Takeaways from the 2025 NLN Education Summit: What Nurse Educators Are Talking About

The National League for Nursing held its 2025 Education Summit at the Hyatt Regency Orlando, September 17 through 19, with a single overarching theme: The Challenge: Unraveling the Mysteries of AI. Over 1,000 nurse educators, academic leaders, simulation specialists, and researchers attended across more than 80 concurrent sessions. The NLN also used the Summit to release a new Vision Statement on AI in Nursing Education, signaling that this is not a peripheral conversation for the profession. It is now a central one.

As an instructional designer and active RN, the Summit's focus sits directly at the intersection of my work. I design nursing curricula, build healthcare eLearning, and work with clinical faculty on assessment design. The questions being asked at the Summit are the same ones that show up in my projects daily: how much AI is appropriate in clinical education, where does it add genuine value, and where does it introduce risks that most organizations have not fully thought through?

Here are the conversations that stood out most and what they mean for nursing education and curriculum development going forward.

The Keynote Perspectives: Three Takes on the Same Problem

The Summit featured three keynote addresses that approached AI in nursing education from different angles, and together they gave a more complete picture than any single perspective could.

The Themes That Ran Through Everything

AI Augments Educators. It Does Not Replace Them.

The clearest consensus across all three keynotes and throughout the session feedback was that AI tools are most valuable when they improve feedback loops, enhance simulation, and support student assessment, not when they replace the educator's clinical judgment and relational presence. The sessions addressed this from multiple angles, but the message was consistent: the ethical, empathetic, and clinically complex dimensions of nursing education require human expertise that AI cannot replicate.

From my own work, this matches what I see in practice. AI-assisted content organization in Articulate Rise 360 is genuinely useful for structuring a module outline. It does not catch the cardiovascular physiology error in a clinical scenario that would mislead learners about hemodynamic management. That catch requires someone with ICU experience.

Simulation and Immersive Learning Are Moving to the Center

Multiple sessions highlighted how virtual reality, augmented reality, and high-fidelity simulation are no longer supplemental to nursing education. They are becoming central preparation platforms, particularly as clinical placement variability remains a persistent problem. The Summit underscored that data derived from simulation and VR performance is now accepted as educational evidence for competency assessment in many programs.

The practical implication for curriculum developers is significant. Simulation scenarios need to be designed with the same rigor as any other learning experience, with measurable learning objectives, NCJMM alignment, and structured debrief protocols. The technology investment only pays off if the instructional design behind it is sound.

Ethics, Bias, and Equity Cannot Be Afterthoughts

Several sessions raised substantive concerns about algorithmic bias in AI tools used for student assessment and clinical decision support. The concern is not theoretical: AI systems trained on non-representative datasets can produce recommendations that reflect and reinforce existing healthcare disparities. When those tools are deployed in nursing education, students may be learning from biased feedback systems without knowing it.

Equity in access to AI-enhanced education also surfaced as a real issue. Programs at under-resourced institutions may lack the infrastructure, faculty training, and financial capacity to implement the same tools being showcased at the Summit. The conversation about AI in nursing education cannot be separated from the conversation about which programs have access to it and which do not.

Faculty Development Is the Bottleneck

One of the most practically urgent themes was faculty development. AI implementation, simulation integration, and immersive learning all demand significant investment in faculty skill-building, not just hardware and software. Many nursing faculty have deep clinical expertise but limited exposure to the instructional technology tools now being integrated into their programs. Training faculty to use these tools well, including developing the debriefing skills that make simulation effective and the prompt engineering skills that make AI-assisted content development reliable, is the work that determines whether the technology investment returns value.

The Summit's emphasis on sustainable models for faculty development and institutional support was one of the more practical takeaways. An institution that invests in a high-fidelity simulation lab and then does not invest in training faculty to facilitate and debrief simulations effectively has spent a lot of money on underperforming equipment.

What This Means for Curriculum Design Right Now

The Summit's themes translate into several specific implications for anyone designing nursing curriculum or healthcare eLearning in late 2025:

• AI literacy belongs in nursing curriculum, not just in faculty development. Graduates entering practice in 2025 and beyond will work alongside AI clinical decision support tools daily. Curriculum that does not address how to evaluate, question, and override AI output is not preparing students for the practice environment they will actually enter.
• Simulation investment requires instructional design investment. Technology without design is expensive underperformance. Programs adding VR or high-fidelity simulation need scenario design expertise alongside equipment procurement.
• Bias review should be built into any AI-assisted content workflow. If AI tools are being used to generate assessment questions, scenario content, or learning objectives, a clinician needs to review the output for accuracy and a bias review should be part of the workflow, not an afterthought.
• The goal has not changed. As Dr. Bradley put it, nursing education exists to develop nurses who can think critically and care for patients safely. Every tool, AI or otherwise, is evaluated against that standard.