NCT07688499

Brief Summary

This educational reform study aims to explore whether a full English course built on the DeepSeek artificial intelligence platform can improve orthopedic nurses' professional English competence. It will also examine nurses' satisfaction with AI-assisted teaching. The main questions it seeks to answer include: Will the DeepSeek-based full English course improve orthopedic nurses' professional English test scores? Will nurses' transcultural nursing self-efficacy and nurse-patient therapeutic interaction ability improve after the course training? What are nurses' experiences when using the DeepSeek AI platform for learning? Researchers will compare the English proficiency changes of the same group of orthopedic nurses before and after the training to observe the course effects. Participants will: Attend an 11-week full English course based on the DeepSeek platform, approximately 1-2 hours per week Complete a professional English written test and an oral proficiency assessment before and after the training Complete the Transcultural Self-Efficacy Tool (TSET-CV) and the Nurse-Patient Therapeutic Interaction Scale (NuPTIS) before and after the training Complete a teaching satisfaction and AI learning experience questionnaire after the course

Trial Health

65
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
20

participants targeted

Target at below P25 for not_applicable

Timeline
11mo left

Started Jul 2026

Status
not yet recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

Study Progress9%
Jul 2026Jul 2027

First Submitted

Initial submission to the registry

July 1, 2026

Completed
Same day until next milestone

Study Start

First participant enrolled

July 1, 2026

Completed
6 days until next milestone

First Posted

Study publicly available on registry

July 7, 2026

Completed
12 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 1, 2027

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

July 1, 2027

Last Updated

July 7, 2026

Status Verified

July 1, 2026

Enrollment Period

1 year

First QC Date

July 1, 2026

Last Update Submit

July 1, 2026

Conditions

Keywords

DeepSeek; artificial intelligence; orthopedic nursing; full English course; nursing English teaching; transcultural nursing; nursing education

Outcome Measures

Primary Outcomes (1)

  • Change in Professional English Written Test Score from Baseline to Week 11

    Change in Professional English Written Test Score from Baseline to Week 11

    from Baseline to Week 11

Study Arms (1)

DeepSeek AI-Based Full English Course Training Arm

EXPERIMENTAL

Participants in this arm are 60 registered orthopedic nurses who receive an 11-week full English course training for orthopedic nursing based on the DeepSeek AI platform. The course adopts a blended online and offline teaching model, covering six modules. Participants undergo professional English written tests, oral assessments, the Transcultural Self-Efficacy Tool (TSET-CV), and the Nurse-Patient Therapeutic Interaction Scale (NuPTIS) before and after the training.

Other: Based on the all-English courses of DeepSeek

Interventions

This intervention is an 11-week full English course training for orthopedic nursing based on the DeepSeek artificial intelligence platform. The course adopts a blended online and offline teaching model, deeply integrating AI technology with nursing English education. It covers six modules: Basic English for Orthopedic Nursing, Trauma Orthopedic Nursing, Joint Replacement and Nursing, Spinal Disorders and Nursing, Common Orthopedic Nursing and Orthoses, and Clinical Cross-cultural Communication. During the training period, nurses engage in personalized learning through the DeepSeek platform, including intelligent vocabulary memorization, AI speech correction exercises, virtual doctor-patient dialogue simulations, AI case analysis, and English literature reading comprehension. These are combined with offline activities such as role-playing, workshops, and case discussions to systematically enhance orthopedic professional English competence and cross-cultural communication skills.

DeepSeek AI-Based Full English Course Training Arm

Eligibility Criteria

Age20 Years - 40 Years
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64)

You may qualify if:

  • ① On-duty nurses in the orthopedics department ② Obtained the nurse professional qualification certificate ③ Obtained the patient's informed consent and signed the informed consent form ④ The research was approved by the ethics committee of this hospital.

You may not qualify if:

  • Nurses who have not completed 1/2 of the course content

Contact the study team to confirm eligibility.

Sponsors & Collaborators

MeSH Terms

Conditions

Language

Condition Hierarchy (Ancestors)

CommunicationBehavior

Central Study Contacts

chuweiwei wei chuww, Bachelor's Degree

CONTACT

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
OTHER
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

July 1, 2026

First Posted

July 7, 2026

Study Start

July 1, 2026

Primary Completion (Estimated)

July 1, 2027

Study Completion (Estimated)

July 1, 2027

Last Updated

July 7, 2026

Record last verified: 2026-07

Data Sharing

IPD Sharing
Will not share

Nature of the Study: This is an educational reform study rather than a clinical drug or device trial. The data collected consist primarily of nurses' English test scores, scale assessments, and teaching satisfaction feedback, which do not involve patient health information or clinical efficacy data. Data Sensitivity: The research data include participants' individual learning performance, English proficiency assessments, and questionnaire feedback, which contain information that could indirectly identify individuals (e.g., employee ID, study code, platform usage records). Although data have been anonymized, there remains a potential risk of re-identification within a small institutional setting. Informed Consent Limitations: The informed consent form for this study did not include authorization for sharing data to public repositories. Participants only consented to the use of their data for analysis and publication of this study.