Social Class and Help-seeking Behavior from AI

Last registered on November 15, 2024

Pre-Trial

Trial Information

General Information

Title
Social Class and Help-seeking Behavior from AI
RCT ID
AEARCTR-0014719
Initial registration date
November 03, 2024

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
November 15, 2024, 2:05 PM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Houston

Other Primary Investigator(s)

PI Affiliation
Boston College
PI Affiliation
University of Houston

Additional Trial Information

Status
On going
Start date
2024-07-01
End date
2024-12-10
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Artificial Intelligence (AI) has introduced new opportunities for workers to resolve challenges when seeking help in the workplace. In this study, we aim to examine whether and how workers from lower, middle, and upper social classes influence their help-seeking behaviors from AI in the workplace. In response, this study will collect data on workers' help-seeking choices when facing difficulties in the workplace.
External Link(s)

Registration Citation

Citation
Li, Meng, Lai Wei and Yao Yao. 2024. "Social Class and Help-seeking Behavior from AI." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.14719-1.0
Experimental Details

Interventions

Intervention(s)
Participants are shown an image of a 10-rung social ladder and asked to reflect on their position relative to others. They are required to compare themselves with others based on their assigned treatment group.
Intervention (Hidden)
Participants are shown an image of a 10-rung social ladder and asked to reflect on their position relative to others. Participants assigned to the lower social class treatment arm are asked to compare themselves to people at the top of the social ladder, while those in the upper social class treatment arm are to compare themselves to people at the bottom of the ladder. Participants in the middle social class treatment arm do not engage in a comparison task.
Intervention Start Date
2024-07-01
Intervention End Date
2024-12-10

Primary Outcomes

Primary Outcomes (end points)
Help-seeking Choice
Primary Outcomes (explanation)
The help-seeking choice is a discrete variable including three possible choices, i.e., solve the question independently, seek help from the human, or seek help from AI.

Secondary Outcomes

Secondary Outcomes (end points)
Participant's Performance
Secondary Outcomes (explanation)
Participants' performance is measured by the number of correctly answered questions.

Experimental Design

Experimental Design
The participant is tasked with making the help-seeking choice when facing difficulty in different scenarios.
Experimental Design Details
In our experiment, we simulate a scenario where participants assume the role of junior workers in a consulting firm, tasked with providing solutions to address the targeted company's challenges.

In the experiment, following the social class manipulation, each participant is directed to introduction pages on help-seeking from the supervisor and AI. Then, a total of eight questions is shown sequentially, one from each difficulty group, ensuring that the average difficulty level of each question set is comparable across participants. Each question will be displayed for 30 seconds, during which participants can decide whether to answer independently, seek help from AI, or seek help from the supervisor.

For questions that participants choose to seek help from the supervisor, they need to communicate with the supervisor, who is framed as an expert in the business management consulting field. To do this, participants must first record and send a video to explain the question's context to the supervisor to seek help, then proceed to the answer submission page. For questions that participants choose to seek help from AI, they must come up with a prompt themselves and enter it on the webpage.

The participants are informed that if the supervisor decides to give advice after reviewing the video, he will give the correct answer to the question with an 80% probability while giving no advice/answer with a 20% probability; if the prompt to AI is effectively enough, AI will also generate the correct answer with an 80% probability while generating no advice/answer with a 20% probability.

In the experiment, we let participants first make help-seeking choices for all 8 questions, then show the response from the supervisor or AI per their choice. This approach prevents biases that could arise from learning and belief updates about the perceived capabilities of the supervisor and AI as participants progress through the question list.

After the help-seeking stage, participants will be directed to the answer submission page, where they need to submit each answer within 15 seconds. Finally, they finish a short follow-up questionnaire to conclude the experiment.
Randomization Method
Randomization based on computer programming.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
300-480 Individuals
Sample size: planned number of observations
2400-3840 Help-seeking Choices
Sample size (or number of clusters) by treatment arms
100-160 Individuals in Per Treatment Arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Houston
IRB Approval Date
2024-04-23
IRB Approval Number
STUDY00004669

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