NURS 220 Week 4 APA Paper Final Example

Plagiarism in Nursing Education: Definitions, Causes, the New Problem of Generative AI and What Students Can Do

Abstract

Plagiarism is the most common academic integrity violation in nursing education, and the arrival of generative artificial intelligence has added new forms of it. This paper examines what plagiarism is, why it occurs among nursing students, how generative AI changes the problem and what students and faculty can do. Drawing on the APA manual, it distinguishes intentional copying from unintentional forms such as patchwriting and self-plagiarism. Recent reviews show that interventions to prevent dishonesty in nursing education are few and focus mainly on plagiarism, and that the link between academic and clinical dishonesty is real in some studies but not uniform. A recent case analysis describes AI-fabricated citations as a distinct problem that standard similarity tools rarely catch. The paper concludes that habits of honest writing, including careful note-taking, true paraphrasing, verification of every source and transparent use of AI, are also habits of honest documentation in nursing practice.

Introduction

Every nursing student learns to document care accurately, because a patient's safety depends on the record being true. Academic writing asks for the same honesty about where words and ideas come from. Plagiarism is the failure of that honesty, and it remains common. Bloompott et al. (2025) identify plagiarism as the most common academic integrity violation in nursing education. This paper examines what counts as plagiarism, why students commit it, how generative artificial intelligence has created new forms and what students can do to avoid it. Its argument is that the skills that prevent plagiarism are the same skills that make a trustworthy nurse.

What Counts as Plagiarism

The American Psychological Association (2020) defines plagiarism as presenting the words, ideas or images of another as one's own, and it treats self-plagiarism, presenting one's own previous work as new, as a related violation. The definition is broader than many students expect, because it covers ideas and structure as well as exact words.

Intentional Forms

Intentional plagiarism includes copying text without quotation marks or citation, buying or borrowing a paper and submitting another person's work. These forms are deliberate choices to misrepresent authorship, and most students recognize them as wrong.

Unintentional Forms

Unintentional plagiarism is more common in student writing and harder to recognize. Patchwriting copies a source's sentence structure while swapping a few words, so that even with a citation the writing is not really the student's own. Missing quotation marks around a copied phrase, citations left off paraphrased ideas and the reuse of a paper from a previous course without permission are other examples. These often arise from poor note-taking, in which copied phrases enter the student's notes and later appear in the paper as if they were the student's words.

Why Plagiarism Happens and Why It Matters in Nursing

Students plagiarize for many reasons: time pressure, uncertainty about citation rules, weak paraphrasing skills, unclear expectations and, in some cases, a belief that it will not be noticed. Online programs add challenges. Bloompott et al. (2025) describe how a graduate nursing department developed a consistent process for addressing plagiarism, including orientation sessions on academic integrity, similarity software, APA instruction and a one-credit introductory course covering scholarly writing, citation, the ethical use of AI and library databases. Their account suggests that much plagiarism can be prevented by teaching rather than only punished after the fact.

The evidence on prevention is still thin. Bekman et al. (2025) systematically reviewed educational interventions to reduce dishonesty among nursing students and found only four studies that met their criteria; all focused on academic dishonesty, especially plagiarism, and none addressed clinical dishonesty. Whether academic dishonesty predicts dishonesty in clinical practice is a further question. Danino et al. (2026) reviewed eight studies and found that four reported significant associations between academic and clinical dishonesty and two found predictive relationships, while two saw them as conceptually distinct; they concluded that the relationship is real in some contexts but not a single, uniform pathway. The finding is a caution against assuming every plagiarist will falsify a chart, but also a reason to take integrity seriously in both settings.

What a Similarity Report Can and Cannot Show

Tools such as SafeAssign compare a paper with databases of published work, websites and earlier student submissions and highlight matching text. A match is not automatically plagiarism: a correctly quoted and cited passage, a common phrase or a reference list entry will all appear as matches. Equally, a low similarity score does not prove a paper is honest. A patchwritten paragraph may escape detection if enough words were changed, and an idea taken without citation produces no match at all because the words are new. Bloompott et al. (2025) note that faculty also need ongoing education in interpreting similarity reports, which suggests that students should read their reports with the same care. The useful question is not the percentage but each highlighted passage: is it quoted and cited, is it a properly cited paraphrase that follows the source too closely or is it someone else's work presented as mine? Revising after the report means fixing the passage itself, by quoting or truly rewriting, not shuffling words until the highlight disappears.

The New Problem of Generative AI

Generative AI tools can help students brainstorm, outline and revise, but they also create new integrity problems. Topaz et al. (2025) describe both the promise of these tools in nursing education and the risks of plagiarism, copyright infringement and misinformation, and they recommend clear institutional guidelines and student education. Iheduru-Anderson (2026) identifies a particular danger: AI tools can fabricate citations, including journal articles and DOIs that do not exist. In the case the author describes, an undergraduate nursing student submitted fabricated articles and misattributed references, errors that revealed a deeper lack of understanding of evidence-based scholarship. The author argues that this kind of fabrication differs from traditional plagiarism because it is often unintentional and nearly undetectable by standard similarity tools. A reference list that looks perfect may contain sources no one ever read.

What Students Can Do

Five habits prevent nearly all plagiarism. First, take notes in your own words and mark any copied phrase with quotation marks and a page number at the moment you copy it. Second, paraphrase by reading a passage, closing the source and writing the idea from memory, then checking accuracy, which avoids patchwriting. Third, cite as you write rather than adding citations at the end. Fourth, find and open every source you cite; if a tool suggests a reference, locate the actual article through the library before using it, as the AI fabrication case shows. Fifth, follow the course's policy on AI and disclose any use the policy requires. Students who are unsure whether something counts as plagiarism can ask the instructor or the writing center before submitting, which is always safer than guessing.

Conclusion

Plagiarism in nursing education ranges from deliberate copying to quiet patchwriting and, now, to citations invented by software. The evidence shows that it is common, that prevention programs are still few and that its relationship to clinical honesty is real in some studies though not uniform. A reference list and a patient chart make the same promise: that what is written there is true and can be traced to its source. Learning to write honestly is practice for a profession in which the record must always be trusted.

References

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

Bekman, N., Danino, E., & Maoz, E. (2025). Addressing dishonesty in nursing education: A systematic review of intervention effectiveness. Nurse Education in Practice, 84, 104327. https://doi.org/10.1016/j.nepr.2025.104327

Bloompott, K., Grugan, A., Borton, R., & Smith, K. (2025). Evolution of plagiarism processes in online graduate nursing programs. Journal of Professional Nursing, 58, 93-96. https://doi.org/10.1016/j.profnurs.2025.03.007

Danino, E., Maoz, E., & Bekman, N. (2026). Beyond a linear assumption: A systematic review of academic and clinical dishonesty in nursing education. Journal of Professional Nursing, 66, 59-66. https://doi.org/10.1016/j.profnurs.2026.05.017

Iheduru-Anderson, K. (2026). Eroding scholarly integrity: Confronting the misuse of generative AI in nursing education. Nurse Education Today, 164, 107145. https://doi.org/10.1016/j.nedt.2026.107145

Topaz, M., Peltonen, L. M., Michalowski, M., Stiglic, G., Ronquillo, C., Pruinelli, L., Song, J., O'Connor, S., Miyagawa, S., & Fukahori, H. (2025). The ChatGPT effect: Nursing education and generative artificial intelligence. Journal of Nursing Education, 64(6), e40-e43. https://doi.org/10.3928/01484834-20240126-01