The use of AI in accounting tasks

Last registered on October 19, 2025
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Pre-Trial

Trial Information

General Information

Title
The use of AI in accounting tasks
RCT ID
AEARCTR-0016215
Initial registration date
June 20, 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
June 23, 2025, 12:08 PM EDT

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

Last updated
October 19, 2025, 2:17 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
SOFIA LOURENCO
Affiliation
ISEG, Universidade de Lisboa
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Other Primary Investigator(s)

PI Name
Januario Monteiro
PI Affiliation
University of East Anglia
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PI Name
Fabio Motoki
PI Affiliation
University of East Anglia
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PI Name
Laura Gomez-Ruiz
PI Affiliation
Universidad Pablo de Olavide
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PI Name
Pinar Guven-Uslu
PI Affiliation
University of East Anglia
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PI Name
Ricardo Malagueno
PI Affiliation
University of Essex
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Additional Trial Information

Status
In development
Start date
2025-06-27
End date
2026-05-31
Keywords
Firms & Productivity
Additional Keywords
Artificial Intelligence, Accounting, Task Performance, Task Complexity, Task Experience
JEL code(s)
M00, M41, M49, M53, O39
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Recent research shows that the use of AI can benefit performance for some tasks and be detrimental for other tasks. Additionally, research also suggests that AI can reduce or conversely increase the performance gap between heterogeneous participants.
This study explores whether and how the use of AI can enhance performance in accounting tasks. To this end we design a framed field experiment with accounting professionals where we vary the absence/presence of AI and the type of task. Our study also explores whether and how the use of AI varies with the characteristics of the participants, e.g. experience.
External Link(s)

Registration Citation

Citation
Gomez-Ruiz, Laura et al. 2025. "The use of AI in accounting tasks." AEA RCT Registry. October 19. https://doi.org/10.1257/rct.16215-1.1
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Experimental Details

Interventions

Intervention(s)
The participants of the experiment will perform accounting tasks online and answer a survey at the end. Randomly assigned participants will be provided with access to an AI platform to support their work in an accounting task, whereas the control group will not.
The control group will only have access to the AI platform after completing the post-experiment survey.
Intervention Start Date
2025-06-27
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
Accuracy – the number of correct answers per question/task
Reasoning – the length of the text and the quality of the arguments presented to support their opinion
Speed – the number of minutes/seconds spent in each question/task
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Use of AI – decision to use or not AI
Number of prompts in AI platform
Quality of prompts in AI platform
Time spent in the AI platform
Revision of answers by the control group once they have access to the AI platform
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is a 2x2 where we vary AI (absent vs present) and task type (less vs more complex).
After completing the task, participants answer a survey about their characteristics.
The control group will only have access to the AI platform after completing the post-experiment survey.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individuals
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
There is no clustering so the number of clusters is equal to the number of individuals.
Sample size: planned number of observations
240 individuals.
Sample size (or number of clusters) by treatment arms
60 individuals.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Essex, Essex Business School, Ethics Sub Committee 2
IRB Approval Date
2025-03-18
IRB Approval Number
ETH2425-0970
Analysis Plan