AI, Automation, and Continuous Learning in Career Choices

Last registered on February 20, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
AI, Automation, and Continuous Learning in Career Choices
RCT ID
AEARCTR-0015382
Initial registration date
February 13, 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 20, 2025, 6:29 AM EST

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

Locations

Country
Switzerland
Region

Primary Investigator

Name
Christian Gschwendt
Affiliation
University of Bern
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Other Primary Investigator(s)

PI Name
Thea Zöllner
PI Affiliation
University of Bern
Contact Investigator

Additional Trial Information

Status
In development
Start date
2025-02-17
End date
2025-12-31
Keywords
Education, Gender, Labor
Additional Keywords
occupational choice, automation, AI, continuous learning
JEL code(s)
J16, J24, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
As generative AI (GAI) continues to reshape the labor market, it naturally affects students' occupational choice. Understanding how students navigate these evolving challenges is crucial for guiding future career decisions. While economic research highlights the influence of GAI on occupational search behavior, little is known about how students weigh GAI usage alongside key occupational attributes, including wage, automation risk, and continuous education requirements. Using a discrete choice experiment, we examine how GAI usage alongside i) the occupation's risk of automation, ii) continuing education needs and, iii) wage, shapes career preferences. Specifically, we investigate how occupational attributes affect the likelihood of an occupation being chosen and whether preferences depend on respondents’ characteristics.
External Link(s)

Registration Citation

Citation
Gschwendt, Christian and Thea Zöllner. 2025. "AI, Automation, and Continuous Learning in Career Choices ." AEA RCT Registry. February 20. https://doi.org/10.1257/rct.15382-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-02-17
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
The main dependent variable captures which of two alternatives a participant selects, with each alternative varying across four attributes. This variable is assigned a value of 1 for the chosen alternative and 0 for the unchosen one. Our focus is on how the four attributes—salary, GAI use, automation risk, and continuing education requirements—influence the decision between the two alternatives.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We estimate heterogeneous preferences among respondent subgroups. In particular, we are interested in heterogeneity by respondent characteristics, such as gender and educational path (VET vs general education). Further, we will run two-way interactions of the following attributes: i) automation risk and continuous education ii) automation risk and GAI-usage iii) GAI-usage and continuous education.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To analyze students' preferences for occupational attributes, we implement a discrete choice experiment into a yearly representative survey of Swiss students in their final years of compulsory education and early years of upper secondary education. This timing captures students before, during or after this important transition between education levels, which typically occurs around age 15. The students are asked about their preferences for i) the occupation's risk of automation, ii) GAI use at work, iii) continuing education needs and, iv) salary.
Experimental Design Details
Not available
Randomization Method
Randomization is done by computer, before sending out the invites to the survey.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4500 students
Sample size: planned number of observations
13500: 4500 individuals times 3 choice sets each
Sample size (or number of clusters) by treatment arms
1125 observations for each block of three choice sets (there are 12 different choice sets in total, each participant sees 3)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan