Generative AI and the Achievement Gap: Evidence from a Field Experiment

Last registered on October 07, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Generative AI and the Achievement Gap: Evidence from a Field Experiment
RCT ID
AEARCTR-0019867
Initial registration date
October 04, 2026

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
October 07, 2026, 10:47 AM EDT

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

Locations

Country
China
Region
Zhejiang Province

Primary Investigator

Name
Jun LUO
Affiliation
Zhejiang University of Finance & Economics
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Fangwen Lu
PI Affiliation
PI Name
Jiayi Pan
PI Affiliation
PI Name
Lehao Zou
PI Affiliation

Additional Trial Information

Status
Completed
Start date
2024-06-01
End date
2025-12-31
Keywords
Behavior, Education
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Generative AI is transforming how cognitive work is produced, raising questions about its consequences for human capital formation and inequality. We tried to study these questions in a randomized field experiment that provides undergraduates with initial exposure to AI and then leaves subsequent use to their own choices in regular coursework.
External Link(s)

Registration Citation

Citation
Lu, Fangwen et al. 2026. "Generative AI and the Achievement Gap: Evidence from a Field Experiment." AEA RCT Registry. October 07. https://doi.org/10.1257/rct.19867-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2024-09-01
Intervention End Date
2025-06-30

Primary Outcomes

Primary Outcomes (end points)
students' academic outcomes
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We focus on students without prior AI experience because the experiment is designed to study the consequences of incorporating AI into students’ learning environments, including how access to the technology shapes subsequent use and learning outcomes.
Experimental Design Details
Randomization Method
randomization done in office by a computer.
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1000 individuals
Sample size: planned number of observations
1000 individuals
Sample size (or number of clusters) by treatment arms
500 students in the control group and 500 in the AI-exposure group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Committee on Human Research Protection of CEBD
IRB Approval Date
2024-07-01
IRB Approval Number
2024070101
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials