How the University of Phoenix Is Teaching Nurses to Work with AI — and What That Means for Classrooms

University of Phoenix showcased practical AI teaching for nurse practitioner programs

At a national education conference, nursing leaders from the University of Phoenix laid out how they are bringing artificial intelligence into nurse practitioner training. The session focused on real classroom tools, ways to assess student work, and steps to keep patients safe when students practice with AI. Presenters showed short demonstrations and described pilot classes where students use AI to sharpen clinical reasoning, not to replace learned skills. The talk stressed simple goals: make students better at asking clinical questions, spotting AI mistakes, and documenting care clearly.

How instructors are actually using AI in teaching and labs

The presenters described several hands-on methods that will feel familiar to most teachers. One is a graded, case-based approach: students get a clinical scenario and see both a human-written answer and an AI-generated answer. In class they compare the two, identify gaps, and explain which parts of the AI output they would trust and why. That turns a black-box tool into a critical thinking exercise.

Another tool is simulated patient encounters where an AI helps generate patient history or common exam findings while a student practices documentation and decision-making. The faculty stressed a layered model: start with low-stakes practice so learners can make mistakes without patient risk, then move to timed, higher-stakes simulations that mimic clinical pressure.

Faculty also use prompt-writing workshops. Students learn how to ask the AI specific, clinical questions and how to check sources the AI cites. Instructors pair that with rubrics that reward clear rationale and safe decision steps rather than just a correct diagnosis. Finally, the team described integrating AI with electronic health record mock-ups so learners can practice entering orders, writing notes, and checking for drug interactions in a controlled setting.

Who led the session and why their view matters

The speakers were senior nursing faculty, curriculum designers, and instructional-technology staff from the University of Phoenix. Together they represent clinical teaching experience, hands-on work with online course design, and day-to-day oversight of student performance. That mix matters because teaching AI in health care is both clinical and technical: faculty must keep patient safety front and center while making the tech approachable for students.

The presenters drew on classroom pilots and faculty development work, not just theory. They emphasized practical limits—what AI can do today, where it still fails, and how to teach students to spot those failures. Their background in both practice and pedagogy gave the session a clear, classroom-ready tone instead of abstract speculation about future tools.

Why this matters now for nursing education and health care

The session comes as nursing programs face pressure to prepare graduates for a digital workplace. AI tools are entering clinics and hospitals for tasks like documentation, triage support, and patient education. At the same time, regulators and accrediting bodies are paying closer attention to how programs teach clinical judgment. Schools that teach students to treat AI as an assistant rather than an oracle are aiming to reduce risks such as incorrect recommendations, biased outputs, or overreliance on technology.

There is no universal model yet, and different programs are testing different safeguards—standardized prompts, bias checks, and faculty oversight. The University of Phoenix presentation fits a broader trend: programs moving from anxious curiosity about AI to concrete classroom practices that protect patients and build useful skills.

What educators and students can expect next from the university

The presenters said the university is rolling out faculty workshops, student toolkits, and pilot curricula that other programs can adapt. Those resources include lesson plans, assessment rubrics, and simulated workflows for common clinical tasks. They also highlighted policies for privacy and data security, plus guidance on when AI use should be restricted in summative exams.

For other nursing programs, the practical takeaway is straightforward. Start small, emphasize critical thinking, and build assessments that reward safe reasoning more than polished answers. The University of Phoenix approach is pragmatic: teach students to use AI well enough that the tools improve care without letting technology shrink professional judgment.

Sources

• prnewswire.com