The people we’re asking to innovate in the classroom are also rounding on patients at 6 AM.
Nursing faculty aren’t just teachers. Most carry clinical caseloads. They precept students. They sit on accreditation committees, curriculum reviews, policy groups. They advise and mentor. They’re supposed to stay current with practice guidelines that update faster than anyone can read them.
Now add “build branching case studies and adaptive assessments” to that list.
The will is there. The hours are not.
What the Job Actually Looks Like
I want to be specific about this, because the workload of nursing faculty is consistently misunderstood by people outside it.
A typical week for a full-time nursing faculty member with a clinical appointment looks something like this: two to three clinical days, one to two full teaching days, committee meetings, student advising appointments, clinical supervision, course prep, grading. If they’re program faculty with curriculum or accreditation responsibilities, add that. If they’re writing for publication, add that. If they have any leadership role, add that.
Most nursing faculty I know work weekends. Not occasionally. Regularly. Not because they’re inefficient, but because the job was never scoped to fit in 40 hours.
This is the population we’re asking to redesign their course structure around evidence-based learning principles. Build retrieval-heavy assessments. Create new clinical case studies every semester. Design simulation scenarios. Develop the kind of layered, spaced curriculum that cognitive science has validated. The evidence says it works. The evidence also says it takes time that these faculty don’t have.
What Changes With AI
I want to be clear about what AI does and doesn’t do here.
It doesn’t make the work easier in the sense of requiring less thought. Good AI-assisted course content requires substantial clinical and pedagogical expertise to produce. You have to know when the output is wrong. You have to know when the framing doesn’t fit your learners. You have to iterate, refine, and verify. It’s not push-button, and anyone telling you it is hasn’t tried to produce content that holds up to clinical scrutiny.
What it does is compress the time between having an idea and having a usable product. A branching case study that would take a faculty member four hours to build from scratch can be scaffolded in an hour with the right tools and iterated from there. A set of retrieval questions for next week’s pharmacology content that would require two hours of writing can be generated, reviewed, and refined in 30 minutes.
That’s the force multiplier. Not eliminating the work. Shifting where the cognitive load goes.
When I’m not spending four hours building a case structure, I’m spending that time on the parts of teaching that only I can do. Watching a student take a history and pointing out the three things they missed. Sitting with a student who’s struggling with clinical reasoning and walking them through a patient presentation in real time. Giving feedback that changes how they think, not just what they know. Those are the things that produce clinicians. Machines can’t do them. I can. I need the hours to do them.
The Right Conversation About AI in Education
Most of the academic conversation about AI in nursing education has been about policing student use. That’s the wrong conversation.
The right conversation is about what AI lets faculty do that they couldn’t do before. Build more cases. Write better questions. Create teaching content aligned to actual competencies rather than whatever happened to be in the slides from three years ago. Run the evidence-based course designs they know work but have never had the bandwidth to implement.
Faculty who figure out how to use AI well aren’t taking shortcuts. They’re removing the resource constraint that has always been the actual barrier between knowing what good teaching looks like and building it.
The constraint was never knowledge. It was time. That’s the problem AI solves, when it’s used deliberately and with enough domain expertise to know when to trust the output.
For the faculty reading this: what’s the thing that eats the most time in your teaching workflow? The part where you know there has to be a better way but you haven’t found it yet? That’s where to start.