AI Mental Health Take-up and Social Demographic Background

Last registered on May 06, 2026
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Pre-Trial

Trial Information

General Information

Title
AI Mental Health Take-up and Social Demographic Background
RCT ID
AEARCTR-0018387
Initial registration date
May 02, 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, 11:21 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
Jiachuan Du
Affiliation
Peking University
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Other Primary Investigator(s)

PI Name
Juanjuan Meng
PI Affiliation
Peking University
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PI Name
Lata Gangadharan
PI Affiliation
Monash University
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PI Name
Yu Gao
PI Affiliation
Peking University
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PI Name
Leo Bao
PI Affiliation
Monash University
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Additional Trial Information

Status
In development
Start date
2026-05-02
End date
2026-08-02
Keywords
Behavior, Health
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines whether AI mental health services can reduce socioeconomic inequality in access to mental health support, relative to traditional human mental health services.

Mental health services are often underutilized, particularly among individuals from lower socioeconomic backgrounds. Financial constraints, stigma, accessibility barriers, and concerns about judgment may disproportionately affect these groups. AI-based counseling tools may lower some of these barriers by offering lower cost, greater immediacy, and perceived anonymity.

In an online experimental survey, participants are introduced to both AI-based and human-provided mental health services. We elicit their willingness to pay (WTP) using the Becker-DeGroot-Marschak (BDM) incentive-compatible mechanism and randomly implement real purchase opportunities for a subset of participants. The study collects detailed information on socioeconomic and demographic characteristics, prior AI usage, prior mental health help-seeking, and channel questions that explains take-up behavior.

In addition to immediate take-up decisions, we conduct a one-month follow-up survey to measure subsequent real-world (field) utilization of AI and human mental health services.

The primary objective is to assess whether the gap in demand and take-up between higher and lower socioeconomic groups is smaller for AI services than for human services. By combining BDM-based valuation with behavioral take-up decisions, the study aims to provide evidence on whether AI can reduce inequality in mental health support take-up.
External Link(s)

Registration Citation

Citation
Bao, Leo et al. 2026. "AI Mental Health Take-up and Social Demographic Background." AEA RCT Registry. May 06. https://doi.org/10.1257/rct.18387-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-05-02
Intervention End Date
2026-08-02

Primary Outcomes

Primary Outcomes (end points)
Willingness to pay (WTP) for AI mental health services.
Willingness to pay (WTP) for human mental health services.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants complete an online survey that includes mental health screening, socioeconomic and demographic characteristics, prior AI usage, prior mental health help-seeking, and channel questions that explains take-up behavior.

All participants are introduced to both an AI-based and a human-delivered counseling service, with order randomized. After each introduction, participants report their maximum willingness to pay for a 30-minute session using the Becker-DeGroot-Marschak (BDM) mechanism.

A subset of participants is randomly selected to receive a monetary endowment and is further randomly assigned to face a real purchase decision for either the AI or human service. A price is randomly drawn according to the BDM procedure and compared to the participant’s stated WTP to determine whether the service is provided.

Approximately one month after the experiment, all participants are invited to complete a follow-up survey. The follow-up survey measures subsequent real-world use of AI mental health services and human mental health services during the one month period.
Experimental Design Details
Not available
Randomization Method
Randomization is implemented using the built-in randomization and random number generation functions of the online survey platform.
Randomization Unit
Randomization occurs at the individual level for:
The order of service demonstration presentation (AI first vs. human first),
Assignment to receive a monetary endowment,
Assignment to AI versus human service for real implementation under the Becker-DeGroot-Marschak (BDM) mechanism, and
The random price draw in the BDM mechanism.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2,000 individuals.
(This number represents the anticipated maximum feasible sample size rather than a guaranteed enrollment, the achieved sample may be lower depending on field conditions. Power analysis based on pilot data shows that the minimum sample size required to detect the relevant effects with adequate power is roughly 1,800.)
Sample size: planned number of observations
2,000 individuals.
Sample size (or number of clusters) by treatment arms
1,000 assigned to AI BDM implementation; 1,000 assigned to human BDM implementation.
At recruitment, the sample was stratified to ensure balance across three demographic characteristics: gender (50% male, 50% female), hukou status (50% urban hukou, 50% rural hukou), and individual monthly income (50% RMB 5,000 or below, 50% above RMB 5,000).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Acceptance and Uptake of AI-Based Mental Health Support: A Randomized Experiment Using Incentive-Compatible Mechanisms
IRB Approval Date
2026-02-12
IRB Approval Number
2026-06
Analysis Plan