Adapting to AI: How Information on Occupational AI Exposure Affects Educational Decisions

Last registered on April 13, 2026
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Pre-Trial

Trial Information

General Information

Title
Adapting to AI: How Information on Occupational AI Exposure Affects Educational Decisions
RCT ID
AEARCTR-0018261
Initial registration date
April 06, 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
April 13, 2026, 9:10 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
Amanda Chuan
Affiliation
Michigan State University
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Other Primary Investigator(s)

PI Name
German Reyes
PI Affiliation
Middlebury College
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PI Name
Joaquin Serrano
PI Affiliation
Universitat Autonoma de Barcelona
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PI Name
Hanzhe Zhang
PI Affiliation
Michigan State University
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Additional Trial Information

Status
In development
Start date
2026-04-01
End date
2036-04-01
Keywords
Education, Labor
Additional Keywords
JEL code(s)
I26, J38, J24, J23
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Are educational decisions affected by information about how artificial intelligence (AI) impacts future job prospects? We will run a field experiment on undergraduate students at a large state university. The treatment group will receive information about how generative artificial intelligence (AGI) has changed various occupations, while the control group will receive placebo information. Our survey will elicit self-reported choice of major, desired occupations, and beliefs regarding future job market outcomes both before and after treatment. We will link these data to administrative student record data on course selection, major declarations, and graduation outcomes. We will also link survey results to post-graduation employment data for students to examine whether treatment assignment influences employment outcomes and occupational selection.
External Link(s)

Registration Citation

Citation
Chuan, Amanda et al. 2026. "Adapting to AI: How Information on Occupational AI Exposure Affects Educational Decisions." AEA RCT Registry. April 13. https://doi.org/10.1257/rct.18261-1.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
We will present information about AI's impact on occupations from Brynjolfsson et al. (2025) and the Anthropic Economic Index (Handa et al., 2025). This information is presented in an accessible, interactive visualization that presents detailed information about each occupation. We also provide the opportunity to converse with a chatbot trained with data and research regarding the impact of AI on various occupations.
Intervention Start Date
2026-04-01
Intervention End Date
2036-04-01

Primary Outcomes

Primary Outcomes (end points)
Our key outcomes are classified into five families of outcomes.
Family 1: Engagement with treatment materials;
Family 2: Belief Updating;
Family 3: Stated Intentions and Choices,
Family 4: Commitment Behaviors;
Family 5: Student Choices and Labor Market Outcomes.

Please see attached pre-analysis plan for details regarding how we measure each of the families of outcomes listed above.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The treatment condition provides information about the exposure of different occupations to generative AI through two components. First, participants interact with an interactive visualization that ranks occupations by their exposure to generative AI. Second, participants interact with an AI chatbot powered by Claude (Anthropic).

The control condition mirrors the treatment in format and interaction burden but strips out all AI-related content. The control visualization displays the same occupations as treatment, but provides information about their alphabetical grouping based on the first letter. The control chatbot acts as a generic career advisor providing education requirements and fields of study.
Experimental Design Details
Not available
Randomization Method
Randomization will occur within Qualtrics' built-in randomizer. We will not stratify based on baseline characteristics. Participants will be assigned to either treatment or control.

Randomization Unit
We will randomize at the respondent level. Students will be randomly assigned to treatment or control when they consent to participate in the survey.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We will target a sample size of 3,000 survey respondents. The unit of observation is the unit of randomization.
Sample size: planned number of observations
We will target a sample size of 3,000 survey respondents.
Sample size (or number of clusters) by treatment arms
We will rely on the Qualtrics randomizer, but expect roughly 1,500 in the treatment condition and 1,500 in the control condition.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For our target of 3,000 students, assuming equal variance between control and treatment we expect to detect effect sizes as small as 0.072 of a standard deviation (List et al., 2019).
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Michigan State University
IRB Approval Date
2026-03-10
IRB Approval Number
202500204
Analysis Plan

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