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TRUVACE RECORD VERSION
record: TRV-2026-2193
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-22T22:03:36.732166Z
status: published
lens: trace
sector: health
headline: Integration of artificial intelligence in quality improvement projects for undergraduate nursing students: Enhancing nursing education through innovative applications
dek: Background and objective: Nursing faculty have been hesitant to incorporate artificial intelligence into coursework due to various reasons, including the risk of affecting academic integrity and the potential for receiving inaccurate information. As nursing cohorts continue to grow, innovative approaches are needed to meet learning objectives; such as artificial intelligence. Incorporating artificial intelligence into group projects may increase responsible, ethical, and critical use of artificial intelligence a…
gain_title: Six undergraduate nursing students who used AI to find evidence for a patient safety project reported useful creative ideas, workflow steps and a broad range of solutions, and AI findings matched most of their literature review articles.
problem_title: Students found the AI output lacked concrete examples and tended to be more practical than theoretical, which limits how far it can replace conventional literature review work.
trace_subject: AI-assisted literature review in an undergraduate nursing quality improvement project
gain_reading: Six undergraduate nursing students who used AI to find evidence for a patient safety project reported useful creative ideas, workflow steps and a broad range of solutions, and AI findings matched most of their literature review articles.
gain_evidence: Benefits of using artificial intelligence included the generation of creative ideas, provision of workflow steps, and a broad range of solutions. | Groups found artificial intelligence generated findings aligned with most articles from their literature reviews.
problem_reading: Students found the AI output lacked concrete examples and tended to be more practical than theoretical, which limits how far it can replace conventional literature review work.
problem_evidence: Challenges included a lack of concrete examples and a tendency for artificial intelligence generated information to be more practical than theoretical.
quick_read: An honors conversion option in a Clinical Science II course let six undergraduate nursing students, split into two groups of three, use artificial intelligence while building a quality improvement project. Each group tied a topic to a Joint Commission national patient safety goal, wrote a clinical question and search strategy, ran a literature review and used AI to identify evidence, checking its accuracy.

The work matters because nursing faculty have been hesitant about AI over academic integrity and inaccurate information, while cohorts keep growing. The authors argue guided use could help programs monitor AI use and manage larger groups. The evidence is a very small voluntary sample, so the broader educational effect remains untested.
limitation: The initiative involved only six self-selecting students in one honors course option, so the reported benefits and challenges cannot be generalized to wider nursing cohorts.
tag: Dual reading
key_points: Participation was voluntary: six students opted in and formed two groups of three. | Groups matched their topics to a Joint Commission national patient safety goal. | Reported benefits were creative ideas, workflow steps and a broad range of solutions. | Reported challenges were a lack of concrete examples and output that was more practical than theoretical. | AI-generated findings aligned with most articles found through the groups' literature reviews.
rundown: The initiative responded to faculty reluctance to bring AI into coursework, which stemmed from concerns about academic integrity and inaccurate information, at a time when nursing cohorts are growing and new methods are needed to meet learning objectives.

Students developed a clinical question and a search strategy and conducted a traditional literature review, then used AI to identify evidence. Validating the accuracy of AI-generated information was built into the approach, giving students practice in critical and responsible use.

The authors conclude that building AI use into nursing curriculums under guided instruction can help programs monitor use while managing growing cohorts, and can improve understanding of practical applications in clinical settings. They state this would prepare students for real-world practice while maintaining academic integrity, a conclusion drawn from the small group's experience.
sources:
- peer_reviewed | Journal of Nursing Education and Practice | https://doi.org/10.63564/jnep.v16n11p1 | 2026-10-22
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