Real-Time AI Feedback to Enhance Teacher–Child Interactions: A Randomized Trial in Early Childhood Classrooms

Last registered on August 08, 2025
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Pre-Trial

Trial Information

General Information

Title
Real-Time AI Feedback to Enhance Teacher–Child Interactions: A Randomized Trial in Early Childhood Classrooms
RCT ID
AEARCTR-0016498
Initial registration date
August 04, 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
August 08, 2025, 6:54 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
Rebecca Royer
Affiliation
University of Chicago
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Other Primary Investigator(s)

PI Name
Dana Suskind
PI Affiliation
University of Chicago
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PI Name
John List
PI Affiliation
University of Chicago
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PI Name
Julie Pernaudet
PI Affiliation
University of Chicago
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PI Name
Ajay Sailopal
PI Affiliation
University of Chicago
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Additional Trial Information

Status
In development
Start date
2025-08-11
End date
2026-07-17
Keywords
Behavior, Education
Additional Keywords
JEL code(s)
C93, D83, I21, J13
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Socioeconomic disparities in child skills emerge rapidly during the critical years of brain development (0-5 years). Yet, research in the first few years of life is limited due to a lack of data from children's natural learning environments. In this study, we leverage Luet, an AI-driven wearable device paired with an app, to measure language engagements in children's natural early learning environments. We design a randomized controlled trial (RCT) with a sample of 30 daycare classrooms in which infants and toddlers wear Luet every school day for nine months. We randomly assign half of classrooms to the treatment group, which consists of early childhood curriculum, coaching, and real-time feedback to teachers about each child's language engagements in class. The other half of classrooms are assigned to a control group. In this pre-analysis plan, we outline the study design, research questions, and methods for this project.
External Link(s)

Registration Citation

Citation
List, John et al. 2025. "Real-Time AI Feedback to Enhance Teacher–Child Interactions: A Randomized Trial in Early Childhood Classrooms." AEA RCT Registry. August 08. https://doi.org/10.1257/rct.16498-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
In our study design, consented children in all classrooms will wear Luet throughout the nine-month period, and all teachers will have access to an app that provides real-time and historical data on each child's Luet usage. Each classroom will be assigned to either a physical development group or to a language development group. In the physical development group, teachers will receive information about physical development in early childhood. In the language development group, teachers will receive an intervention consisting of coaching, app feedback about language inputs, and an early childhood education curriculum.
Intervention Start Date
2025-09-01
Intervention End Date
2026-06-26

Primary Outcomes

Primary Outcomes (end points)
Our key outcomes consist of three categories: teacher beliefs (measured through SPEAK-CAT scores), teacher-child interactions (measured through conversational turn counts at the child level), and child outcomes (measured through the NIH Baby Toolbox and ROWPVT).
Primary Outcomes (explanation)
Conversational turn counts (CTCs) will be measured using AI algorithms paired with the AI wearable device "Luet" using standard counting procedures from the literature.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes for teacher beliefs include teacher beliefs about growth mindset, beliefs about AI, and teacher reports about their relationships with the children in class. Secondary outcomes for teacher-child interactions include adult word count measured at the child level. Secondary outcomes for child outcomes include MacArthur-Bates Communicative Development Inventories, ASQ:SE, and Woodcock Johnson-IV. Long term outcomes include K-12 academic and disciplinary records as well.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our primary research questions focus on how our intervention moves beliefs and behavior through the lens of the theory of change. To do so, we test for treatment effects first on teacher beliefs about child development, then on teacher behavior measured through teacher-child interactions, and finally on child outcomes. Secondary research questions include heterogeneity of treatment effects by classroom and child characteristics, changes to disparities in child development, changes in children's skills measured through other algorithms applied to the audio data, and spillovers to parental knowledge and investment.
Experimental Design Details
Not available
Randomization Method
We will use a rerandomization procedure (by a computer) combined with a staggered design.
Randomization Unit
Randomization is at the daycare center. We anticipate each center will have 1-3 classrooms in the study.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Estimated 16 centers and 30 classrooms.
Sample size: planned number of observations
Estimated 260 children in the study.
Sample size (or number of clusters) by treatment arms
8 centers in treatment and 8 centers in control (15 classrooms in each group).
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 Chicago Social & Behavioral Sciences (SBS) IRB
IRB Approval Date
2025-06-30
IRB Approval Number
IRB25-0419
Analysis Plan

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