Using Generative AI Well: AI Skill Training and School-to-Work Transitions

Last registered on February 12, 2026
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Pre-Trial

Trial Information

General Information

Title
Using Generative AI Well: AI Skill Training and School-to-Work Transitions
RCT ID
AEARCTR-0017760
Initial registration date
January 26, 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
January 28, 2026, 7:49 AM EST

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

Last updated
February 12, 2026, 4:15 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Switzerland
Region

Primary Investigator

Name
Anne Brenøe
Affiliation
University of Zurich
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Other Primary Investigator(s)

PI Name
Albert Thieme
PI Affiliation
University of Zurich
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Additional Trial Information

Status
On going
Start date
2026-01-26
End date
2028-12-31
Keywords
Education, Labor
Additional Keywords
Vocational education and training, apprenticeships, school-to-work transition, generative AI, job search
JEL code(s)
I21, J24, O33, C93
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates whether a brief training in responsible and effective use of generative AI tools improves students’ apprenticeship application materials, performance in mock interviews, and subsequent transitions after compulsory school in Switzerland. Schools are cluster-randomized to either receive a standard application training only (control) or to additionally receive a 90-minute generative AI workshop delivered before the standard training (treatment). Outcomes are measured via student surveys, standardized volunteer ratings of application materials and mock interviews, and linkages to education registries.
External Link(s)

Registration Citation

Citation
Brenøe, Anne and Albert Thieme. 2026. "Using Generative AI Well: AI Skill Training and School-to-Work Transitions." AEA RCT Registry. February 12. https://doi.org/10.1257/rct.17760-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Control: standard YES application training.
Treatment: 90-minute generative AI workshop delivered before the standard YES training (plus the standard training).
Intervention Start Date
2026-02-16
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
1) Quality of application materials (CV and motivation letter), based on standardized volunteer ratings.
2) Mock interview performance, based on standardized volunteer ratings.
3) Upper-secondary placement.
Primary Outcomes (explanation)
Upper-secondary placement (binary): Indicator equal to 1 if the student has a confirmed upper-secondary education solution after grade 9 (after grade 10 for students enrolled in grade 10 at baseline), and 0 otherwise. Confirmed upper-secondary education solution includes transition to the academic high school track (Gymnasium or Fachmittelschule) or into vocational education (EBA or EFZ).

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We implement a field experiment in collaboration with our project partner Young Enterprise Switzerland (YES). Our study population consists of students in the last two grades of lower secondary education as well as students in bridge programs after having completed lower secondary education. Recruitment of classes occurs via YES school registrations plus direct outreach by the research team. We randomize schools into control and treatment with 50% in each group in separate waves (the timing depends on when students fill out the baseline survey).

All participating classes receive a visit by a YES volunteer to cover their standard application training. Treatment classes additionally receive a 90-minute generative AI workshop before the standard application training.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
School
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
74 schools
Sample size: planned number of observations
2200 students in around 148 classes.
Sample size (or number of clusters) by treatment arms
37 schools control, 37 schools treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The study is designed as a cluster-randomized experiment at the school level. We plan to recruit approximately 74 schools with an average of 30 students per school (around 2,200 student observations in total). We consider two primary outcomes: (i) a standardized outcome and (ii) a binary indicator for which we assume a control-group mean of approximately 0.8. Schools are assigned to treatment and control using pairwise randomization at the cluster level. We assume a two-sided significance level of 5 percent, 80 percent power, intra-cluster correlation of 5 percent, and the inclusion of pair fixed effects and individual-level covariates that explain 30 percent of the between-school residual variance and 30 percent of the within-school variance. The minimum detectable effect size is around 0.156 standard deviations for the standardized outcome and 6.2 percentage points for the binary outcome.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Human Subjects Committee of the Faculty of Economics, Business Administration and Information Technology, University of Zurich.
IRB Approval Date
2025-10-07
IRB Approval Number
OEC IRB #2025-097
Analysis Plan

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