AI Match

Last registered on May 27, 2026
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Pre-Trial

Trial Information

General Information

Title
AI Match
RCT ID
AEARCTR-0018555
Initial registration date
May 18, 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 27, 2026, 10:03 AM EDT

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

Locations

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Primary Investigator

Name
Ben Weidmann
Affiliation
UCL
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Other Primary Investigator(s)

PI Name
Maria Del Rio-Chanona
PI Affiliation
UCL
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PI Name
Suphanit Piyapromdee
PI Affiliation
UCL
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Additional Trial Information

Status
In development
Start date
2026-06-01
End date
2027-03-01
Keywords
Education, Firms & Productivity, Lab
Additional Keywords
AI
JEL code(s)
J24, O33, C91, C92
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This laboratory experiment studies how individuals use generative AI tools to complete computer-based, cognitive tasks, and how the productivity effects of AI vary across users with different baseline skill profiles. Participants complete problems in two domains: 'analytical' and 'social'. The experiment has two phases. First, participants work without AI. Second, participants are randomly assigned according to a 2×2 design that crosses AI access (AI vs. no AI) with a brief intervention (practical AI training vs. active control). We measure task performance, beliefs about own performance, AI usage from chat logs, and a range of baseline cognitive and personality measures.
External Link(s)

Registration Citation

Citation
Del Rio-Chanona, Maria, Suphanit Piyapromdee and Ben Weidmann. 2026. "AI Match." AEA RCT Registry. May 27. https://doi.org/10.1257/rct.18555-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The study uses a 2×2 between-subjects design. The first intervention is access to a generative AI tool during the Phase 2 task block (AI vs. no AI). The second intervention, delivered between the Phase 1 (baseline) and Phase 2 (main experimental tasks) is a brief practical training session (vs. an active control activity of equivalent length and structure). All four cells (AI × training; AI × control; no-AI × training; no-AI × control) are equally populated, in expectation.
Intervention Start Date
2026-06-01
Intervention End Date
2026-10-01

Primary Outcomes

Primary Outcomes (end points)
Our key outcome variables are 'Phase 2 task performance'. In particular, we measure participant performance on an analytical task and a negotiation task.

Primary Outcomes (explanation)
The analytical performance measure involves data-analytic subtasks (computing descriptive statistics, generating plots, and interpreting numerical results).

The negotiation performance measure is individual points earned in the Phase 2 negotiation exercise — a points-based, multi-issue negotiation conducted via chat between paired participants and including zero-sum, integrative, and compatibility issues.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes are constructed primarily from two sources of chat data:
(a) the participant's interaction log with the AI tool
(b) the negotiation chat between paired participants
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
A laboratory experiment lasting approximately two hours. Participants first complete cognitive and personality measures, then complete baseline (Phase 1) versions of an analytical exercise and a negotiation exercise without AI, and report beliefs about their own performance. They are then randomly assigned to one cell of a 2×2 design (AI access × practical training/active control), receive their assigned training or control activity, and complete post-baseline (Phase 2) versions of both exercises under their assigned AI condition.

Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer. We use simple randomization. Each participant has a 25% chance of being assigned to each cell of the 2x2 design.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
800 individual participants
Sample size: planned number of observations
800
Sample size (or number of clusters) by treatment arms
We are using simple randomisation. In expectation, we are aiming for 200 participants in each of the 4 treatment arms.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
As we are using novel tasks (bespoke for this experiment) with unknown psychometric properties, we are unable to provide MDES.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Economics Local Research Ethics Committees at University College London
IRB Approval Date
2026-05-16
IRB Approval Number
4164
Analysis Plan