.
Classrooms are randomly assigned to either: (1) Treatment arm receiving access to an AI-powered mathematics tutoring platform in addition to standard instruction, or (2) Control arm receiving standard mathematics instruction only.
Experimental Design Details
Not available
Randomization Method
Done in office by a computer using stata
Randomization Unit
Classroom
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
48 sections (16 JSS1, 32 JSS2) across 12 schools
Sample size: planned number of observations
Approximately 2,000 students
Sample size (or number of clusters) by treatment arms
24 control and 24 treated
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using a two-level classroom-cluster design with α=0.05 (two-tailed), power=0.80, 48 clusters, average cluster size=30, 50/50 allocation, and a conditional ICC of 0.07, the MDES is 0.257 SD on the student-level standardised mathematics score
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Sierra Leone Ethics and Scientific Review Committee (SLESRC)
IRB Approval Date
2025-09-08
IRB Approval Number
007/09/2025
Analysis Plan

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