Teaching Econometrics with AI: Evidence from a Randomized Trial

Last registered on September 15, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

Teaching Econometrics with AI: Evidence from a Randomized Trial

RCT ID

AEARCTR-0016770

Initial registration date

September 12, 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 15, 2025, 9:41 AM EDT

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

Locations

Country

Chile

Region

Primary Investigator

Name

Sebastian Gallegos

Affiliation

UAI Business School

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2025-09-01

End date

2025-12-23

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 the impact of promoting the use of GPT EconometríaUAI, a conversational AI assistant, on undergraduate students’ learning outcomes in econometrics. The intervention will be implemented during the second semester of 2025 at Universidad Adolfo Ibáñez across seven sections of the Econometrics course. Students will be individually randomized within sections into treatment and control groups. The treatment group will receive a series of emails encouraging the use of GPT EconometríaUAI for study support, while the control group will not receive such promotion (placebo emails may be used).
The primary outcome is student achievement on the first midterm exam. Secondary outcomes include satisfaction with the course and students’ perceptions of their learning and study experience. The study will provide experimental evidence on the role of AI-based study assistants in shaping both academic performance and broader student experiences in higher education.

External Link(s)

Registration Citation

Citation

Gallegos, Sebastian. 2025. "Teaching Econometrics with AI: Evidence from a Randomized Trial." AEA RCT Registry. September 15. https://doi.org/10.1257/rct.16770-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The intervention consists of promoting the use of GPT EconometríaUAI through emails sent from the official course coordination to students randomized into the treatment group. We plan to send three emails as follows:
Email 1 — Monday at noon (2 days before the exam)
Subject: Have you tried the EconometríaUAI GPT?
Dear [NAME],
Remember that you have access to GPT EconometríaUAI, a tool specially trained with course materials to help you study.
👉 Try it here: [GPT LINK] https://chatgpt.com/g/g-ftvZAGEWO-econometriauai
Explore it today and get better prepared for Wednesday’s exam!
Sincerely,
Econometrics Coordination

Email 2 — Tuesday 11 am (1 day before the exam)
Subject: Questions about Midterm 1? GPT can help you
Dear [NAME],
It’s normal to have questions the day before an exam! GPT EconometríaUAI can support you.
Take advantage of this tool to review your knowledge!
For example, ask:
❓ How do I interpret the constant in a simple regression?
🤖 GPT responds: The constant (intercept) in a simple linear regression represents the predicted value of the dependent variable when the explanatory variable equals zero.
👉 Try it now with your own questions: [GPT LINK] https://chatgpt.com/g/g-ftvZAGEWO-econometriauai
Sincerely,
Econometrics Coordination

Email 3 — Wednesday 10 am (morning of the exam day)
Subject: Last questions before today’s exam?
Dear [NAME],
Today at 6:00 pm is Midterm 1.
Do you have last-minute questions? Watch this short video (<1 minute) and learn how GPT can help you review:
🎥 Watch video: https://www.youtube.com/watch?v=UNHFs4TKi5M
👉 Access GPT here: https://chatgpt.com/g/g-ftvZAGEWO-econometriauai
Best of luck!
Econometrics Coordination

Intervention (Hidden)

Intervention Start Date

2025-09-08

Intervention End Date

2025-12-19

Primary Outcomes

Primary Outcomes (end points)

Grade and total Score on the first econometrics midterm exam. This is the main measure of student academic achievement that the intervention seeks to influence. We will also decompose the score on specific questions, as we expect the GPT to have larger effects on different types of assessments

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Course satisfaction. Student self-reported satisfaction with the econometrics course, collected via a post-exam survey.

Learning perceptions. Student perceptions of their own understanding and mastery of course content, measured through survey questions.

Exposure and use of GPT EconometríaUAI. Self-reported frequency, type of use, and perceived usefulness of the GPT assistant, also collected through the post-exam survey.

Exploratory outcomes:
Spillovers across peers. Indicators of whether control group students indirectly learned about or used the GPT through classmates, as captured by survey questions on peer communication and study group dynamics.

Longer-term academic performance. Scores on subsequent exams (e.g., second midterm, final exam), if the intervention is extended.

Secondary Outcomes (explanation)

Experimental Design

Random assignment will be conducted at the individual level, within each course section, with a 33%-67% split between treatment and control. This assignment will be stratified by observable characteristics available in administrative records (such as gender and high school type), to improve the precision of estimates.

Experimental Design Details

Randomization Method

randomization done in office by a computer, replicable, with stata code

Randomization Unit

individual level, within classrooms

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

no clusters

Sample size: planned number of observations

385

Sample size (or number of clusters) by treatment arms

127 to treatment, 258 to control

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

Assuming a standard deviation of 1.0 for test scores and a baseline mean of 4.5, the design allows detecting a minimum statistically significant effect (with 80% power and α=0.05) of about 0.3 sd on the score. Controlling for the stratification method and further pre-treatment covariates will enhance the precision of the estimates

Supporting Documents and Materials

IRB