How artificial intelligence (AI) supports nursing education: profiling the roles, applications, and trends of AI in nursing education research (1993–2020)
Abstract
This study provides research-based evidence to profile: (1) the roles of artificial intelligence in nursing, (2) its research applications, and (3) the research trends for future study. On the basis of the PRISMA statement, a series of AI and nursing education related keywords from the literature were used to retrieve high-quality journal articles from the Web of Science. A total of 112 AI-supported nursing education research articles were analyzed based on a three-dimensional framework, including interaction (e.g. the roles of AI, types of AI systems), research (e.g. methods and fields), and performance (e.g. research groups and measurement foci). The results revealed that AI played a primary role in profiling and prediction in nursing research (63%), and the most used AI system in nursing was intelligent agents (53%). The quantitative approach (87%) was the dominant research method, and the most relevant studies concerned health and medicine (92%). Regarding sample and measurement matters, patients and medical staff (75%) were the two primary research samples, and the performance evaluation of AI-related tools and systems (90%) was the core measurement focus. Additional content analysis across the three research interests was performed and discussed. Directions for future studies are provided.
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The mechanism is consistent with arguments that AI can facilitate convenience and performance while simultaneously undermining independent thought if not accompanied by reflective safeguards.
“Such a mechanism is consistent with arguments that AI can facilitate convenience and performance while simultaneously undermining independent thought if not accompanied by reflective safeguards [27, 30, 41].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [30]
The difference between the current study's results and Kwak et al.'s results can be related to generational differences among nurses.
“These results can be related to the generational differences among nurses. 30”
From Exploring nurses’ awareness and attitudes toward artificial intelligence: implications for nursing practice (2024) · cites this paper as [30]
Demographic factors play a crucial role in shaping perceptions of technological advancements, as conservative attitudes are particularly pronounced among female and older nurses.
“These conservative attitudes are particularly pronounced among female and older nurses, suggesting that demographic factors play a crucial role in shaping perceptions of technological advancements.7,26,30”
From Exploring nurses’ awareness and attitudes toward artificial intelligence: implications for nursing practice (2024) · cites this paper as [30]
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“7,26,30 The current study results about the conservative attitudes and concerns toward AI technology come in contrary to the results reported by Kwak et al., 31 as they examined the attitudes of student nurses toward AI technology and resulted in the positive willingness of the students to work…”
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“Such a pattern is pedagogically significant because efficiency gains may coexist with shallower processing, reduced metacognitive monitoring, and lower conceptual depth [30, 43].”
- Authors
- Hwang, G.-J., Tang, K.-Y., & Tu, Y.-F.
- Affiliations
- National Taiwan University of Science and Technology (TW)
- Ming Chuan University (TW)
- Fu Jen Catholic University (TW)
- Corresponding author
- Tang, Kai-Yu
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2022-06-26
- Volume
- 32
- Issue
- 1
- Pages
- 373-392
- ISSN
- 1049-4820 (ISSN-L)
- 1744-5191
- OpenAlex
- W4283581602
- Cited by
- 87
- References
- 72
- Citations by year
- 2026: 27 · 2025: 32 · 2024: 21 · 2023: 6 · 2021: 1
- FWCI
- 4.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Learning Environments (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 109-2511-H-011-002-MY3, and MOST 109-2511-H-130-002)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, nursing education, intelligent agents, research trends, AI evaluation
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