Development of an Artificial Intelligence Teaching Assistant System for Undergraduate Nursing Students: A Field Testing Study
Open accessAbstract
Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this study was to develop, test usability of, and explore undergraduate nursing students' perceptions toward the Artificial Intelligence–Teaching Assistant System. The system was developed based on three main components: machine tutor intelligence, a graphical user interface, and a communication connector. They were included in the system to support contextual machine tutoring. A field-testing study design, a mixed-method approach, was utilized with questionnaires and focus group interview. Twenty-one undergraduate nursing students participated in this study, and they interacted with the system for 2 hours following the required activity checklist. The students completed the validated usability questionnaires and then participated in the focus group interview. Descriptive statistics were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data from the focus group interviews. The results showed that the Artificial Intelligence–Teaching Assistant System was user-friendly. Four main themes emerged, namely, functionality, feasibility, artificial unintelligence, and suggested learning modality. However, Artificial Intelligence–Teaching Assistant System functions, user interface, and content can be improved before full implementation.
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For students, studies documented improved personalisation and self-directed learning capabilities, where GenAI functions as cognitive scaffolding that helps students advance towards the performance of complex tasks.
“For students, studies documented improved personalisation and self-directed learning capabilities, where GenAI functions as cognitive scaffolding that helps ' students advance towards the performance of complex tasks '”
From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Kowitlawakul et al., 2024
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- Authors
- Kowitlawakul, Y., Tan, J. J. M., Suebnukarn, S., Nguyen, H. D., Poo, D. C. C., Chai, J., Kamala, D. M., & Wang, W.
- Affiliations
- Thammasat University (TH)
- Venue
- CIN: Computers, Informatics, Nursing
- Published
- 2024-01-24
- Volume
- 42
- Issue
- 5
- Pages
- 334-342
- ISSN
- 1538-2931 (ISSN-L)
- 1538-9774
- OpenAlex
- W4391231738
- Cited by
- 10
- References
- 28
- Citations by year
- 2026: 6 · 2025: 4
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.gla.ac.uk
- Licence
- cc-by-sa
- Locations
- CIN Computers Informatics Nursing (publishedVersion)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- nursing education, undergraduate nursing students, student engagement, student motivation, active learning, usability, student perceptions, online learning, chatbots
- MeSH terms
- Adult, Artificial Intelligence, Education, Nursing, Baccalaureate, Female, Humans, Male, Surveys and Questionnaires, Students, Nursing, User-Computer Interface, Focus Groups, Problem-Based Learning, Young Adult
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