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

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.

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