AI-Assisted Parenting Guidance and Child Development: A Randomized Experiment in Rural China

Last registered on February 19, 2026

Pre-Trial

Trial Information

General Information

Title
AI-Assisted Parenting Guidance and Child Development: A Randomized Experiment in Rural China
RCT ID
AEARCTR-0017889
Initial registration date
February 13, 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
February 19, 2026, 7:16 AM EST

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

Locations

Region

Primary Investigator

Affiliation
Sun Yat-Sen University

Other Primary Investigator(s)

PI Affiliation
South China Normal University
PI Affiliation
Sun Yat-sen University
PI Affiliation
Southwestern University of Finance and Economics

Additional Trial Information

Status
On going
Start date
2025-09-08
End date
2026-03-10
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We present a pre-analysis plan for a cluster-randomized experiment that evaluates whether AI-assisted parenting guidance can improve parenting quality and child development during early adolescence. In a rural county in China, we randomly assign 78 primary-school classes (over 2,500 fourth- and fifth-grade children with parents at home from 10 schools) to one of three parental guidance arms: (i) a parenting guidebook adapted from UNICEF guidelines, (ii) an AI-assisted parenting chatbot built on a trained large language model that delivers real-time personalized advice, or (iii) a no-guidance control. In a cross-cutting design, parents within each class are further randomized to receive SMS messages conveying children's expressed expectation of their parents, the benefits of supportive parenting, or a perspective-taking reflection message. All information was sent to high- and low-message coverage classes. We examine the treatment effects on parenting styles and practices and on children's mental health and academic achievement. This study provides among the first experimental evidence on parenting interventions and on whether AI-enabled tools can enhance parenting quality and children's outcomes.
External Link(s)

Registration Citation

Citation
Chu, Pengfei et al. 2026. "AI-Assisted Parenting Guidance and Child Development: A Randomized Experiment in Rural China." AEA RCT Registry. February 19. https://doi.org/10.1257/rct.17889-1.0
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Experimental Details

Interventions

Intervention(s)
we recruited ten schools in a County in Hunan Province to participate in our experiment. Our baseline sample consists of over 3,500 fourth- and fifth-grade students, including 2,505 non-left-behind children from 78 classes in 10 public primary schools. Our interventions target parents of these 2,552 non-left-behind children. We also analyze spillover effects on left-behind children.


We use a two-level cross-randomization design. First, within each school-by-grade stratum, we randomly assigned classes to two parental guidance treatment groups or the control group. Second, within each class, we randomly assigned parents to one of three information nudge treatments or to a control. This design allows us to estimate the effects of each parental guidance treatment, each information treatment, and their interactions.


We design two parental guidance interventions: (i) \textit{parenting guidebook (PG)}. Parents received a guidebook on how to tackle parenting challenges. This guidebook, adapted from UNICEF's parenting education guidelines and tailored to the Chinese cultural context, includes practical examples related to ten key issues identified by local parents in a preliminary survey. (ii) \textit{AI-assisted parenting chatbot (AIPC)}. Parents were provided with an account to access our designed app, which is based on a trained large language model (Deepseek). This AI chatbot allows parents to acquire real-time, personalized advice for daily parenting challenges. The chatbot was trained using the same material as the PG intervention, enabling it to offer suggestions in line with UNICEF's guidelines. We assigned 26 classes to PG treatment groups and 26 classes to AIPC treatment groups. The remaining 26 classes were assigned as control classes (between-class controls) with no parenting guidance.



We then cross-randomized students across the three parental guidance
intervention arms and the information treatment arm. Each student (and their parents) was randomly assigned to one of three information treatment groups or to a control group: (i) \textit{Children's expressed needs}: Parents received information about children's expressed preferences for parental engagement, derived from the baseline survey. (ii) \textit{Benefits of supportive parenting}: Parents received information on the positive effects of patient and egalitarian parenting practices. (iii) \textit{Perspective-taking reflection}: Parents received a message encouraging recalling their own negative childhood experiences, such as corporal punishment. (iv) \textit{Control}: Parents in the control group received no information messages. We refer to this group as \textit{within-class controls}. All information was delivered to parents twice a month via SMS messages.
Intervention Start Date
2025-09-15
Intervention End Date
2026-01-31

Primary Outcomes

Primary Outcomes (end points)
Children's academic performance and mental health
Primary Outcomes (explanation)
Children's academic performance is measured by Chinese language and mathematics test scores. At the end of each school semester, each school administers an exam to assess students' academic performance. We obtain transcript data for each end-of-semester exam before and after the interventions from the Educational Bureau (The second-round test scores will be collected in early March 2026). We standardize the raw test scores in each school-grade cell according to the between-class control group to have a mean of 0 and a standard deviation of 1 by wave. As complementary measures, we also use parents' assessments of children's academic performance and parents' grade expectations.


Children's mental health is measured by the CES-D-10 scale. Students report how often, during the past week, they experienced each of ten feelings or behaviors (e.g., depressed mood, difficulty concentrating, poor sleep, loneliness). Each item is coded on a four-point frequency scale (0-3), with two positively worded items reverse-coded so that higher values consistently indicate worse mental health. We sum the ten items to obtain a total score ranging from 0 to 30. A score of 10 or above indicates an elevated risk of depression.

Secondary Outcomes

Secondary Outcomes (end points)
Intermediate outcomes: Parenting style and practice, Parent--child relationship quality, Parents' AI chatbot and guidebook usage, Parental educational inputs

Other outcomes include: (1) Socio-emotional skills captured by SDQ domains. (2) Non-cognitive skills including economic preferences, Big Five personality traits (BFI-10), self-control, misbehavior, and growth mindset. (3) Self-perceived creativity. (4) Peer networks measured by number of friends and perceived peer relationship quality.
Secondary Outcomes (explanation)
Intermediate outcomes include: (1) Parenting style and practice, assessed from both child and parent perspectives using items adapted from Maccoby and Martin (1983), including discipline strategies, rule enforcement, positive parenting, perceived efficacy, and parental empathy. (2) Parent-child relationship quality, assessed by frequency of conflicts reported by parents and children's reports of verbal abuse and physical punishment. (3) AI chatbot and guidebook usage, measured through app-recorded chatbot usage logs and a five-question knowledge test on guidebook content. (4) Parental educational inputs, measured by children's reports of time spent on various activities with parents and parents' reports of monthly educational expenditure.


We aggregate items within each family and standardize scores relative to the control group (mean 0, SD 1). We also construct Anderson (2008) standardized indices within each outcome family and adjust p-values using the Romano and Wolf (2005) stepdown procedure.

More details about intermediate and other outcome variables can be found in the pre-analysis document.

Experimental Design

Experimental Design
We implement a two-level cross-randomized cluster randomized controlled trial in a rural county in Hunan Province, China.

Level 1 (Class-level): 78 primary-school classes (grades 4-5) in 10 schools are stratified by school and grade, then randomly assigned with equal probability to one of three parental guidance arms: (i) Parenting Guidebook (PG, 26 classes), (ii) AI-Assisted Parenting Chatbot (AIPC, 26 classes), or (iii) No-guidance control (26 classes).

Level 2 (Individual-level, cross-randomized): Within each class, parents of non-left-behind children are further randomized to one of three SMS information nudge treatments or a within-class control. Classes are also randomly assigned to high-coverage or low-coverage information intensity. In high-coverage classes, parents are equally distributed across four groups (25% each). In low-coverage classes, 13% receive each message type and 61% serve as controls.

The three information nudge treatments are: (1) Children's expressed needs, (2) Benefits of supportive parenting, (3) Perspective-taking reflection.

Our primary analysis uses intention-to-treat (ITT) estimates with standard errors clustered at the class level.
Experimental Design Details
Not available
Randomization Method
Randomization was conducted by computer. Classes were stratified by school and grade, then randomly assigned to treatment arms using a computer-generated random sequence. Within each class, individual-level randomization to information nudge treatments was also conducted by computer, conditional on the class-level coverage assignment (high or low).
Randomization Unit
Two levels of randomization: (1) Class (classroom) level for the parental guidance intervention (PG, AIPC, or control) and for information coverage intensity (high vs. low). (2) Individual (student/parent) level for the information nudge treatment assignment within each class.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
78 classes in 10 primary schools
Sample size: planned number of observations
2,505 non-left-behind children (with at least one parent at home) from a total baseline sample of 3,552 fourth- and fifth-grade students
Sample size (or number of clusters) by treatment arms
Parental guidance arms (class-level): 26 classes PG treatment, 26 classes AIPC treatment, 26 classes control. Within each arm, 13 classes assigned to high-coverage information and 13 classes to low-coverage information. Information nudge arms (individual-level within each class): In high-coverage classes, 25% children's expressed needs, 25% benefits of supportive parenting, 25% perspective-taking reflection, 25% within-class control. In low-coverage classes, 13% each message type, 61% within-class control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
IRB Approvement
Document Type
irb_protocol
Document Description
IRB Approvement
File
IRB Approvement

MD5: 5ed78ac322ff941d1f9e475f220d026c

SHA1: b984bac5efa89322e26db47fb55815641194ad2d

Uploaded At: February 13, 2026

IRB

Institutional Review Boards (IRBs)

IRB Name
Research Ethics Committee of the China Center for Behavioral Economics and Finance (CCBEF)
IRB Approval Date
2025-05-01
IRB Approval Number
2025B05
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan for: AI-Assisted Parenting Guidance and Child Development: A Randomized Experiment in Rural China

MD5: c866e606028419e55735de154821a598

SHA1: 2ed94bdaa38d31d0550ceb804407bde4bd413a93

Uploaded At: February 13, 2026