Team Production with AI

Last registered on July 06, 2026
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Pre-Trial

Trial Information

General Information

Title
Team Production with AI
RCT ID
AEARCTR-0019019
Initial registration date
July 03, 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
July 06, 2026, 9:21 AM EDT

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

Locations

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Primary Investigator

Name
Gregory Sun
Affiliation
Washington University in St Louis
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Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2026-06-18
End date
2026-07-17
Keywords
Labor
Additional Keywords
Artificial intelligence, team production
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This trial aims to understand how AI use affects team production. To do so, we will randomize in two steps. First, we randomize students in a summer RA program into either getting training that encourages AI use and provides tips on how to use AI tools most effectively versus getting a training that warns about all of the pitfalls of using AI. Throughout the summer, we will then have students complete tasks in pairs, where we will randomly assign pars such that teams vary in their composition of AI trainings.
External Link(s)

Registration Citation

Citation
Sun, Gregory. 2026. "Team Production with AI." AEA RCT Registry. July 06. https://doi.org/10.1257/rct.19019-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
There are two layers of interventions. The first layer is at the person level, while the second layer is at the team level, based on the assignments of the first layer.

For the first layer, the 21 students in the summer program were randomized into two AI trainings. Training 1, the AI-positive training, provided tips and tricks on how to use AI tools most effectively, and tried to show students that good AI use requires good human input, but can be highly productivity-enhancing if used correctly. Training 2, the AI-negative largely just walks through examples of papers showing pitfalls of using AI.

For the second layer, on 10 days of the program, students will be assigned to work on coding tasks writing code to implement various procedures described in John List's Experimental Economics textbook. Within each day, students will be paired into teams, and team compositions are set to be roughly even between having 1) 2 AI-negative participants, 2) 2 AI-positive participants, and 3) one of each. Teams will have 1 hour to complete the code.
Intervention Start Date
2026-06-18
Intervention End Date
2026-07-17

Primary Outcomes

Primary Outcomes (end points)
To test if the intervention succeeded in changing behavior, we will administer a problem set, with coding tasks. We will check whether there are stronger signs of AI use among the treated students.

We will evaluate code based on completeness (how many cases it is able to handle), as well as overall readability by a third party.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study examines how training shapes the way people use AI tools on collaborative coding tasks. Twenty-one students in a summer program were randomly assigned to one of two trainings. The first, an AI-positive training, offered practical tips for using AI tools effectively and emphasized that good results depend on good human input while highlighting AI's potential to boost productivity. The second, an AI-negative training, walked through examples of papers illustrating the pitfalls of relying on AI. Over 10 days of the program, students were then paired into two-person teams to write code implementing procedures from John List's Experimental Economics textbook, with one hour per task. Team compositions were balanced across three types: two AI-negative participants, two AI-positive participants, or one of each, allowing us to study how individual training interacts with team composition.
To assess whether the training changed behavior, we administer a problem set of coding tasks and look for stronger signs of AI use among students in the AI-positive condition. Submitted code is evaluated on completeness, measured by how many cases it successfully handles, and on readability as judged by a third party.
Experimental Design Details
Not available
Randomization Method
By computer
Randomization Unit
Two layers:
Layer 1 is randomized at the person level, while Layer 2 is randomized at the team level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
21 total students,
100 total "clusters" (defined as an assignment-team pair).
Sample size: planned number of observations
21 pupils, 10 problems with 10 teams per problem.
Sample size (or number of clusters) by treatment arms
Roughly 33 each of teams comprising:
1) 2 AI-negative participants
2) 2 AI-positive participants
3) Mix of both
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
AURA IRB (University of Chicago)
IRB Approval Date
2026-06-10
IRB Approval Number
IRB26-1004
Analysis Plan

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