Barriers to retraining: Identifying motivation as a factor in the rural-urban AI-divide

Last registered on June 14, 2026
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Pre-Trial

Trial Information

General Information

Title
Barriers to retraining: Identifying motivation as a factor in the rural-urban AI-divide
RCT ID
AEARCTR-0018530
Initial registration date
May 01, 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
May 06, 2026, 10:59 AM EDT

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

Last updated
June 14, 2026, 3:39 PM EDT

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

Locations

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Primary Investigator

Name
Sandra Goff
Affiliation
Bates College
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Other Primary Investigator(s)

PI Name
Caroline Noblet
PI Affiliation
University of Maine
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Additional Trial Information

Status
In development
Start date
2026-06-14
End date
2026-06-26
Keywords
Behavior, Labor, Other
Additional Keywords
AI, job retraining, digital divide, AI-divide, rural-urban divide
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
A rural-urban digital divide persists in the United States, despite significant efforts and successes in closing the gap, exacerbating uneven access to education, employment, healthcare, and innovation. As artificial intelligence (AI) has become a larger factor in our daily lives, this digital divide has spilled over into a rural-urban AI-divide, with urban adults of working age using AI at twice the rate of their rural counterparts. But the AI-divide may not be fully explained by differential access. This work uses an experimental design with random assignment to identify motivational aspects of the rural-urban AI-divide. In the study, participants engage in two rounds of an online task. In the first round, participants learn how to use a Caesar cipher to encode a word. In the second round, participants are asked if they would like to continue to perform the Caesar cipher task or learn a new encoding skill and use this skill to complete a task that will earn them a higher piece rate per correctly answered question. The information provided about the new skill differs by randomly assigned experimental condition. In the no information condition, participants are only told they will learn a new encoding skill. In the coding skill condition, participants are told they will learn how to use computer programming for encoding. In the AI skill condition, participants are told they will learn an AI-related skill for encoding. In all three conditions, the skill and tasks are identical – participants learn how to use a simple Python script to encode words with a Caesar cipher. We also implement a version of this experiment that uses the same three conditions but does not allow participants to choose the task they complete in the second round.
After completing the second round, participants are asked to complete a survey about their AI experiences, attitudes, and beliefs. Finally, they answer a few sociodemographic questions.
This work explores whether take-up of the new skill in Round 2 differs by experimental condition and by rural residence. Our design also allows us to determine whether engaging with the AI-related skill alters perceptions of the benefits of AI and confidence in AI-skill ability.
External Link(s)

Registration Citation

Citation
Goff, Sandra and Caroline Noblet. 2026. "Barriers to retraining: Identifying motivation as a factor in the rural-urban AI-divide." AEA RCT Registry. June 14. https://doi.org/10.1257/rct.18530-2.1
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Experimental Details

Interventions

Intervention(s)
The primary intervention in this study is the introduction of an opportunity to learn a new skill with differing amounts of information about the skill provided to the participant. Additionally, in one version of the study participants get to choose whether to engage in the new task, while in the other version participants do not choose.
Intervention Start Date
2026-06-14
Intervention End Date
2026-06-26

Primary Outcomes

Primary Outcomes (end points)
Outcome 1: Acceptance of new learning opportunity
Outcome 2: AI attitudes/confidence
Outcome 3: Interest in learning new skill
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participant Recruitment:
The online survey experiment will be conducted via CloudResearch Connect. We intend to include 500 participants targeted from states which lead in both forest cover and rurality - Maine, Vermont, West Virginia, New Hampshire and Alabama - to allow for comparisons across rural, suburban, and urban populations.

Experimental Design Overview:
STEP 1: Consent
STEP 2: Participants complete a set of encoding tasks using a Caesar cipher.
STEP 3: Participants are then asked if they would like to learn a new task. The information provided about the new skill differs by randomly assigned experimental condition. In the no information condition, participants are only told they will learn a new encoding skill. In the coding skill condition, participants are told they will learn how to use Python for encoding. In the AI skill condition, participants are told they will learn an AI-related skill for encoding. In all three conditions, the actual task is the same – participants learn how to use a simple Python script to encode words with a Caesar cipher. We also implement a version that uses the same conditions but does not allow participants to choose the task they complete in the second round. This allows us to understand how engaging in the new task affects attitudes toward AI regardless of willingness to engage in the new skill.
STEP 4: Participants either continue to complete the initial task or the new task, depending on their choice
STEP 5: Participants complete a survey re: AI perceptions, confidence, etc.
Experimental Design Details
Not available
Randomization Method
Randomization to experimental condition is performed by the Qualtrics software
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 individuals
Sample size: planned number of observations
500 individuals
Sample size (or number of clusters) by treatment arms
100 per experimental condition in the choice version of the study
60 per experimental condition in the no choice version of the study
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See pre-analysis plan
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Bates College IRB
IRB Approval Date
2026-03-28
IRB Approval Number
EC3-26-14
Analysis Plan

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