Generative Artificial IntelligenceNCT07189611
Implementation of a Blended Online and Offline Teaching Model
A study testing A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence in people with Generative Artificial Intelligence, run by Hengxu Wang.
Trial summary
This study aims to design, implement, and evaluate a blended online and offline teaching model for Internal Medicine Nursing, integrating generative artificial intelligence (GAI), a virtual simulation platform, card-based exercises, and scenario simulation. Read more about this study
- Who is it for?
- Ages 18 to 25 yearsHealthy volunteers can take part
- Conditions the study is for
- Generative Artificial Intelligence
- What is being tested?
- A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 600 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2026to 1 Jun 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 to 25 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- Nursing major students;
- Four-year undergraduate students.
Who cannot join
- Students who drop out midway;
- Students whose absences accumulate to exceed 30% of the total class hours.
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: A Study Protocol for Implementing a Blended Online and Offline Teaching Model Based on Generative Artificial Intelligence in the Practical Teaching of Internal Medicine Nursing: a Mixed-methods Study
This study aims to design, implement, and evaluate a blended online and offline teaching model for Internal Medicine Nursing, integrating generative artificial intelligence (GAI), a virtual simulation platform, card-based exercises, and scenario simulation. The objective is to address key limitations of traditional teaching, including low student engagement, insufficient cultivation of clinical thinking, limited personalized learning, and a disconnect between theory and practice.
A mixed-methods approach will be used. All undergraduate nursing students from the 2024 cohort at Changsha Medical University will be enrolled via convenience sampling as the experimental group to receive the new blended model. The 2023 cohort will serve as the control group, receiving traditional teaching. Quantitative data (course grades, satisfaction questionnaires) and qualitative data (semi-structured interviews) will be collected to comprehensively evaluate the model's effectiveness.
Read more
Expected outcomes include improved student mastery of theoretical knowledge, enhanced practical skills and clinical thinking, increased learning interest, and higher teaching satisfaction. The study intends to provide a replicable, scalable innovative solution for nursing education reform, ultimately contributing to the training of high-quality applied nursing talents.
Key problems addressed:
Overcoming single-method teaching and poor interaction through GAI and gamification.
Enhancing clinical thinking and decision-making via dynamic GAI cases and card-based exercises.
Providing personalized learning paths and instant feedback using GAI technology.
Bridging the theory-practice gap with high-fidelity virtual and scenario simulations.
Implementing a multi-dimensional evaluation system beyond final exams to assess comprehensive student abilities.
Read the detailed description
This study protocol describes the development, implementation, and evaluation of a blended online and offline teaching model integrated with generative artificial intelligence (GAI) for practical teaching in Internal Medicine Nursing. The model combines a GAI-optimized clinical case library, a virtual simulation platform, card-based desktop exercises, and scenario simulation teaching.
The clinical case library will be developed using GAI to generate progressive, multi-stage cases reflecting real clinical progression (e.g., from COPD to Cor Pulmonale), each containing 2-3 stages designed to train clinical reasoning and decision-making. Online teaching resources will include a Learning Terminal-based course covering nine internal medicine systems, with electronic courseware, assessments, and discussion forums. The existing virtual simulation platform will be enhanced with a GAI-based Q\&A assistant to support knowledge acquisition and operational training. Dedicated online learning groups will facilitate communication.
Offline teaching will incorporate card-based desktop exercises and high-fidelity scenario simulations. The card game includes five card types: Patient Information, Nursing Goal, Nursing Intervention, Emergency Situation, and Assessment \& Feedback. Scenarios are derived from the GAI case library and involve standardized patients and high-fidelity simulators to replicate clinical environments.
The model will be implemented using a mixed-methods design. The experimental group (2024 undergraduate nursing cohort) will receive the blended model, while the control group (2023 cohort) will receive traditional teaching. Evaluation includes quantitative metrics (theory and practical exam scores, teaching satisfaction surveys) and qualitative methods (semi-structured interviews with the experimental group). Course scores are weighted 60% for theory and 40% for practical skills, the latter comprising case analysis, emergency drills, virtual simulation performance, and online course results. A multidimensional evaluation mechanism involving students, teachers, and expert supervisors will be established.
The teaching team consists of 8 full-time instructors, 4 clinical teachers, and 4 training center staff. Lessons learned from the mixed-methods evaluation will be used to refine and promote the teaching model.
- Conditions
- Generative Artificial Intelligence
- Being tested
- A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence
- Keywords
- generative artificial intelligence, practical teaching, Internal Medicine Nursing, blended online and offline teaching model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligenceBehavioural
This study will employ a convergent mixed-methods design. Participants will be convenience-sampled undergraduate nursing students from the 2024 cohort (intervention group) and the 2023 cohort (control group) at Changsha Medical University. The intervention group will experience the new blended model, which includes: 1) Optimizing a GAI-assisted clinical case library with progressive scenarios; 2) Utilizing online resources (Learning Terminal platform, virtual simulation experiments with an AI assistant); 3) Engaging in offline interactive sessions (card-based desktop deduction games and scenario simulations). The control group will receive traditional teaching methods. Quantitative data will include course scores (theoretical knowledge and practical skills) and teaching satisfaction questionnaires. Qualitative data will be collected via semi-structured interviews to explore students' experiences deeply.
| Study group | Role | What they receive |
|---|---|---|
| Experimental group | Experimental | (2) Experimental Group Teaching Implementation Process: a blended online and offline teaching model based on generative artificial intelligence
① Pre-class Preview: Students join the teaching QQ group and Learning Terminal group before class, complete the learning of online resources on the Learning Terminal platform, and perform virtual simulation experiments. ② In-class Implementation: Teaching is conducted in small groups. Each class is divided into 4 small groups, with 4-5 students forming one team for card-based desktop exercise teaching and scenario simulation teaching, each session lasting 2 class hours.③ Post-class Review: Students use generative AI (Deepseek) for knowledge consolidation and to access new technologies and research advancements related to the course content. Receives: Behavioral: A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Course Scores
The total course score is a composite measure evaluating overall academic performance. It comprises two components: a theoretical knowledge score (assessed via a closed-book examination, scored out of 100 points) and a practical skill assessment score (evaluated through case analysis, emergency drill simulations, virtual simulation performance, and online course participation, each contributing 20% to the practical score, which is also scaled to 100 points). The final total course score is calculated by weighting the theoretical score at 60% and the practical score at 40%, resulting in a composite value out of 100.
Measured: At the end of the 6-month course.
Teaching Satisfaction Score
Teaching satisfaction will be measured using a validated evaluation questionnaire developed based on a review of relevant literature, research group discussions, and consultation with nursing education experts. The questionnaire produces a quantitative satisfaction score.
Measured: At the end of the 6-month course.
Secondary outcomes (2)
Online and Offline Teaching Effect Evaluation
A multidimensional teaching effectiveness score will be calculated based on a digital combined evaluation system. This system incorporates standardized quantitative evaluations from three sources: student self-assessment (using a structured rating scale), teacher and small team self-assessment (using a structured rating scale), and expert supervision group assessment (using a structured observation rubric). These assessments are designed to use a common unit of measure (points on a standardized scale). The scores from these three components will be aggregated using a pre-defined weighting formula to produce a single composite teaching effectiveness score.
Measured: At the end of the 6-month course.
Qualitative Interviews
Thematic analysis of semi-structured individual interviews conducted with participants from the experimental group. This qualitative measure will identify, analyze, and report patterns (themes) concerning the perceived effectiveness, learning experiences, strengths, and limitations of the blended teaching model. The results will be presented as a summary of key themes and insights, not as a numerical score.
Measured: Within one month after completion of the 6-month course.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Sep 2025
- Study start1 Jan 2026 (estimated)
- Primary completion1 Jan 2028 (estimated)
- Study completion1 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Generative Artificial IntelligenceStudies 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
- Not applicable
- Intervention model
- Sequential
- Masking
- None (open label)
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hengxu Wang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Hengxu Wang, Changsha Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07189611
- Sponsor's protocol ID
- X2025046
- Status last verified
- Sep 2025
- First submitted
- 27 Aug 2025
- Last updated
- 24 Sep 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Protection of Participant Privacy and Confidentiality: Our primary ethical obligation is to protect the privacy and confidentiality of our student participants. Informed Consent Limitations: The informed consent process for this study, approved by the institutional ethics review board, explicitly outlines how the collected data will be used (i.e., for aggregate analysis to evaluate teaching effectiveness within the confines of this research project). Nature of the Data and High Risk of Re-identification: The qualitative interview data are particularly sensitive.
Study at a glance
From the registry record, last updated by the sponsor on 24 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study Protocol for Implementing a Blended Online and Offline Teaching Model Based on Generative Artificial Intelligence in the Practical Teaching of Internal Medicine Nursing: a Mixed-methods Study
- ClinicalTrials.gov ID
- NCT07189611
- Acronym / sponsor ID
- X2025046
- Conditions
- Generative Artificial Intelligence
- Keywords
- generative artificial intelligence, practical teaching, Internal Medicine Nursing, blended online and offline teaching model
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Hengxu WangUniversity, hospital or other
- Study start
- 1 Jan 2026 (estimated)
- Primary completion
- 1 Jan 2028 (estimated)
- Study completion
- 1 Jun 2028 (estimated)
- First posted
- 24 Sep 2025 (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.
This study aims to design, implement, and evaluate a blended online and offline teaching model for Internal Medicine Nursing, integrating generative artificial intelligence (GAI), a virtual simulation platform, card-based exercises, and scenario simulation. The objective is to address key limitations of traditional teaching, including low student engagement, insufficient cultivation of clinical thinking, limited personalized learning, and a disconnect between theory and practice.
A mixed-methods approach will be used. All undergraduate nursing students from the 2024 cohort at Changsha Medical University will be enrolled via convenience sampling as the experimental group to receive the new blended model. The 2023 cohort will serve as the control group, receiving traditional teaching. Quantitative data (course grades, satisfaction questionnaires) and qualitative data (semi-structured interviews) will be collected to comprehensively evaluate the model's effectiveness.
Expected outcomes include improved student mastery of theoretical knowledge, enhanced practical skills and clinical thinking, increased learning interest, and higher teaching satisfaction. The study intends to provide a replicable, scalable innovative solution for nursing education reform, ultimately contributing to the training of high-quality applied nursing talents.
Key problems addressed:
Overcoming single-method teaching and poor interaction through GAI and gamification.
Enhancing clinical thinking and decision-making via dynamic GAI cases and card-based exercises.
Providing personalized learning paths and instant feedback using GAI technology.
Bridging the theory-practice gap with high-fidelity virtual and scenario simulations.
Implementing a multi-dimensional evaluation system beyond final exams to assess comprehensive student abilities.
Detailed description
This study protocol describes the development, implementation, and evaluation of a blended online and offline teaching model integrated with generative artificial intelligence (GAI) for practical teaching in Internal Medicine Nursing. The model combines a GAI-optimized clinical case library, a virtual simulation platform, card-based desktop exercises, and scenario simulation teaching.
The clinical case library will be developed using GAI to generate progressive, multi-stage cases reflecting real clinical progression (e.g., from COPD to Cor Pulmonale), each containing 2-3 stages designed to train clinical reasoning and decision-making. Online teaching resources will include a Learning Terminal-based course covering nine internal medicine systems, with electronic courseware, assessments, and discussion forums. The existing virtual simulation platform will be enhanced with a GAI-based Q\&A assistant to support knowledge acquisition and operational training. Dedicated online learning groups will facilitate communication.
Offline teaching will incorporate card-based desktop exercises and high-fidelity scenario simulations. The card game includes five card types: Patient Information, Nursing Goal, Nursing Intervention, Emergency Situation, and Assessment \& Feedback. Scenarios are derived from the GAI case library and involve standardized patients and high-fidelity simulators to replicate clinical environments.
The model will be implemented using a mixed-methods design. The experimental group (2024 undergraduate nursing cohort) will receive the blended model, while the control group (2023 cohort) will receive traditional teaching. Evaluation includes quantitative metrics (theory and practical exam scores, teaching satisfaction surveys) and qualitative methods (semi-structured interviews with the experimental group). Course scores are weighted 60% for theory and 40% for practical skills, the latter comprising case analysis, emergency drills, virtual simulation performance, and online course results. A multidimensional evaluation mechanism involving students, teachers, and expert supervisors will be established.
The teaching team consists of 8 full-time instructors, 4 clinical teachers, and 4 training center staff. Lessons learned from the mixed-methods evaluation will be used to refine and promote the teaching model.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Experimental group | Experimental | Behavioral: A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence(2) Experimental Group Teaching Implementation Process: a blended online and offline teaching model based on generative artificial intelligence ① Pre-class Preview: Students join the teaching QQ group and Learning Terminal group before class, complete the learning of online resources on the Learning Terminal platform, and perform virtual simulation experiments. ② In-class Implementation: Teaching is conducted in small groups. Each class is divided into 4 small groups, with 4-5 students forming one team for card-based desktop exercise teaching and scenario simulation teaching, each session lasting 2 class hours.③ Post-class Review: Students use generative AI (Deepseek) for knowledge consolidation and to access new technologies and research advancements related to the course content. |
Who may qualify
- Ages
- 18 to 25 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (2 listed)
- Nursing major students;
- Four-year undergraduate students.
Key exclusion criteria (2 listed)
- Students who drop out midway;
- Students whose absences accumulate to exceed 30% of the total class hours.
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 |
|---|---|---|
| A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligenceThis study will employ a convergent mixed-methods design. Participants will be convenience-sampled undergraduate nursing students from the 2024 cohort (intervention group) and the 2023 cohort (control group) at Changsha Medical University. The intervention group will experience the new blended model, which includes: 1) Optimizing a GAI-assisted clinical case library with progressive scenarios; 2) Utilizing online resources (Learning Terminal platform, virtual simulation experiments with an AI assistant); 3) Engaging in offline interactive sessions (card-based desktop deduction games and scenario simulations). The control group will receive traditional teaching methods. Quantitative data will include course scores (theoretical knowledge and practical skills) and teaching satisfaction questionnaires. Qualitative data will be collected via semi-structured interviews to explore students' experiences deeply. | Behavioural | Experimental group |
Primary outcome
Course Scores
Time frame: At the end of the 6-month course.
The total course score is a composite measure evaluating overall academic performance. It comprises two components: a theoretical knowledge score (assessed via a closed-book examination, scored out of 100 points) and a practical skill assessment score (evaluated through case analysis, emergency drill simulations, virtual simulation performance, and online course participation, each contributing 20% to the practical score, which is also scaled to 100 points). The final total course score is calculated by weighting the theoretical score at 60% and the practical score at 40%, resulting in a composite value out of 100.
Teaching Satisfaction Score
Time frame: At the end of the 6-month course.
Teaching satisfaction will be measured using a validated evaluation questionnaire developed based on a review of relevant literature, research group discussions, and consultation with nursing education experts. The questionnaire produces a quantitative satisfaction score.
2 secondary outcomes
Online and Offline Teaching Effect Evaluation
Time frame: At the end of the 6-month course.
Qualitative Interviews
Time frame: Within one month after completion of the 6-month course.
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 24 Sep 2025.
- Individual participant dataSharing planned: No. Protection of Participant Privacy and Confidentiality: Our primary ethical obligation is to protect the privacy and confidentiality of our student participants. Informed Consent Limitations: The informed consent process for this study, approved by the institutional ethics review board, explicitly outlines how the collected data will be used (i.e., for aggregate analysis to evaluate teaching effectiveness within the confines of this research project). Nature of the Data and High Risk of Re-identification: The qualitative interview data are particularly sensitive.
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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.
Questions about this trial
What is this clinical trial studying?
A study testing A blended online and offline teaching model for internal medicine nursing practice based on generative artificial intelligence in people with Generative Artificial Intelligence, run by Hengxu Wang.
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 2 inclusion and 2 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 24 Sep 2025. 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 hengxu wang (+86-15575503185, 1543980936@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Course Scores (time frame: At the end of the 6-month course.). The study lists 2 primary outcomes in all. 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 24 Sep 2025.