Human Reliance on AI Recommendations Under Time Constraints and Varying Financial Incentives

Last registered on September 15, 2026
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Pre-Trial

Trial Information

General Information

Title
Human Reliance on AI Recommendations Under Time Constraints and Varying Financial Incentives
RCT ID
AEARCTR-0018623
Initial registration date
August 11, 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
August 20, 2026, 8:31 AM EDT

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

Last updated
September 15, 2026, 1:33 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
Maxwell Chupak
Affiliation
Clemson University
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Other Primary Investigator(s)

PI Name
Michael Makowsky
PI Affiliation
Clemson University
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PI Name
Alexander Loeb
PI Affiliation
Clemson University
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Additional Trial Information

Status
On going
Start date
2026-08-13
End date
2026-10-07
Keywords
Behavior, Firms & Productivity, Governance, Lab, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This research studies how monetary penalties for inaccurate responses affect participants’ reliance on AI-generated recommendations. Participants complete a series of CAPTCHA-style object-counting tasks, with and without access to a pre-generated recommendation from an AI agent. The experiment permits within-participant comparisons of task attempts and accuracy across AI-available and AI-unavailable conditions. Participants are paid a piece-rate reward for each correctly completed task and are assigned to one of three treatment levels that differ in the penalty for incorrect responses. To study differences in AI reliance across participants, we elicit participants’ beliefs about the accuracy of the AI’s recommendations using an incentive-compatible quadratic scoring rule, as well as their beliefs about their own accuracy under both AI conditions. After participants have completed tasks under both conditions, we elicit their willingness to pay for access to the AI using a Becker-DeGroot-Marschak (1964) mechanism. Our main hypotheses are that (i) participants with AI access will attempt more tasks, (ii) willingness to pay for AI access will increase with the penalty for incorrect responses, and (iii) willingness to pay will be higher among participants who perceive a larger improvement in their probability of answering correctly when using AI. Participants are recruited via Prolific and complete the experiment on Qualtrics.
External Link(s)

Registration Citation

Citation
Chupak, Maxwell, Alexander Loeb and Michael Makowsky. 2026. "Human Reliance on AI Recommendations Under Time Constraints and Varying Financial Incentives." AEA RCT Registry. September 15. https://doi.org/10.1257/rct.18623-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
1. Participants complete three modules, each containing 30 CAPTCHA-style object-counting tasks.

2. Participants are randomly assigned to one of three financial incentive conditions that differ in the monetary penalty (relative to a constant piece-rate reward) for incorrect responses. AI recommendations are available in one of the first two modules and unavailable in the other; AI availability is varied within-participant and counterbalanced across participants. Access to AI recommendations in the third module is determined by a Becker-DeGroot- Marschak (BDM) mechanism eliciting each participant’s willingness to pay.

3. Following each module, participants are asked to estimate the number of tasks they believe they answered correctly. After completing their first module with AI access, participants are also asked to estimate the accuracy of the AI recommendations. Interactions with the AI button, including revealing and auto-filling the recommendation, are recorded to provide additional information on AI usage.
Intervention Start Date
2026-09-30
Intervention End Date
2026-10-07

Primary Outcomes

Primary Outcomes (end points)
For each participant, the primary outcomes are,

1. Task productivity and accuracy. The number of tasks attempted within the time limit and the number of tasks answered correctly in the AI-available and AI-unavailable modules, including differences across the three penalty conditions.

2. Willingness to pay for AI access. Participants’ willingness to pay for AI access in Module C.
Primary Outcomes (explanation)
1. Task productivity and accuracy. Task productivity is measured by the number of tasks attempted within the time limit, and accuracy is measured by the number of tasks answered correctly in the AI-available and AI-unavailable modules.

2. Willingness to pay. WTP is the participant’s maximum stated willingness to pay for AI access elicited using the Becker-DeGroot-Marschak mechanism in Module C.

Secondary Outcomes

Secondary Outcomes (end points)
Willingness To Pay (other measures): Measures of participants’ perceived and realized value of access to AI assistance.
Secondary Outcomes (explanation)
1. Perceived value of AI access. For each participant, perceived incremental value is constructed from the participant’s elicited beliefs about their probability of answering correctly with and without AI. Given the $0.25 reward for a correct response and the participant’s assigned penalty, perceived incremental value is the difference between the perceived expected payoff with AI and without AI.

2. Realized value of AI access. Realized incremental value is measured as the difference between the participant’s realized payoff in the AI-available module and their realized payoff in the AI-unavailable module.

Experimental Design

Experimental Design
Participants complete three modules of CAPTCHA-style object-counting tasks under time constraints. AI recommendations are available in one of the first two modules, with AI availability varied within participants and module order counterbalanced across partici-
pants.

Participants are randomly assigned to one of three financial incentive conditions that differ in the penalty for incorrect responses with the reward for correctly completed tasks held constant across all participants. In the third module, access to AI recommendations is determined through a Becker-DeGroot-Marschak (BDM) mechanism eliciting willingness to pay.

Participants also complete an incentivized elicitation of their beliefs about the AI’s accuracy once during the experiment and a non-incentivized elicitation of their beliefs about their own accuracy following each module. Earnings are determined by one randomly
selected module.
Experimental Design Details
Not available
Randomization Method
Randomization of the accuracy of the first two AI recommendations, module presentation order (excluding the final module), BDM price, and assignment to penalty level is conducted by the Qualtrics survey software.
Randomization Unit
Penalty level is randomized at the participant level. First task accuracy for modules with AI recommendations and module order across the experiment are randomized within participant.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable. Randomization occurs at the individual participant level.
Sample size: planned number of observations
400 individual participants. Up to 36,000 task-level observations (400 participants × 90 tasks), conditional on completion of all tasks..
Sample size (or number of clusters) by treatment arms
Approximately 133 participants per penalty treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power calculations are based on the primary outcome of the within-subject difference in accuracy between AI and non-AI modules. Using pilot data, the standard deviation of the participant-level difference in accuracy between AI and non-AI conditions is 0.138. With a planned sample of 400 participants, a two-sided significance level of 5\%, and 80\% power, the minimum detectable effect size is approximately 0.019 accuracy points, equivalent to a 1.9 percentage point change in task accuracy.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Clemson University Institutional Review Board
IRB Approval Date
2026-07-26
IRB Approval Number
IRB2026-0020
Analysis Plan