Artificial Teaching at the Right Level: Evidence from Al-enhanced Learning Platform in Higher Education

Last registered on March 31, 2026
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Pre-Trial

Trial Information

General Information

Title
Artificial Teaching at the Right Level: Evidence from Al-enhanced Learning Platform in Higher Education
RCT ID
AEARCTR-0018206
Initial registration date
March 24, 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
March 31, 2026, 9:43 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
Justinas Grigaitis
Affiliation
University of Zurich
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Other Primary Investigator(s)

PI Name
Tabea Braun
PI Affiliation
University of Zurich
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Additional Trial Information

Status
In development
Start date
2026-03-25
End date
2026-06-04
Keywords
Behavior, Education, Labor
Additional Keywords
Artificial Intelligence
JEL code(s)
I24; I23; C93; D83; O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates how voluntary AI-assisted practice tools affect student outcomes in higher education. We run a field experiment in a mandatory economics course at a large research university. All enrolled students (target sample of approx. 550) are offered access to a course-specific online learning platform with AI-assisted self-study tools. Impact evaluation relies on variation in AI feature availability across parts of the course and individual-level randomization into alternative default AI tool configurations. Primary outcomes are exam performance as well as engagement with AI-tools and traditional course materials.
External Link(s)

Registration Citation

Citation
Braun, Tabea and Justinas Grigaitis. 2026. "Artificial Teaching at the Right Level: Evidence from Al-enhanced Learning Platform in Higher Education." AEA RCT Registry. March 31. https://doi.org/10.1257/rct.18206-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Provision of course-specific educational AI tools for voluntary AI-assisted study.
Intervention Start Date
2026-03-25
Intervention End Date
2026-06-04

Primary Outcomes

Primary Outcomes (end points)
Exam performance; Usage of digital learning platform; Self-reported and observed online usage of official study materials (lecture attendance, self-learning video watching, practice exercises, studying with peers); Endline course-specific knowledge quiz a week before the exam.
Primary Outcomes (explanation)
Platform usage will be analyzed in aggregate (total page visits) and at the tool level (e.g., chatbot, AI-generated quizzes, exercises with AI feedback).

Secondary Outcomes

Secondary Outcomes (end points)
Text data from student-AI interactions.
Secondary Outcomes (explanation)
Text analysis will be performed on all interactions with AI tools to study behavioral channels of the effects.

Experimental Design

Experimental Design
This study evaluates AI-assisted self-study tools in a mandatory undergraduate economics course at a Swiss research university. All enrolled students are offered voluntary access to a course-specific online learning platform. The platform contains multiple AI-assisted practice tools (e.g., chatbot and quizzes with AI feedback), which rely on official course materials (e.g., lecture slides and transcripts). The study uses two empirical components: (i) variation in AI feature availability throughout the semester, and (ii) individual-level randomization into alternative default AI tool configurations. Randomization is at the student level (no clustering). The target sample is approximately 584 enrolled students.
Experimental Design Details
Not available
Randomization Method
Treatment group is based on a random encrypted user ID (hash) assigned upon login.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
582 individuals
Sample size: planned number of observations
582 individuals
Sample size (or number of clusters) by treatment arms
294 individuals in the more difficult default AI content version; 288 individuals in the less difficult default AI content version.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
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
2026-03-23
IRB Approval Number
OEC IRB # 2026-029
Analysis Plan