UMSN is Building the Future of AI in Health Care

Artificial intelligence is transforming every corner of health care—from diagnostics to documentation—but at the University of Michigan School of Nursing (UMSN), the focus is clear: nurses should be at the center of that transformation. Rather than reacting to change, UMSN is staying ahead of the technological curve and empowering nurses to help shape the future of care, starting with AI.

That’s the driving force behind the Nurse AI Trainer (NAIT). Developed in partnership with the Statistical Online Computational Resource (SOCR), NAIT introduces nursing students to the world of AI in a way that’s approachable, hands-on, and grounded in real nursing practice. Whether it’s used to simulate complex clinical situations, improve workflow planning or generate patient education materials, the tool is helping nurses to think more critically—and creatively—about the role AI can play in their work.

“AI can unlock new ways of thinking,” said Dr. Dinov, professor and chair of UMSN’s Department of Systems, Populations and Leadership and co-leader of the NAIT project. “It gives nurses a space to experiment, explore ideas, tinker with, and visualize care models that may be impractical to test directly in clinical settings. UMSN faculty guide nursing students to simulate and experiment in a safe virtual environment that encourages innovation without real-world risks.”

The NAIT app is meticulously designed to offer comprehensive training modules, interactive resources and leading-edge tools specifically for nursing professionals. Its core mission is to support active learning and foster a deep understanding of both foundational and generative AI—their applications, principles and ethical considerations in nursing practice. By using interactive methods, NAIT empowers nurses to understand AI and incorporate it ethically and effectively into education, research, clinical scholarship and community service.

UMSN has developed a suite of training models within NAIT, guided by faculty who are part of the Nurse AI Trainer leadership team. These focus on key areas like risk mitigation—helping nurses identify and manage risks of AI in clinical settings; incorporating AI tools in ways that enhance care without replacing human expertise; continuous quality improvement, showing how AI can drive better care outcomes; and ethical AI use, teaching responsible and transparent data and technology management.

NAIT also highlights AI’s role across nursing practice, from clinical decision support tools that analyze patient data and suggest treatments, to administrative assistants that reduce paperwork, to patient monitoring systems that track vital signs in real time. AI-powered education tools help personalize patient learning, and predictive analytics aid proactive care to prevent complications. These applications help nurses deliver safer, more effective and more personalized care every day.

Transforming the Learning Experience at UMSN with AI

The same innovative spirit behind NAIT is reshaping the classroom. The Michigan Intelligent Teaching Assistant, or MITA, is a generative AI tool designed to support faculty and students throughout a course. Created with SOCR and developed with key contributions from UMSN faculty, including Drs. Michelle Aebersold, vice chair for Research, Department of Systems, Populations and Leadership and John Knight, clinical assistant professor, MITA offers tutoring, answers FAQs, helps with grading and organizes course materials—all to enhance the learning experience, not replace the educator.

The idea for MITA was born from two powerful motivations: the need to provide sustainable, high-quality education to all students, what former U-M President James B. Angell once called “uncommon education for the common folk”—despite limited resources, and the exciting opportunities to innovate by using foundational and generative AI models.

While the initial idea came fairly easily, turning it into reality involved a much larger push involving planning, designing, testing, analyzing, redesigning and constantly improving the system. Partnering with transdisciplinary students and faculty colleagues across departments was key to making MITA what it is today.

“It’s [MITA] designed to free up time so instructors can spend more of it mentoring, guiding and connecting with students,” Dr. Dinov explained. “It’s an enhancement for the human relationship at the core of education.”

Theoretically, this tool will provide students with timely support outside of class hours, while faculty enjoy streamlined workflows that create room for deeper engagement. For educators curious about AI but unsure where to start, the MITA Transition Feasibility Calculator offers a thoughtful entry point. It helps instructors model different course scenarios and anticipate impacts on workload, cost and student satisfaction before making changes. This reflects a broader effort to ensure AI is used deliberately and transparently, with both learners and educators in mind.

Keeping Humanity at the Heart of Innovation

Amid all the excitement about AI, UMSN remains steady in its belief that nursing is, and always will be, a human profession. UMSN faculty are integrating AI into practice and teaching while actively researching its effects on the human side of health care.

“Even as we adopt powerful new tools, we must continue to teach the values and practices that put people first,” said Dr. Dinov. “Our goal isn’t to turn nurses into data scientists. It’s to prepare them to lead confidently in a world where data, technology and compassion must work together.”

What’s Next: Pioneering the Future of Academic Support

Bringing MITA into everyday teaching requires careful planning and ongoing checks to make sure it meets the school’s goals while honoring nursing’s human values. UMSN has a golden opportunity to lead this transformation, balancing new technology with its mission to educate compassionate nurses.

The plan unfolds in phases over the next two years. The first phase, lasting about 6 – 12 months, focuses on building the foundation—setting up login systems, course tools and basic AI features for a few pilot courses.

The second phase, from 12 – 18 months, involves refining MITA’s abilities by adding features like automated grading, personalized learning paths, and interactive learning platforms (ILPs), while expanding to more classes. The third phase, spanning 18 – 30 months, brings full integration with the university’s learning system, broader support for faculty and students, and formal training programs.

Finally, an ongoing phase will maintain and enhance MITA, introducing new features and continuously monitoring its impact to ensure it supports both students and educators effectively.