AI-Assisted Feedback for Learning Standardized Tuina Skills (AI-TUINA)

August 12, 2026 updated by: Xing-Chen Zhou, Zhejiang University

Effects of Sensor- and Generative AI-Supported Personalized Feedback on the Acquisition of Standardized Tuina Skills Among Rehabilitation Trainees: A Randomized Controlled Trial

The goal of this educational study is to determine whether personalized feedback generated using sensor data and generative artificial intelligence (AI) can improve the learning of standardized Tuina skills among rehabilitation trainees. Tuina is a form of manual therapy that requires learners to control the location, force, rhythm, and consistency of their hand movements.

A total of 81 rehabilitation trainees will be randomly assigned to one of three training groups: AI-supported personalized feedback, sensor-based data feedback without AI-generated recommendations, or traditional instructor feedback. All groups will receive the same standardized demonstration, training tasks, practice duration, and number of practice sessions.

The main question is whether trainees receiving AI-supported personalized feedback achieve better retention of standardized Tuina skills four weeks after training. The researchers will also compare immediate skill performance, force and rhythm control, transfer of skills to a related task, learning efficiency, self-efficacy, cognitive load, satisfaction, and the safety and acceptability of AI-generated feedback.

Skill performance will be assessed using a blinded Objective Structured Clinical Examination (OSCE) and objective sensor-based measurements. The study activities will be conducted using a mechanical simulation model and a pressure-sensing system, rather than on patients.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Manual rehabilitation skills are complex sensorimotor skills that require learners to integrate anatomical localization, body mechanics, force control, rhythm, consistency, safety, and professional communication. Conventional instruction generally relies on teacher demonstration, observation, and verbal feedback. This approach may be limited by instructor availability and may not provide continuous and objective information about force and rhythm during each practice attempt.

Sensor-based training systems can quantify performance characteristics such as mean force, deviation from the target force, force variability, operating frequency, rhythm variability, and the proportion of time spent within the target range. However, novice learners may have difficulty converting numerical data and performance curves into specific strategies for improvement. Generative AI may help translate objective performance data into immediate, structured, and actionable feedback.

This is a single-center, prospective, assessor-masked, three-arm, parallel-group randomized educational study. Eighty-one rehabilitation trainees will be allocated in a 1:1:1 ratio to an AI-supported personalized feedback group, a sensor-based data feedback group, or a traditional instructor feedback group. All participants will receive the same instructional materials, standardized demonstration, safety instructions, practice tasks, number of practice opportunities, and total training time.

The AI-supported personalized feedback will follow a structured task-gap-action format. The feedback will identify the target skill, describe the difference between the trainee's measured performance and the predefined target, and provide specific recommendations for the next practice attempt. The AI system will receive only coded sensor measurements and predefined task standards. It will not receive participant names, student identification numbers, facial images, voices, patient information, or clinical records. AI-generated feedback will be restricted to educational use and will be subject to instructor oversight and predefined safety rules.

Assessments will be performed at baseline, immediately after the training intervention, and four weeks after training. The primary outcome is the total OSCE score four weeks after training, representing retention of the standardized Tuina skill. OSCE performances will be coded and evaluated independently by assessors who are unaware of group allocation. Objective sensor-based measures will be used to evaluate force accuracy, force variability, rhythm accuracy, rhythm stability, and the proportion of performance within the target range.

Secondary evaluations will include immediate OSCE performance, performance on a related transfer task, the number of practice attempts required to reach a predefined competency standard, learning self-efficacy, cognitive load, learning engagement, satisfaction, trust in AI feedback, instructor feedback time, and the frequency of AI-generated feedback requiring instructor modification or correction.

The study is an educational intervention and will not involve patient treatment, invasive procedures, biological specimen collection, or clinical decision-making. All research-related Tuina practice will be performed on a mechanical simulation model equipped with a pressure-sensing system.

Study Type

Interventional

Enrollment (Estimated)

81

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • Aged 18 years or older.
  • Currently enrolled in or receiving training in rehabilitation medicine, physical therapy, rehabilitation therapy, or a related health profession.
  • Has not previously achieved the predefined competency standard for the standardized Tuina skill evaluated in this study.
  • Able to understand the study instructions and independently complete the training tasks and study questionnaires.
  • Able and willing to attend all scheduled training sessions and the four-week follow-up assessment.
  • Willing to participate voluntarily and provide written informed consent.

Exclusion Criteria:

  • Current acute injury, clinically significant pain, or functional limitation involving the hand, wrist, upper limb, shoulder, neck, or lower back that may interfere with repeated manual skill practice.
  • Previous systematic training in the same standardized Tuina technique with performance at or above the predefined competency standard.
  • Any medical, physical, cognitive, or psychological condition that, in the investigator's judgment, may make participation unsafe or prevent valid completion of the study procedures.
  • Unable to understand the study procedures or provide informed consent.
  • Direct involvement in the design, randomization, intervention delivery, outcome assessment, data management, or statistical analysis of this study.
  • Concurrent participation in another training study that may substantially affect performance of the standardized Tuina skill.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-Supported Personalized Feedback
Participants will receive standardized Tuina instruction and practice using a mechanical simulation model equipped with a pressure-sensing system. After each practice attempt, participants will receive objective sensor measurements and personalized, actionable feedback generated by a generative AI system under instructor supervision.
Behavioral: AI-Supported Personalized Feedback
Participants will complete four standardized training sessions over two weeks, with each session lasting approximately 45 to 60 minutes. A pressure-sensing system will measure force accuracy, force variability, operating frequency, rhythm stability, and time within the target range. After each practice attempt, a generative AI system will provide feedback using a structured task-gap-action format. The feedback will describe the target task, identify differences between measured performance and the predefined standard, and recommend specific actions for the next practice attempt. AI output will be restricted to educational use and overseen by instructors.
Active Comparator: Sensor-Based Data Feedback
Participants will receive the same standardized Tuina instruction, practice tasks, number of practice opportunities, and training duration as the experimental group. After each practice attempt, participants will receive sensor-generated numerical measurements, performance curves, and target ranges, without AI-generated interpretation or personalized recommendations.
Behavioral: Sensor-Based Data Feedback
Participants will complete four standardized training sessions over two weeks, with each session lasting approximately 45 to 60 minutes. After each practice attempt, participants will view numerical sensor measurements, force-time curves, rhythm information, and predefined target ranges. No generative AI interpretation, personalized action plan, or AI-generated recommendation will be provided.
Active Comparator: Traditional Instructor Feedback
Participants will receive the same standardized Tuina instruction, practice tasks, number of practice opportunities, and training duration as the other groups. Feedback will be provided through conventional instructor observation and verbal guidance. Participants will not view sensor-generated performance data or AI-generated recommendations.
Behavioral: Traditional Instructor Feedback
Participants will complete four standardized training sessions over two weeks, with each session lasting approximately 45 to 60 minutes. Instructors will observe performance and provide conventional verbal feedback based on the standardized teaching protocol. Participants will not receive sensor-derived performance displays or AI-generated feedback.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Standardized Tuina Skill Performance Measured by Total OSCE Score
Time Frame: Four weeks after completion of the training intervention
Participants will complete a standardized Objective Structured Clinical Examination (OSCE) assessing anatomical localization, body mechanics, force control, rhythm, procedural consistency, safety, communication, and overall performance. Coded performance recordings will be independently scored by two assessors who are unaware of group allocation. The mean of the two assessors' total scores will be used for analysis. Higher scores indicate better standardized Tuina skill performance.
Four weeks after completion of the training intervention

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Immediate Standardized Tuina Skill Performance Measured by Total OSCE Score
Time Frame: Immediately after completion of the training intervention
Standardized Tuina skill performance will be independently evaluated by two masked assessors using the same OSCE scoring rubric as the primary outcome. The mean of the two assessors' total scores will be analyzed. Higher scores indicate better performance.
Immediately after completion of the training intervention
Absolute Error From the Target Force
Time Frame: Baseline, immediately after the intervention, and four weeks after the intervention
The pressure-sensing system will calculate the absolute difference, in newtons, between the participant's mean applied force and the predefined target force. A lower absolute error indicates greater force accuracy.
Baseline, immediately after the intervention, and four weeks after the intervention
Coefficient of Variation of Applied Force
Time Frame: Baseline, immediately after the intervention, and four weeks after the intervention
Force stability will be calculated as the standard deviation of applied force divided by the mean applied force and expressed as a percentage. A lower coefficient of variation indicates more stable force control.
Baseline, immediately after the intervention, and four weeks after the intervention
Absolute Error From the Target Operating Frequency
Time Frame: Baseline, immediately after the intervention, and four weeks after the intervention
The absolute difference, in hertz, between the participant's measured operating frequency and the predefined target frequency will be calculated. A lower value indicates greater rhythm accuracy.
Baseline, immediately after the intervention, and four weeks after the intervention
Proportion of Practice Time Within the Target Performance Range
Time Frame: Baseline, immediately after the intervention, and four weeks after the intervention
The pressure-sensing system will calculate the percentage of recorded practice time during which both applied force and operating frequency remain within the predefined acceptable target ranges. Values range from 0% to 100%, with higher percentages indicating better performance.
Baseline, immediately after the intervention, and four weeks after the intervention
Performance on a Related Tuina Skill Transfer Task
Time Frame: Immediately after the intervention and four weeks after the intervention
Participants will perform a related standardized Tuina task that was not practiced in the same form during training. Performance will be evaluated using a predefined transfer-task rubric by assessors unaware of group allocation. Higher scores indicate better transfer of the learned skill.
Immediately after the intervention and four weeks after the intervention
Number of Practice Attempts Required to Reach the Competency Standard
Time Frame: During the four training sessions over two weeks
The number of practice attempts required for a participant to first meet the predefined competency threshold will be recorded. A lower number of attempts indicates greater learning efficiency. Participants who do not reach the threshold will be recorded as not achieving competency during the intervention period.
During the four training sessions over two weeks
Proportion of AI-Generated Feedback Requiring Instructor Modification
Time Frame: During the four training sessions over two weeks
For participants in the AI-supported feedback arm, instructors will record whether each AI-generated feedback message is accepted without modification, modified for accuracy or clarity, or blocked because it is potentially inappropriate or unsafe. The outcome will be calculated as the percentage of AI-generated feedback messages requiring modification or blocking.
During the four training sessions over two weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Zhejiang University

Investigators

  • Principal Investigator: Xing-Chen Zhou, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Shangcheng District, Hangzhou, Zhejiang

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 13, 2026

Primary Completion (Estimated)

May 1, 2027

Study Completion (Estimated)

August 1, 2028

Study Registration Dates

First Submitted

August 12, 2026

First Submitted That Met QC Criteria

August 12, 2026

First Posted (Actual)

August 17, 2026

Study Record Updates

Last Update Posted (Actual)

August 17, 2026

Last Update Submitted That Met QC Criteria

August 12, 2026

Last Verified

August 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • No. 2026JX0812

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

De-identified individual participant data underlying the results reported in the primary publication will be considered for sharing. The shared dataset may include participant demographic and educational characteristics, randomized group assignment, assessment time points, OSCE scores, derived sensor-based performance measures, questionnaire scores, and adverse event data. Direct identifiers, the participant identification key, facial images, audio recordings, raw performance videos, identifiable free-text responses, and raw AI interaction logs will not be shared.

IPD Sharing Time Frame

Beginning 12 months after publication of the primary study results and remaining available for five years.

IPD Sharing Access Criteria

Data will be available to qualified researchers who submit a methodologically sound research proposal. Requests will be reviewed by the principal investigator and the responsible institutional research team. Approved requesters must sign a data use agreement, use the data only for the approved purpose, protect participant confidentiality, and agree not to attempt participant re-identification. Additional ethics approval may be required depending on the proposed use and applicable institutional policies.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ICF
  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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