Scaling the personalized adaptive learning program in Andhra Pradesh, India

Last registered on March 07, 2026

Pre-Trial

Trial Information

General Information

Title
Scaling the personalized adaptive learning program in Andhra Pradesh, India
RCT ID
AEARCTR-0014271
Initial registration date
January 31, 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
February 05, 2025, 8:33 AM EST

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

Last updated
March 07, 2026, 7:09 PM EST

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

Locations

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Primary Investigator

Affiliation
Columbia University

Other Primary Investigator(s)

PI Affiliation
The University of Chicago
PI Affiliation
The University of Chicago
PI Affiliation
Columbia University
PI Affiliation
The University of Chicago
PI Affiliation
Indian School of Business, Hyderabad
PI Affiliation
The University of Chicago

Additional Trial Information

Status
On going
Start date
2023-07-31
End date
2027-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Personalized Adaptive Learning (PAL) with edtech fosters remedial learning and mitigates learning gaps in settings where children lag behind grade-level skills (de Barros and Ganimian 2023; Muralidharan, Singh, and Ganimian 2019; Escueta et al. 2017). It can be a cost-effective way to address pandemic-related learning loss and inequities in access to education when investments in edtech have already been made (World Bank 2020). However, the effectiveness of education interventions, including learning through the use of edtech, when conducted at scale can be sensitive to the implementation features (List 2022; Banerjee et al. 2017; Kulik and Fletcher 2016). The Government of Andhra Pradesh (GoAP) in India has launched one of the first government-run PAL programs in over 500 schools, with an interest in scaling to more schools. Our research aims to evaluate the Government's PAL program and provide insights on challenges and necessary adaptations to establish sustainable and scalable PAL Labs using a “right-fit” research approach.
External Link(s)

Registration Citation

Citation
Gupta, Saloni et al. 2026. "Scaling the personalized adaptive learning program in Andhra Pradesh, India." AEA RCT Registry. March 07. https://doi.org/10.1257/rct.14271-2.0
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Experimental Details

Interventions

Intervention(s)
The Government of Andhra Pradesh (GoAP) in India has launched one of the first government-run PAL programs in over 500 schools, with an interest in scaling to more schools. However, the effectiveness of education interventions, including learning through the use of edtech, when conducted at scale can be sensitive to the implementation features (List 2022; Banerjee et al. 2017; Kulik and Fletcher 2016). In collaboration with the GoAP, this project contains 4 studies (Randomized Controlled Trials and other interventions) to achieve the project goals of:
1) Evaluating the impact of the GoAP’s PAL program, and
2) Studying how the program can be designed to be more effective at scale.

Personalized Adaptive Learning (PAL) with edtech fosters remedial learning and mitigates learning gaps in settings where children lag behind grade-level skills (de Barros and Ganimian 2023; Muralidharan, Singh, and Ganimian 2019; Escueta et al. 2017). It can be a cost-effective way to address pandemic-related learning loss and inequities in access to education when investments in edtech have already been made (World Bank 2020).

In 2019, the School Education Department, Government of Andhra Pradesh (GoAP) rolled out one of the first government-run PAL programs for students in grades 6 to 9 in 520 schools across 17 districts of Andhra Pradesh. Each of the 520 schools received 30 tablets with the PAL software with Mathematics content loaded on them and were told to setup a dedicated PAL Lab for students. Each student has an individual login through which they access the software. For each Mathematics chapter, the dynamic adaptive software identifies individual student learning gaps through a pre-assessment and provides remedial content including videos and questions. The chapters and content are mapped to the AP state curriculum. School math teachers are instructed to dedicate 2 out of 8 math periods in a week for PAL Lab. Further, the Government hired Field Management Staff (FMS) to help with operations and logistics of the PAL Labs in schools.

In collaboration with the GoAP, the researchers have been conducting 3 research studies (Randomized Controlled Trials and other studies) in Government schools. The aim of the first study is to evaluate the impact of the PAL program on student learning outcomes in mathematics.
As an extension of the first study, a second phase was conducted to examine the longer-term impacts of the government-run PAL program and the mechanisms underlying its effects. This follow-up was conducted with 40 treatment and 40 control schools from the original RCT to assess the persistence of impacts. In addition, the design includes a non-experimental component drawing on data from 80 additional schools (40 residential schools and 40 schools from the broader group of 500 non-RCT PAL schools that had received the program prior to the RCT). Outcomes in these schools will be compared to those in the original treatment and control schools to better understand how variation in exposure, and implementation context shapes learning outcomes.

The second study aims to study whether varying the intensity of software remediation affects student learning outcomes. In particular, it examines the trade-off between reinforcing remedial concepts—which may enable faster learning over time—and the risk that students remain overly focused on remedial content and progress more slowly. The study also explores whether differences in students’ prerequisite knowledge generate spillover effects at the classroom level within a large-scale personalized adaptive learning program in Andhra Pradesh.

The third study examines how implementation intensity and frontline support shape the effectiveness of a large-scale personalized adaptive learning program, by assessing whether variation in the frequency of Field Management Staff visits influences software usage and sustained engagement in schools.

These studies will contribute to the evidence base to support the scaling of targeted foundational learning programs in government systems.
Intervention Start Date
2023-07-31
Intervention End Date
2026-07-31

Primary Outcomes

Primary Outcomes (end points)
Student learning outcomes in Math
Primary Outcomes (explanation)
Math outcomes will be constructed from three sources i) Standardized learning assessments in math at endline, ii) PAL software data available with the software vendor and made available with state permissions and iii) school assessments data provided by the state government

Secondary Outcomes

Secondary Outcomes (end points)
PAL software usage, teacher and headmaster adoption rates and perceptions, implementation fidelity, student interest in math and other subjects, student attendance, any spillover on language subjects (english and Telugu), tablets vs paper assessments, one-language math word problems, remedial vs at-grade gains, heterogeneity by gender, grade, baseline ability level.
Secondary Outcomes (explanation)
Secondary outcomes for students will be constructed from PAL software data available with the software vendor and made available with state permissions, and surveys conducted with students on interest and use of PAL prior to conducting the learning assessments.
Teacher and Headmaster adoption and perceptions will be captured through teacher-headmaster surveys to understand their experience using PAL and how it has influenced regular classroom teaching. Teacher and headmaster surveys will also be conducted in the non-PAL control schools to understand how remedial and foundational learning for weaker students is supported in the absence of PAL.

Experimental Design

Experimental Design
We evaluated a large-scale government-run Personalized Adaptive Learning (PAL) program using a combination of randomized controlled trials and A/B tests. We have launched two RCTs to evaluate the PAL program by randomizing at either the student- or school-level, and are currently conducting an A/B test across 524 PAL schools to examine how variation in state-sponsored implementation support affects program usage. As an extension of the first study, we are also conducting a second round of standardized learning assessments to study how learning outcomes vary with PAL exposure across different school types, durations of exposure, and implementation contexts.

We will also examine two methodological considerations related to assessment design. First, we will test whether administering endline assessments on tablets produces differential effects by treatment status. Second, we will assess whether student performance varies based on the language of the test questions (English only versus Telugu only).
Experimental Design Details
Not available
Randomization Method
Using Software such as Stata/R on office computers for three studies and using PAL software for one study
Randomization Unit
Cluster and Individual Both
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
140 schools
Sample size: planned number of observations
24,000+ students
Sample size (or number of clusters) by treatment arms
Impact Study - 60 T 60C (Schools)
Remediation Study - 2500 T 2500 C (Individual)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Impact Study - 0.248 standard deviations Remediation Study - 0.1543 standard deviations
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
UChicago IRB
IRB Approval Date
2023-06-26
IRB Approval Number
IRB23-0807
Analysis Plan

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