AI in Higher Education: Experimental Insights into Motivational and Cognitive Barriers (Part 2)

Last registered on September 28, 2026
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Pre-Trial

Trial Information

General Information

Title
AI in Higher Education: Experimental Insights into Motivational and Cognitive Barriers (Part 2)
RCT ID
AEARCTR-0019803
Initial registration date
September 25, 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
September 28, 2026, 9:47 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
Bastian Westbrock
Affiliation
Swansea University
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Other Primary Investigator(s)

PI Name
Sarah Rezaei
PI Affiliation
Utrecht University
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Additional Trial Information

Status
In development
Start date
2026-09-30
End date
2030-09-01
Keywords
Education, Labor
Additional Keywords
Behavior, Education, AI use
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project investigates how generative AI can support—or disrupt—personalized learning, depending on students’ academic skills and perceptions of their own skills. Although AI-generated guidance has the potential to improve learning and performance, students may differ in their capacity to evaluate and use it critically; some may engage thoughtfully with AI suggestions, while others may rely on them uncritically.

Using field-based randomized controlled trials, the study compares students completing assessment tasks with and without AI support. It examines how AI assistance affects both actual performance and students’ perceptions of their performance, and whether these effects vary with academic ability, self-perceived ability, and perceived AI literacy.

The project seeks to identify the cognitive and behavioural barriers that prevent students from benefiting fully from generative AI. It will also explore whether these barriers differ across levels of academic ability, degrees of self-confidence, and subject areas. The findings will inform the design of educational practices and AI-based learning tools that help a broader range of students engage with AI critically and effectively.

The topic of this project is linked to an earlier pre-registered trial (https://www.socialscienceregistry.org/trials/15659). No data of that trial is used for the current project.
External Link(s)

Registration Citation

Citation
Rezaei, Sarah and Bastian Westbrock. 2026. "AI in Higher Education: Experimental Insights into Motivational and Cognitive Barriers (Part 2)." AEA RCT Registry. September 28. https://doi.org/10.1257/rct.19803-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Undergraduate students are randomly assigned to different versions of an summative assignment. Some versions include GenAI-generated answer suggestions below questions. The intervention is the provision of these AI-generated suggestions, enabling a comparison between students who receive this support and those who do not.
Intervention Start Date
2026-09-30
Intervention End Date
2030-09-01

Primary Outcomes

Primary Outcomes (end points)
• Academic performance: points earned as a proportion of the points available on questions eligible for AI support.
• Perceived usefulness: the student's rating of the AI suggestion among observations receiving support.
Primary Outcomes (explanation)
The primary outcome variables will be moderated, one-by-one, with the following pre-treatment moderators:
• Academic ability
• Self-perceived academic ability
• Perceived AI literacy

Secondary Outcomes

Secondary Outcomes (end points)
• The total assignment mark, including questions not eligible for AI support
• Assignment or item completion: Any text-based indicator of copying or engagement will be reported as exploratory
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This randomized controlled trial involves undergraduate students who are assigned different versions of an assignment. Some versions include AI-generated answer suggestions. Assignment data will be matched with administrative records on student prior academic performance to analyze heterogeneity in outcomes.
Experimental Design Details
Not available
Randomization Method
Treatment allocation is determined by the final digit of each student’s university-assigned student number.
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
no clustering
Sample size: planned number of observations
500 individual students
Sample size (or number of clusters) by treatment arms
250 students 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
ETHICAL COMMITTEE OF THE FACULTY OF LAW, ECONOMICS AND GOVERNANCE, UTRECHT UNIVERSITY
IRB Approval Date
2025-03-19
IRB Approval Number
25-001
Analysis Plan

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