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TRUVACE RECORD VERSION
record: TRV-2026-1389
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-15T01:09:16.249883Z
status: published
lens: p_space
sector: education
headline: Exploring Policy for Generative Artificial Intelligence in Nursing Education: Student and Faculty Perceptions
dek: Background: Generative artificial intelligence (GAI) is rapidly reshaping higher education, creating opportunities and challenges related to academic integrity, educator readiness, and policy clarity, particularly in nursing education.
Purpose: To examine nurse educator and student perspectives on GAI use, institutional policies, syllabus language, and best practices in nursing education.
Methods: A descriptive quantitative survey was completed by 211 nurse educators and 89 nursing students (N = 300) across dive…
gain_title: (none)
problem_title: Among 211 nurse educators and 89 nursing students, generative AI policies and expectations were communicated inconsistently, leaving students especially confused about acceptable use.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Among 211 nurse educators and 89 nursing students, generative AI policies and expectations were communicated inconsistently, leaving students especially confused about acceptable use.
problem_evidence: widespread uncertainty and inconsistent communication of GAI policies and expectations | Students reported greater confusion regarding acceptable use and institutional guidance
quick_read: A descriptive quantitative survey gathered views from 211 nurse educators and 89 nursing students across varied nursing programs. It asked about generative AI use, institutional policies, syllabus wording, and best practices. Respondents described uncertainty and uneven communication about what is expected of them.

The findings matter because unclear rules around generative AI touch academic integrity and educator readiness in a field that trains clinical professionals. The authors call for clearer, more accessible, and unified governance. The abstract reports perceptions only, so it does not show whether clearer policies would change student behavior or learning.
limitation: The study is a descriptive survey of perceptions, and the abstract reports no outcome measures showing how policy clarity affects academic integrity or learning.
tag: Evidence-backed problem
key_points: The survey included 211 nurse educators and 89 nursing students, for a total of 300 respondents. | Students reported more confusion than educators about acceptable use and institutional guidance. | Educators described approaches to generative AI that were decentralized and inconsistent. | Demographic factors were not significantly associated with perceptions. | The authors conclude that nursing education needs clearer and unified governance of generative AI.
rundown: The study used a descriptive quantitative survey of participants from diverse nursing programs. Responses were analyzed by program type, learning model, and degree level, which allowed comparison across different educational settings.

Results pointed to widespread uncertainty and inconsistent communication of generative AI policies and expectations. Students were more confused about what counts as acceptable use, while educators reported that approaches differ and are decentralized rather than coordinated.

Demographic factors were not significantly associated with perceptions, so the confusion did not appear to be confined to particular groups. The authors argue for policy that supports ethical, effective, and pedagogically sound integration of generative AI.
sources:
- peer_reviewed | Nurse Educator | https://doi.org/10.1097/nne.0000000000002303 | 2026-10-14
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