Integrity When Every Student Has a Chatbot: An Ethical Analysis and Policy Proposal for Generative AI in a Nursing Program
Student Name
Master of Science in Nursing Program, Aspen University
N580: Issues in Nursing Education
Instructor Name
Month Day, Year
Integrity When Every Student Has a Chatbot: An Ethical Analysis and Policy Proposal for Generative AI in a Nursing Program
In a composite baccalaureate program, a faculty member ran a student's written care plan through an artificial intelligence detection tool, which reported a high probability that the text was machine-generated. The student, who immigrated as a teenager and writes carefully in her second language, denied using any tool and showed drafts saved over several days. The faculty member was unsure what to do, and the program had no policy on generative artificial intelligence beyond a general academic honesty statement written years earlier.
Generative artificial intelligence tools that produce fluent text on demand have changed the question of academic integrity for nursing education. This paper analyzes the ethical issues, reviews relevant evidence about academic dishonesty among nursing students and about the reliability of detection tools, and proposes a program policy that distinguishes permitted, disclosed and prohibited uses.
Why Integrity Matters More in Nursing
Academic integrity in nursing is not only an academic value. Students who misrepresent their own knowledge in coursework may carry the same habits into practice, where honesty in documentation and reporting protects patients. Krueger (2014), surveying 336 nursing students, found that over half admitted to dishonest behavior in the classroom and also in clinical settings, and that the frequency of classroom and clinical cheating was positively related; peer behavior and personal beliefs were associated with dishonest behavior. The profession's code of ethics also treats integrity as part of the nurse's character and responsibility (American Nurses Association, 2025). A policy on generative tools is therefore a question of professional formation, not only of grading.
The Ethical Issues
Four ethical issues arise. The first is honesty: submitting machine-generated text as one's own reasoning misrepresents what the student knows, which is the core of academic dishonesty. The second is learning. Assignments such as care plans and reflective papers exist to build reasoning and judgment; outsourcing them bypasses the learning they are meant to produce, even if no rule forbids it. The third is fairness in enforcement. Detection tools are unreliable, and Liang et al. (2023) reported that detectors frequently misclassify writing by non-native English speakers as generated by artificial intelligence, raising concerns about fairness and the risk of marginalizing these students in educational settings. Accusing a student on the basis of a detector score alone, as in the opening case, risks an unjust outcome and falls hardest on multilingual students. The fourth is preparation for practice. Generative tools are entering clinical documentation and patient education, so graduates need to learn to use them critically, verify their output and understand their limits.
These issues point in different directions. A total ban protects the learning purpose of some assignments but is unenforceable and leaves students unprepared. Unrestricted use undermines assessment. The ethical response is a policy that is clear about purpose, fair in enforcement and honest about the technology's role in future practice.
Proposed Program Policy
The proposed policy has five elements. First, each assignment states one of three levels of permitted use. Level 1, no use: assessments of individual knowledge and reasoning, such as examinations, care plans for assigned patients and reflective journals, must be completed without generative tools. Level 2, assisted use with disclosure: students may use tools for defined tasks such as brainstorming, checking grammar or generating practice questions, and must attach a brief statement describing the tool, the prompts used and how the output was changed. Level 3, integrated use: assignments that teach students to evaluate tool output, for example critiquing an AI-generated patient education handout for accuracy and readability.
Second, students remain responsible for every statement they submit, including verifying facts and references; submitting fabricated citations produced by a tool is treated as a serious integrity violation. Third, detection software may prompt a conversation but may never be the sole basis for an integrity finding. Faculty who suspect misuse meet with the student, review drafts and version history, and may ask the student to explain or reproduce the reasoning orally. Fourth, patient information may never be entered into public generative tools, in keeping with privacy law and clinical agency policy. Fifth, the policy is taught, not just posted: every student completes a short module in the first semester, and faculty receive guidance on designing assignments that emphasize process, such as staged drafts and oral explanation.
The policy should be reviewed every year. Generative tools are changing quickly, and a rule that makes sense now, for example that tools may not be used to draft reflective journals, may need revision as tools are built into the software students and nurses use every day. A standing committee with faculty, students and a clinical partner can review cases, collect questions from faculty and students, and recommend changes. Publishing the policy's rationale along with its rules helps students understand that the aim is to protect learning and patient safety, not to police technology for its own sake.
Applying the Policy to the Case
Under the proposed policy, the care plan in the opening case would be a Level 1 assignment. The detector result alone would not support a finding. The faculty member would meet with the student, review the drafts and version history she offered, and perhaps ask her to talk through her reasoning for the priority problem and interventions. If she can explain and defend her work, the matter ends there, and the program has avoided an unjust accusation against a multilingual student. If the conversation reveals that she cannot explain content she submitted, the program's usual integrity process applies.
Conclusion
Generative artificial intelligence has made academic integrity in nursing education both more important and harder to protect. Honesty, learning, fair enforcement and preparation for practice all matter, and they are best served by a policy that states clearly what each assignment permits, requires disclosure of assisted use, holds students responsible for accuracy, never relies on detectors alone and protects patient privacy. The case of the student flagged by a detector shows what is at stake: a policy that is clear and fair protects both the integrity of the program and the students it is meant to form into trustworthy nurses.
References
American Nurses Association. (2025). Code of ethics for nurses. https://codeofethics.ana.org/
Krueger, L. (2014). Academic dishonesty among nursing students. Journal of Nursing Education, 53(2), 77-87. https://doi.org/10.3928/01484834-20140122-06
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), Article 100779. https://doi.org/10.1016/j.patter.2023.100779
How this N 580 Module 8 example is structured
Aspen does not publish N580 module prompts, so check your classroom for the exact instructions. This example opens with a detector-flagged case, explains why integrity matters in nursing, analyzes four ethical issues with evidence, proposes a five-element tiered policy, applies it to the case and concludes.
N580 Module 8 questions, answered
What does N580 Module 8 usually ask for?
Aspen's N580 description includes ethical issues related to students' academic performance and the role of technology, so a closing paper on an ethical issue in education, often with a policy recommendation, is a typical final assignment. Check your classroom for the exact focus.
Are AI detection tools reliable?
Not reliable enough to be the sole basis for an academic integrity finding. Research has shown that detectors frequently misclassify writing by non-native English speakers as machine-generated.
Should nursing programs ban generative AI?
A total ban is hard to enforce and leaves graduates unprepared for tools entering practice. Many programs instead set assignment-level rules on permitted use, require disclosure and teach students to verify output.
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