Medical EducationAI-TUINANCT07767916
AI-Assisted Feedback for Learning Standardized Tuina Skills
A study testing AI-Supported Personalized Feedback, Sensor-Based Data Feedback and Traditional Instructor Feedback in people with Medical Education and Clinical Skills, run by Zhejiang University.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Medical Education, Clinical Skills
- What is being tested?
- AI-Supported Personalized Feedback, Sensor-Based Data Feedback and Traditional Instructor Feedback
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 81 participants
- Where
- No sites listed yet
- Study dates
- 13 Aug 2026to 1 Aug 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read more
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.
Read the 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.
- Conditions
- Medical Education, Clinical Skills
- Being tested
- AI-Supported Personalized Feedback, Sensor-Based Data Feedback, Traditional Instructor Feedback
- Keywords
- Artificial Intelligence, Generative Artificial Intelligence, Rehabilitation Education, Health Professions Education, Tuina, Manual Therapy Skills, Sensor-Based Feedback, Personalized Feedback, Deliberate Practice, Objective Structured Clinical Examination
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Supported Personalized FeedbackBehavioural
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.
Sensor-Based Data FeedbackBehavioural
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.
Traditional Instructor FeedbackBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| AI-Supported Personalized Feedback | Experimental | 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. Receives: Behavioral: AI-Supported Personalized Feedback |
| Sensor-Based Data Feedback | Active comparator | 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. Receives: Behavioral: Sensor-Based Data Feedback |
| Traditional Instructor Feedback | Active comparator | 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. Receives: Behavioral: Traditional Instructor Feedback |
What is being measured
The primary outcome is the main question the study is designed to answer.
Standardized Tuina Skill Performance Measured by Total OSCE Score
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.
Measured: Four weeks after completion of the training intervention
Secondary outcomes (8)
Immediate Standardized Tuina Skill Performance Measured by Total OSCE Score
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.
Measured: Immediately after completion of the training intervention
Absolute Error From the Target Force
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.
Measured: Baseline, immediately after the intervention, and four weeks after the intervention
Coefficient of Variation of Applied Force
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.
Measured: Baseline, immediately after the intervention, and four weeks after the intervention
Absolute Error From the Target Operating Frequency
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.
Measured: Baseline, immediately after the intervention, and four weeks after the intervention
Proportion of Practice Time Within the Target Performance Range
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.
Measured: Baseline, immediately after the intervention, and four weeks after the intervention
Performance on a Related Tuina Skill Transfer Task
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.
Measured: Immediately after the intervention and four weeks after the intervention
Number of Practice Attempts Required to Reach the Competency Standard
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.
Measured: During the four training sessions over two weeks
Proportion of AI-Generated Feedback Requiring Instructor Modification
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.
Measured: During the four training sessions over two weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Aug 2026
- Study start13 Aug 2026 (estimated)
- Primary completion1 May 2027 (estimated)
- Study completion1 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Medical EducationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Participants will be randomly assigned in a 1:1:1 ratio to one of three parallel educational intervention groups: AI-supported personalized feedback, sensor-based data feedback, or traditional instructor feedback. Participants will remain in their assigned group throughout the study, with no crossover between groups.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 81 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhejiang University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xing-Chen Zhou, First Affiliated Hospital of Zhejiang University
- Investigators
- Xing-Chen Zhou, Principal investigator, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Shangcheng District, Hangzhou, Zhejiang
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07767916
- Sponsor's protocol ID
- No. 2026JX0812
- Acronym
- AI-TUINA
- Status last verified
- Aug 2026
- First submitted
- 12 Aug 2026
- Last updated
- 17 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Study at a glance
From the registry record, last updated by the sponsor on 17 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effects of Sensor- and Generative AI-Supported Personalized Feedback on the Acquisition of Standardized Tuina Skills Among Rehabilitation Trainees: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07767916
- Acronym / sponsor ID
- AI-TUINA / No. 2026JX0812
- Conditions
- Medical Education, Clinical Skills
- Keywords
- Artificial Intelligence, Generative Artificial Intelligence, Rehabilitation Education, Health Professions Education, Tuina, Manual Therapy Skills, Sensor-Based Feedback, Personalized Feedback
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 81 participants (estimated)
- Lead sponsor
- Zhejiang UniversityUniversity, hospital or other
- Study start
- 13 Aug 2026 (estimated)
- Primary completion
- 1 May 2027 (estimated)
- Study completion
- 1 Aug 2028 (estimated)
- First posted
- 17 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- Model description
- Participants will be randomly assigned in a 1:1:1 ratio to one of three parallel educational intervention groups: AI-supported personalized feedback, sensor-based data feedback, or traditional instructor feedback. Participants will remain in their assigned group throughout the study, with no crossover between groups.
| Arm | Type | Receives |
|---|---|---|
| AI-Supported Personalized Feedback | Experimental | Behavioral: AI-Supported Personalized FeedbackParticipants 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. |
| Sensor-Based Data Feedback | Active comparator | Behavioral: Sensor-Based Data FeedbackParticipants 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. |
| Traditional Instructor Feedback | Active comparator | Behavioral: Traditional Instructor FeedbackParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Supported Personalized FeedbackParticipants 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. | Behavioural | AI-Supported Personalized Feedback |
| Sensor-Based Data FeedbackParticipants 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. | Behavioural | Sensor-Based Data Feedback |
| Traditional Instructor FeedbackParticipants 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. | Behavioural | Traditional Instructor Feedback |
Primary outcome
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.
8 secondary outcomes
Immediate Standardized Tuina Skill Performance Measured by Total OSCE Score
Time frame: 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
Coefficient of Variation of Applied Force
Time frame: 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
Proportion of Practice Time Within the Target Performance Range
Time frame: 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
Number of Practice Attempts Required to Reach the Competency Standard
Time frame: 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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Aug 2026.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Xing-Chen Zhou principal investigator
The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Shangcheng District, Hangzhou, Zhejiang
Questions about this trial
What is this clinical trial studying?
A study testing AI-Supported Personalized Feedback, Sensor-Based Data Feedback and Traditional Instructor Feedback in people with Medical Education and Clinical Skills, run by Zhejiang University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 17 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xing-Chen Zhou, Ph.D. (+86-18370133761, zhouxingchen0210@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Standardized Tuina Skill Performance Measured by Total OSCE Score (time frame: Four weeks after completion of the training intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Aug 2026.