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Appl Clin Inform 2024; 15(05): 842-851
DOI: 10.1055/a-2373-3151
Special Topic on Teaching and Training Future Health Informaticians

Increasing Generative Artificial Intelligence Competency among Students Enrolled in Doctoral Nursing Research Coursework

Meghan Reading Turchioe
1   Columbia University School of Nursing, New York, New York, United States
,
Sergey Kisselev
1   Columbia University School of Nursing, New York, New York, United States
,
Liesbet Van Bulck
2   Department of Public Health and Primary Care, KU Leuven - University of Leuven, Leuven, Belgium
,
Suzanne Bakken
1   Columbia University School of Nursing, New York, New York, United States
3   Department of Biomedical Informatics, Columbia University, New York, New York, United States
4   Data Science Institute, Columbia University, New York, New York, United States
› Institutsangaben Funding This project was funded through a grant from the Columbia Center for Teaching and Learning. M.R.T. is also funded by National Institute of Nursing Research (NINR) of the National Institutes of Health (NIH). (grant no.: R00NR019124).
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Abstract

Background Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.

Objective This study aimed to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in health care.

Methods We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat Generative Pretrained Transformer (ChatGPT) 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in health care, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in health care practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students (n = 10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis.

Results Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. All of them reported increasing their self-rated competency in generative AI by one to two points on a five-point rating scale.

Conclusion This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.

Keywords

artificial intelligence - nursing informatics - informatics - medical informatics - nursing students - graduate nursing education - nursing education research

Protection of Human and Animal Subjects

The Columbia University IRB approved this study.




Publikationsverlauf

Eingereicht: 01. März 2024

Angenommen: 24. Juli 2024

Accepted Manuscript online:
25. Juli 2024

Artikel online veröffentlicht:
16. Oktober 2024

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