Leveraging Large Language Models (LLM) to Enhance Research Competency Among Undergraduate Nursing Students: A Novel Approach to Research Education

NCT07540078 | Enrolling By Invitation

• 1 other identifier: NUS-IRB-2025-136
• Study Type: interventional
• Target: 640
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this mixed method interventional study is to develop and test the effectiveness of integrating ChatGPT into the nursing research course to improve research competency among third-year undergraduate nursing students. The main questions it aims to answer is: Will participants who undergo the LLM-integrated curriculum show an increase in research competency and attitudes compared to participants who did not undergo this curriculum. Researchers will compare a students assessment grades, as well as their research competency and attitude, measured via the Research Competence Scale (R-Comp) and Revised Attitudes Towards Research scale (R-ATR) respectively. Research will determine whether the LLM-integrated curriculum could improve students understanding and attitudes towards research.

Conditions

Education; AI (Artificial Intelligence)

Outcome Measures

Primary Outcomes (3)

• Research Competence questionnaire

  A 32-item, 5-point Likert scale assessing participants perceived research competency. The minimum and maximum score one can attain on this measure is 32 and 160 respectively, with a higher score indicating higher perceived competence in research

  Once at baseline (week 0), once at Week 10

• Revised Attitudes Toward Research scale

  A 13-item, 7-point Likert scale assessing participants' own attitudes towards research. The minimum and maximum score one can attain on this measure is 13 and 91 respectively, with a higher score indicating a more positive attitude towards research.

  Once at baseline (week 0), once at Week 10

• Grades of research proposal

  Grades from the research proposal will also be used as an objective measure of the student's research competency for triangulation of data. This research proposal is an existing assessment in NUR3202C it will be assessed using a structured rubric that grades students based on the content, organization, delivery and collaboration.

  Week 15

Study Arms (2)

AY2025/2026 cohort

NO INTERVENTION

AY2026/2027 cohort

EXPERIMENTAL

Other: LLM-Integration

Interventions

LLM-Integration OTHER

The curriculum for AY2026/2027 will have the following integrated into their lessons/ learning materials: 1. ChatGPT integrated curriculum, 2. tutor manual; 3. student manual; and 4. ChatGPT interactive platform.

AY2026/2027 cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• All year-three students in cohort years AY2025/26 and AY2026/27 who are enrolled in mandatory research course titled "NUR3202C: Research and Evidence-Based Healthcare"

You may not qualify if:

• NIL

Sponsors & Collaborators

• Ministry of Education, Singapore: collaborator
• National University Health System, Singapore: lead

Study Sites (1)

National University of Singapore, Yong Loo Lin School of Medicine, Singapore, Singapore 117599

Singapore, Singapore

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: April 7, 2026
• First Posted: April 20, 2026
• Study Start: August 13, 2025
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: April 20, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

SG (1)