| Course | NUR 405 Professional Nurse Concepts Advanced |
|---|---|
| Module | Module 1 |
| Paper type | AI prioritization exercise with written critique |
| Length | About 597 words, 5 pages |
| Format | APA 7 student paper |
| School | Arizona State University |
| Program | BSN in Nursing |
| Updated | October 2026 |
Free sample paper for NUR 405 Module 1
Four Patients, One Chatbot and a Missed Warning Sign: Checking an AI Prioritization Against Nursing Frameworks
Student Name
Edson College of Nursing and Health Innovation, Arizona State University
NUR 405: Professional Nurse Concepts: Advanced
Instructor Name
Month Day, Year
Four Patients, One Chatbot and a Missed Warning Sign: Checking an AI Prioritization Against Nursing Frameworks
The Assignment Given to the AI
I entered a de-identified four-patient assignment at 0700 and asked: "Using nursing prioritization frameworks, in what order should a new nurse see these patients, and why?"
| Patient | Situation at 0700 |
|---|---|
| A | 68-year-old, day 1 after colon resection; heart rate 108, blood pressure 102/64, urine output 20 mL per hour for the last two hours; reports pain 6 of 10 |
| B | 74-year-old with pneumonia on 2 L oxygen; saturation 93%; asking for help to the bathroom |
| C | 55-year-old with type 2 diabetes; fasting glucose 248 mg/dL; insulin due at 0730 with breakfast |
| D | 81-year-old with dementia, admitted for a urinary tract infection; calm; fall risk score high; bed alarm on |
The AI's Ranking
The tool ranked B first, citing oxygen and "breathing before everything," then D for fall risk, then C for the insulin timing and A last as "stable post-operative pain management." It explained each choice with a short sentence and mentioned the airway, breathing and circulation framework.
My Ranking
I ranked A first. A heart rate of 108, a blood pressure trending down and urine output of 20 mL per hour for two hours in a patient one day after major abdominal surgery are early signs of hypovolemia or bleeding, a circulation problem that is actual, not potential, and can worsen quickly. Patient B has a stable breathing problem: a saturation of 93% on oxygen is within her expected range, and her need is for assistance, which can be delegated to the assistive personnel with a reminder about her oxygen. Patient C's insulin is time-sensitive but not urgent at 0700. Patient D is a real safety risk, but the bed alarm is on and she is calm, so she can be checked on the way.
Where the AI Went Wrong
The tool applied the frameworks as labels rather than to the data. It saw "oxygen" and placed breathing first without noticing that the breathing problem was stable, and it saw "pain" after surgery and treated patient A as routine. It never connected the three vital signs that together suggested a circulation problem. The NCSBN clinical judgment model describes this step as recognizing and analyzing cues: noticing which findings matter and how they relate before generating hypotheses (Dickison et al., 2019). The tool generated a hypothesis without that analysis.
In Tanner's model of clinical judgment (Tanner, 2006), the first step is noticing, the step in which a nurse, drawing on knowledge of the patient and the situation, sees what is significant. Noticing is where the tool was weakest and where a nurse's attention to trends, rather than single values, matters most.
Four Rules for Using AI in Prioritization
1. Decide first, then compare. Rank the patients myself before asking a tool, so its answer cannot anchor my judgment; automation bias leads clinicians to accept advice they should question (Goddard et al., 2012).
2. Feed it trends, not snapshots. Most of the tool's error came from reading values without their direction.
3. Treat its reasons as claims to test. A ranking that cites a framework is not the same as one that applies it.
4. Never enter identifiable patient information into a public tool.
Reflection
The activity made me more confident in my own reasoning and more careful about AI. The tool was useful for one thing: its wrong answer forced me to say exactly why patient A came first. That explanation is what I will need to give a charge nurse on my first day.
References
Dickison, P., Haerling, K. A., & Lasater, K. (2019). Integrating the National Council of State Boards of Nursing Clinical Judgment Model into nursing educational frameworks. Journal of Nursing Education, 58(2), 72-78. https://doi.org/10.3928/01484834-20190122-03
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121-127. https://doi.org/10.1136/amiajnl-2011-000089
Tanner, C. A. (2006). Thinking like a nurse: A research-based model of clinical judgment in nursing. Journal of Nursing Education, 45(6), 204-211. https://doi.org/10.3928/01484834-20060601-04
NUR 405 Module 1 instructions, in plain terms
The posted syllabus lists the AI Prioritization Activity at 3 of the course's 100 points and describes it in one line: students will utilize AI to apply prioritization frameworks. The detailed instructions, the scenario or the freedom to choose one, and the rubric are in Canvas, so read them before starting. Expect to give an AI tool a realistic patient assignment, ask it to prioritize using nursing frameworks such as airway, breathing and circulation, Maslow's hierarchy or actual versus potential problems, and then evaluate its answer against your own reasoning. The value of the activity lies in your critique, not in the tool's output. Use a de-identified or invented scenario; do not put real patient information into any public AI tool, which also protects you under clinical site policies.
Inside the NUR 405 Module 1 example
The sample opens with the exact prompt and a table of four patients with the data a nurse would see at the start of a shift. A short section summarizes the AI's ranking and reasons. The student then gives her own ranking, justifying each position with specific vital signs and named frameworks. A section explains where the tool went wrong, using the NCSBN clinical judgment model's step of recognizing and analyzing cues and Tanner's idea of noticing. Four rules for using AI in prioritization follow, one supported by research on automation bias, and a brief reflection says what the exercise taught about the student's own reasoning. Three sources support it.
NUR 405 Module 1 rubric: what earns full marks
Since the activity carries few points, the rubric is probably simple, but it likely rewards correct use of prioritization frameworks, a clear comparison between the AI's answer and the student's, accurate clinical reasoning tied to specific data, thoughtful evaluation of the tool's strengths and limits, attention to privacy and clear writing. Reasoning earns credit when it cites the actual findings, such as a falling urine output, rather than general principles. Naming a framework and showing how it applies is stronger than naming it alone. Identifying the specific error in the tool's logic shows critical thinking. Ethical use of AI, including privacy, may be part of the score.
NUR 405 Module 1 help with common mistakes
The weakest submissions paste the AI's answer and add "I agree." Rank the patients yourself first, then compare, so your judgment is visible. Another common miss is choosing a scenario with an obvious answer, which leaves nothing to critique; include a subtle cue, such as a trend in vital signs. Keep the scenario de-identified. If you would like help building a prioritization scenario and critique from your own clinical experience, describe the patient mix to the desk without names. Save a screenshot of the AI's response in case faculty ask for it. Practice the same comparison with NCLEX-style questions, since the skill is identical.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Arizona State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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NUR 405 Module 1 questions, answered
Where can I find a free NUR 405 Module 1 sample paper?
This page carries a full NUR 405 AI Prioritization Activity sample comparing a chatbot's four-patient ranking with nursing frameworks and naming the warning sign it missed.
What is the NUR 405 AI Prioritization Activity?
A short assignment in which students use an AI tool to apply prioritization frameworks to patients and then evaluate the result.
Which prioritization frameworks should I use?
Common choices are airway, breathing and circulation, Maslow's hierarchy of needs and actual versus potential problems, applied to the specific data for each patient.
Can AI prioritize patients correctly?
It may produce a plausible ranking, but it can miss trends and connections among findings, so a nurse must check its reasoning against the data.
Is it safe to use patient information with AI tools?
Never type identifiable patient details into a public chatbot; use invented or fully de-identified scenarios.