Testing the Effects of AI Access on Parent Knowledge of Child Development

Last registered on May 06, 2026

View Trial History

Pre-Trial

Trial Information

General Information

Title

Testing the Effects of AI Access on Parent Knowledge of Child Development

RCT ID

AEARCTR-0018452

Initial registration date

May 01, 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 06, 2026, 11:02 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Rebecca Royer

Affiliation

University of Chicago

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Julie Pernaudet

PI Affiliation

University of Chicago

Contact Investigator

PI Name

Dana Suskind

PI Affiliation

University of Chicago

Contact Investigator

PI Name

John List

PI Affiliation

University of Chicago

Contact Investigator

PI Name

Dani Levine

PI Affiliation

University of Chicago

Contact Investigator

Additional Trial Information

Status

In development

Start date

2026-05-01

End date

2026-05-22

Keywords

Behavior, Education

Additional Keywords

artificial intelligence, early childhood, belief updating

JEL code(s)

J13, C91, D83

Secondary IDs

Prior work

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

Abstract

Caregiver knowledge of child development shapes early learning environments that drive long-term developmental outcomes. Artificial intelligence chatbots represent a potentially scalable tool for improving parent knowledge, though their effects remain untested. This pre-analysis plan describes a randomized experiment examining whether access to an AI chatbot improves parents' knowledge of early child development. We will recruit approximately 400 U.S. parents of children aged 0–6 from the online platform Prolific and randomly assign them to a treatment condition, in which they have access to an AI chatbot (Claude) while completing the Survey of Parent/Provider Expectations And Knowledge (SPEAK), or a control condition, in which they complete the SPEAK without access to the chatbot. Our primary outcome is SPEAK score. Secondary analyses will examine AI tool usage and query content as potential mediators, and will test for heterogeneity of treatment effects by parent socioeconomic status, race, gender, and AI perceptions.

External Link(s)

Registration Citation

Citation

Levine, Dani et al. 2026. "Testing the Effects of AI Access on Parent Knowledge of Child Development." AEA RCT Registry. May 06. https://doi.org/10.1257/rct.18452-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We will make public the intervention when the trial is complete.

Intervention (Hidden)

Participants in the treatment group will be given access to an artificial intelligence chatbot, Claude, during the SPEAK which we explain they can use as they please while answering the SPEAK items. We will provide these participants with a brief training on the tool to ensure they understand how to use it. Participants in the control group will not be given access to the chatbot.

Intervention Start Date

2026-05-01

Intervention End Date

2026-05-22

Primary Outcomes

Primary Outcomes (end points)

SPEAK score

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Heterogeneity by parent education, race, gender, age and AI perceptions. Mediation by quantity and quality of interactions with AI chatbot in the treatment group. See attached analysis plan for more details.

Secondary Outcomes (explanation)

Experimental Design

We will make public the experimental design when the trial is complete.

Experimental Design Details

All participants will be given the same external link on Prolific to complete the study. Using the link, all participants will answer the same set of demographic questions, complete the SPEAK, and then complete questions on their AI perceptions.

Participants in the treatment group will experience one additional feature. When treated participants reach the SPEAK, they will be given access to an artificial intelligence tool, Claude, which we explain they can use as they please while answering the SPEAK items. We will provide these participants with a brief training on the tool to ensure they understand how to use it.

We design the study such that the only difference between the control group and the treatment group is the AI tool. This design is an encouragement design, meaning treated participants may not use the AI tool. We record all messages between the participant and the tool during the study, so we can measure exactly how much each participant interacted with the tool and in what ways. It is also possible that control participants use an external AI tool to answer the SPEAK questions. While we cannot measure this perfectly, we will ask all participants at the end of the study whether they used any external tools while answering the survey questions, including AI.

Randomization Method

We rely on the randomization feature in Qualtrics to assign participants to treatment and control groups, ensuring an approximately even split.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

N/A

Sample size: planned number of observations

400 participants total.

Sample size (or number of clusters) by treatment arms

Approximately 200 participants in treatment and 200 participants in control.

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

Given 400 participants with half in treatment and half in control, our minimum detectable effect is 0.28 standard deviations with 80% power and alpha = 0.05.

Supporting Documents and Materials

IRB