AI supported education technology and academic achievement of students

Last registered on September 06, 2021


Trial Information

General Information

AI supported education technology and academic achievement of students
Initial registration date
September 04, 2021

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
September 06, 2021, 2:18 PM EDT

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



Primary Investigator

Peking University

Other Primary Investigator(s)

PI Affiliation
Peking University
PI Affiliation
Peking University
PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This research targets on evaluating the causal impact of AI supported education technology on academic achievement of students, by conducting large-scale Randomized Controlled Trials (RCT) in multiple middle schools in various cities in China.
External Link(s)

Registration Citation

Liu, Chenran et al. 2021. "AI supported education technology and academic achievement of students." AEA RCT Registry. September 06.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Academic achievement of students measured by exam scores.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We first recruit middle schools to participate in our experiment around China, with the help of the iFLYTEK. In each recruited school, we then randomly several classes in the 7th grade into treatment group and some other classes in the same grade into control group.

For the treatment group, each student is arranged to use an AI learning device provided by the iFLYTEK to do math exercises. The device is designed to be capable of providing adaptive exercises to the user’s mastery of learning materials, and to help the user finally grasp all key points of the course. This feature is achieved by taking advantage of massive question database, AI algorithm and cloud computing technology. Except for this, all other schedule and arrangement of teaching and learning remain unchanged.

For the control group, we do not make any interferences.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
About 200 classes.
Sample size: planned number of observations
About 10000 students.
Sample size (or number of clusters) by treatment arms
100 classes control, 100 classes provided with the AI learning devices.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board, Guanghua School of Management, Peking University
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials