New Technologies and Productivity: A Study with Vocational Students

Last registered on October 06, 2025
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Pre-Trial

Trial Information

General Information

Title
New Technologies and Productivity: A Study with Vocational Students
RCT ID
AEARCTR-0016936
Initial registration date
October 02, 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
October 06, 2025, 11:40 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
Hanna Brosch
Affiliation
Technical University of Munich
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Other Primary Investigator(s)

PI Name
Christina Langer
PI Affiliation
Stanford Digital Economy Lab
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PI Name
Philipp Lergetporer
PI Affiliation
Technical University of Munich
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PI Name
Harald Pfeifer
PI Affiliation
Federal Institute for Vocational Education and Training
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PI Name
Nele Tschöpe
PI Affiliation
Federal Institute for Vocational Education and Training
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Additional Trial Information

Status
In development
Start date
2025-10-06
End date
2026-12-31
Keywords
Firms & Productivity, Labor
Additional Keywords
artificial intelligence, apprenticeships, middle-skilled workers, inequality
JEL code(s)
I21, J24, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Artificial intelligence (AI) has the potential to transform the nature of work, yet little is known about how generative AI (GenAI) affects middle-skilled workers. Apprentices, who epitomize middle-skilled workers, provide an ideal setting to study these effects during the formative stage of skill development. In this study, we examine the impact of GenAI on apprentices’ productivity in Germany across three occupations: IT specialists for system integration, IT specialists for application development, and clerks in digitalization management. We design a randomized controlled trial (RCT) in which final-year apprentices are randomly assigned to either a treatment group with access to GenAI tools or a control group with no internet resources. All participants first solve a common baseline task, which allows us to compare performance across occupations and provides us with our own measure of individual ability. Subsequently, each occupation receives a middleand two high-skilled task specific to their field. The experiment evaluates whether GenAI enhances apprentices’ ability to complete middle- and especially higher-skilled tasks and whether it affects other task-related outcomes, like their understanding of the tasks at hand. In this pre-analysis plan, we present the study design, research questions, and empirical strategy for this project.
External Link(s)

Registration Citation

Citation
Brosch, Hanna et al. 2025. "New Technologies and Productivity: A Study with Vocational Students." AEA RCT Registry. October 06. https://doi.org/10.1257/rct.16936-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We conduct a RCT (lab-in-the-field experiment) in vocational training schools across Germany. The whole experiment takes place during regular school hours and lasts approximately 1.5 hours (equivalent to two school hours).

Apprentices are randomly assigned to either the treatment group or the control group with equal probability (p = 1/2). Randomization is implemented by drawing a lot, after which apprentices complete the experiment on a computer provided by the research team and work on tasks with different difficulty levels.
Intervention Start Date
2025-10-06
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
1. Productivity
(a) Task completion time
(b) Correctness of answers
2. Task comprehension
3. Positive task experience

Further, we plan to perform two main heterogeneities analysis by:
- AI Literacy
- Educational Attainment and Ability
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We may conduct additional exploratory heterogeneity analyses by apprentices’ effort they put into each task and their confidence about the correctness of their result.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experiment proceeds in the following steps:
1. Apprentices draw a lot to determine their treatment status.
2. Based on the draw, apprentices are assigned to a computer labeled with a number
indicating whether it belongs to the treatment or control group. Whenever capacity
allows, we separate treatment and control apprentices and let them participate in
the experiment in two different rooms to minimize spillovers.
3. Each apprentice then participates in the experiment on the computer, consisting of
the following components:
• Pre-survey, consisting of a demographic questionnaire (5 minutes)
• Baseline-task (10 minutes)
• Treatment information: apprentices in the treatment group are informed that
they will have access to ChatGPT for the subsequent tasks
• One medium- and two high-skilled tasks (20 minutes each), presented in randomized
order After each of the four tasks (baseline-task, medium- and two high-skilled),
apprentices answer questions about their task experience and respond to comprehension
questions
• Post-study questionnaire (10 minutes)
The treatment group receives access to ChatGPT (with access to GPT-4o and GPT-5). Treatment computers are pre-logged into ChatGPT Plus accounts. The control group does not have access to GenAI, and their computers are restricted from accessing the internet and any generative AI tools.
Experimental Design Details
Not available
Randomization Method
Randomization done by drawing a lot.
Randomization Unit
Individual level (student)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
800 apprentices (400 per treatment arm)
Sample size: planned number of observations
800 apprentices (400 per treatment arm)
Sample size (or number of clusters) by treatment arms
800 apprentices (400 per treatment arm)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethikkommission Technische Universität München
IRB Approval Date
2025-04-14
IRB Approval Number
2025-43-NM-BA
Analysis Plan

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