Chatbots as Idea Evaluation Sparring Partners

Last registered on October 13, 2025

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Pre-Trial

Trial Information

General Information

Title

Chatbots as Idea Evaluation Sparring Partners

RCT ID

AEARCTR-0016864

Initial registration date

September 24, 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

September 29, 2025, 10:55 AM EDT

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

Last updated

October 13, 2025, 5:35 AM EDT

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

Locations

Country

Austria

Region

Primary Investigator

Name

Julian Just

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2025-10-03

End date

2025-11-17

Keywords

Firms & Productivity

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Idea evaluation is inherently subjective, often limited by cognitive biases and incomplete information. This study explores how AI chatbots, adopting different conversational stances - such as supportive, critical, balanced perspectives - can broaden evaluators’ judgments. Through controlled interactions, we examine how these AI sparring partners influence assessment confidence, shifts in evaluation, and the overall quality of decisions. By analyzing participants’ personality traits, perceptions of the AI, and their reasoning processes, the research seeks to determine whether AI can effectively expand human perspectives in idea evaluation without distorting judgment. The findings aim to inform the design of human-AI collaboration systems for more informed and consistent decision-making.

External Link(s)

Registration Citation

Citation

Just, Julian. 2025. "Chatbots as Idea Evaluation Sparring Partners." AEA RCT Registry. October 13. https://doi.org/10.1257/rct.16864-1.2

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2025-10-03

Intervention End Date

2025-11-17

Primary Outcomes

Primary Outcomes (end points)

The key outcome variables in this experiment are idea assessment, assessment confidence, assessment change before and after AI interaction, assessment quality change measured through metadelta, and the convergence of evaluations across participants. Additionally, perceptions of the AI chatbot and unstructured data from prompts and rationales are collected to understand how AI stances influence reasoning and trust.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We have an experiment with 8 conditions testing how AI sparring partners influence idea evaluation. We may focus
Participants are randomly assigned to one of these setups:

With pre-assessment: Evaluate ideas first, then chat with AI, then re-evaluate. (currently planned as main focus)
Without pre-assessment: Chat with AI first, then evaluate ideas for the first time.

The AI takes one of 4 roles, controlled by a custom GPT system prompt:

Basic (plain/default)
Angel’s Advocate (supportive)
Devil’s Advocate (critical)
Dialectic (balanced debate)

The AI chat is embedded in Qualtrics - participants move seamlessly between survey questions and the GPT conversation.
All conditions measure:
Holistic and detailed idea assessments
Confidence in assessments
Changes in evaluation scores
Alignment with group averages (metadelta)
Convergence of opinions
Personality traits, AI perceptions, and chat transcripts (prompts/rationales).

Experimental Design Details

Randomization Method

Randomization done in office by a computer - Qualtrics

Randomization Unit

The unit of randomization is the individual participant.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

800 evaluations of domain-experts (working in specific field where the to be evalauted idea is from) on Prolific, 150 evalaution of business students - each participant evaluates one idea

Sample size (or number of clusters) by treatment arms

800 prolific experts, 150 students; may be lower if we include some conditions only in a specific sample

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

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