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

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

Status
Enrolling by invitation
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07540078
Acronym
NRAC
Enrollment
640
Registered
2026-04-20
Start date
2025-08-13
Completion date
2026-12-01
Last updated
2026-04-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

AI (Artificial Intelligence), Education

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.

Interventions

OTHERLLM-Integration

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.

Sponsors

National University Health System, Singapore
Lead SponsorOTHER
Ministry of Education, Singapore
CollaboratorOTHER_GOV

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* 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"

Exclusion criteria

* NIL

Design outcomes

Primary

MeasureTime frameDescription
Research Competence questionnaireOnce at baseline (week 0), once at Week 10A 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
Revised Attitudes Toward Research scaleOnce at baseline (week 0), once at Week 10A 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.
Grades of research proposalWeek 15Grades 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.

Countries

Singapore

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Apr 21, 2026