Cultivating Agency in the AI Era: A Field Experiment in Middle Schools

Last registered on March 23, 2026
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Pre-Trial

Trial Information

General Information

Title
Cultivating Agency in the AI Era: A Field Experiment in Middle Schools
RCT ID
AEARCTR-0018139
Initial registration date
March 15, 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
March 23, 2026, 7:06 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
Zeyang Chen
Affiliation
Renmin University of China
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Other Primary Investigator(s)

PI Name
Yu-Jane Liu
PI Affiliation
Peking University
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PI Name
Jianhong Wang
PI Affiliation
Peking University; The University of Hong Kong
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PI Name
Tiantian Zhou
PI Affiliation
Peking University
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Additional Trial Information

Status
In development
Start date
2026-03-20
End date
2026-07-31
Keywords
Behavior, Education, Labor
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 a brief, school-based intervention can cultivate agency—the capacity to act with intention and purpose—among adolescents in the age of artificial intelligence. We conduct a randomized controlled trial in junior high schools in China, involving approximately 60 classrooms and 3,000 seventh and eighth-grade students. The intervention consists of a 45-minute curriculum module designed to shift students' worldview from passive AI users to active AI collaborators. Rather than teaching technical skills that may quickly become obsolete, the module focuses on cognitive reframing: helping students recognize AI as a tool to augment human capabilities rather than a competitor that will replace them. Key components include: Understanding what AI can and cannot do; Identifying uniquely human strengths (creativity, critical thinking, empathy); Developing a growth mindset toward technological change; Practicing purposeful collaboration with AI tools. We measure impacts on AI self-efficacy, technological anxiety, and domain-general agency using incentivized behavioral tasks and validated psychological scales. The research addresses a critical gap in AI literacy education by providing experimental evidence on whether foundational attitudes toward technology can be intentionally shaped during adolescence, a formative period for technological identity formation.
External Link(s)

Registration Citation

Citation
Chen, Zeyang et al. 2026. "Cultivating Agency in the AI Era: A Field Experiment in Middle Schools." AEA RCT Registry. March 23. https://doi.org/10.1257/rct.18139-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-03-23
Intervention End Date
2026-04-30

Primary Outcomes

Primary Outcomes (end points)
Incentivized outcomes:
(1) Fact-checking behavior in judging AI-generated contents
(2) Digging into the reasons when receiving AI direct answers
(3) Creativity with the AI-generated ideas alongside
(4) Describing contexts to LLMs

Non-incentivized outcomes:
(1) attitude towards AI
(2) attitudes towards the necessity of learning for humans

Exploratory Outcomes:
(1) Spillover effects on academic self-efficacy and study habits
(2) Heterogeneous effects by baseline STEM interest, gender, and socioeconomic status
(3) Long-term technology adoption patterns (measured at 3-month follow-up)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We employ a school-based randomized controlled trial with the following structure:
Setting: Public junior high schools (grades 7-8) in China. Schools represent diverse institutional contexts including high-performing and average institutions to enhance external validity.
Participant Recruitment: All students in selected classrooms are invited to participate. Students are informed that participation is voluntary and that all data will be kept confidential.
Baseline: Students complete a 30-minute computer-based survey during regular school hours, including: Demographic information; Baseline measures of AI attitudes and anxiety; Cognitive ability assessment; Prior technology use patterns
Intervention: Classrooms are randomly assigned to either: (1) Treatment: Receive a 45-minute "AI Agency" module delivered by their regular teacher (2) Control: Continue with standard curriculum (no AI-specific intervention)
The AI Agency module uses standardized, scripted materials (PPT slides, short video clips, discussion prompts) that teachers receive 48 hours before delivery. Teachers are instructed to follow the script closely to ensure fidelity and to record audio of the session for quality control. The module is designed to be culture-neutral and does not require teachers to have AI expertise.
Follow-up Assessments: (1) Immediate post-test: Administered within 48 hours of the intervention; (2) Three-month follow-up: Conducted during the same academic term to assess persistence of effects
Incentives: All outcome measurement tasks include real incentives. Students earn tokens exchangeable for school supplies (pencils, notebooks, etc.). Each incentivized question includes a random lottery where one student per classroom receives their earned reward. This ensures incentive compatibility while maintaining cost-effectiveness at scale.
Experimental Design Details
Not available
Randomization Method
Classrooms are first stratified by school and grade level. Within each stratum, classrooms are randomly assigned to treatment or control with equal probability (50/50), which is decided by random numbers generated by a computer.
Randomization Unit
Classroom (cluster)
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
60 classrooms across 3~4 junior middle schools
Sample size: planned number of observations
Students: Approximately 3000 students (50 students per classroom × 60 classrooms) Observations per student: - Baseline: 1 observation - Immediate post-test: 1 observation - Three-month follow-up: 1 observation Total planned observations: 9000 student-time observations (3000 students × 3 time points)
Sample size (or number of clusters) by treatment arms
Treatment Group (Anti-Fraud Module):
- 25 classrooms
- ~1,250 students (approximately 50 per classroom)
- Receive: 45-minute anti-fraud education module + all three assessments

Control Group (Standard Curriculum):
- 25 classrooms
- ~1,250 students (approximately 50 per classroom)
- Receive: No intervention + all three assessments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Faculty Research Committee, Faculty of Business and Economics, The University of Hong Kong
IRB Approval Date
2026-03-12
IRB Approval Number
N/A
Analysis Plan