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Gaps in Generative AI Adoption: Do Misperceptions about Others Matter?

Last registered on August 17, 2025
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Pre-Trial

Trial Information

General Information

Title
Gaps in Generative AI Adoption: Do Misperceptions about Others Matter?
RCT ID
AEARCTR-0015961
Initial registration date
July 10, 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
August 01, 2025, 10:05 AM EDT

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

Last updated
August 17, 2025, 10:03 AM EDT

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

Locations

Country
Australia
Region

Primary Investigator

Name
Yujiao Li
Affiliation
University of Technology Sydney
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Elif Incekara-Hafalir
PI Affiliation
University of Technology Sydney
Contact Investigator

Additional Trial Information

Status
In development
Start date
2025-08-18
End date
2025-12-31
Keywords
Behavior, Gender, Labor
Additional Keywords
Generative AI
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines whether misperceptions about others’ use of and attitudes toward generative artificial intelligence (AI) tools influence students’ own adoption—and whether these belief gaps differ by gender and other demographics. Research indicates that there is lower generative AI adoption among vulnerable populations in developed countries. Prior research also shows that women tend to use generative AI tools less than men, but the underlying causes of this gap remain unclear. One possible factor is that certain subgroups may underestimate how widely generative AI is used or valued by peers and employers. We conduct a two-stage online study with university students in Australia. The first stage survey measures actual generative AI use, personal attitudes, and perceptions about others, as well as collecting demographic data. The second stage tests impact of potential inaccurate beliefs and potential information treatment effects, which will be based on the first stage data.
External Link(s)

Registration Citation

Citation
Incekara-Hafalir, Elif and Yujiao Li. 2025. "Gaps in Generative AI Adoption: Do Misperceptions about Others Matter?." AEA RCT Registry. August 17. https://doi.org/10.1257/rct.15961-1.2
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2025-09-04
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
Measures of generative AI use: binary adoption indicator, weekly usage frequency, daily usage time
Second-order beliefs: perceived peer usage and attitudes, perceived attitudes of employers
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Experience with generative AI:
type of tasks, tools used, paid subscription

Attitudes toward generative AI:
moral attitudes, perceived benefits, direct utility benefit (enjoyable to use), direct utility cost (difficult to use), trust in accuracy, confidence in skills, patience with generative AI, concerns about using GenAI
Secondary Outcomes (explanation)
We include a list experiment to test whether social desirability bias influences responses to the moral attitude question.

Experimental Design

Experimental Design
A sample of university students in Australia are recruited through UTS Behavioural Lab to complete an online survey. Participants are asked to report their own use of generative AI tools, their personal attitudes toward generative AI, and their second-order beliefs. These beliefs include perceptions about their peers’ adoption of generative AI, peers’ moral attitudes toward generative AI, and employers’ attitudes toward generative AI in the labor market. Second-order beliefs are elicited by asking participants to guess the percentage of others who hold specific behaviors or views. In addition to a base payment, a small number of participants whose guesses are closest to the average values receive bonus payments. The survey concludes with basic demographic questions.

In the second-stage experiment, participants will be randomly assigned in a 1:1 ratio to either a treatment or a control group. The treatment group will receive corrective information (e.g., a bar graph summarising peer generative AI usage from stage 1) before completing outcome measuring questions. The control group will not receive any such information and will proceed directly to the same outcome tasks. This random assignment is designed to estimate the effect of belief correction on generative AI-related attitudes and willingness to pay. The second-stage experiment details will be based on the results of the first stage, and this preregistration will be updated accordingly before second-stage data collection.
Experimental Design Details
Randomization Method
Randomization done by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
200 individuals in the first stage. The number for the second stage will be based on the results of the first stage, and the preregistration will be updated accordingly before second-stage data collection.
Sample size: planned number of observations
200 individuals in the first stage. The number for the second stage will be based on the results of the first stage, and the preregistration will be updated accordingly before second-stage data collection.
Sample size (or number of clusters) by treatment arms
The first-stage sample includes 100 male and 100 female students. In the second stage, participants will be randomly assigned to treatment and control groups in equal proportions (1:1) and the information treatment details will be based on the first stage results. The preregistration will be updated before the treatment and data collection in the second stage.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
UTS HREC
IRB Approval Date
2025-06-22
IRB Approval Number
ETH23-8040
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials