Hillary Washburn, EdD

I am most interested in using all available information to accurately diagnose workplace problems and design interventions that consider the whole system and deeply engage the people involved.

I approach artificial intelligence as an eager early adopter, a curious practitioner, and a disciplined evaluator. I use AI actively in my own work, and I am interested not only in what it can do, but in how we come to know what it can do, how we decide what it should do, and what responsibilities follow when we use it in systems that affect people's learning, work, health, and access to opportunity.

My perspective is grounded in responsible, evidence-based adoption rather than hype. I see AI as a powerful tool for extending human judgment, supporting analysis, improving access to expertise, and helping people work through complex problems. At the same time, its value depends on context, evidence, transparency, and the quality of the human judgment around it.

I am especially interested in the decision-making questions AI raises. What should we delegate, augment, automate, or protect from automation? When does AI help people think more clearly, and when does it make weak reasoning faster and harder to see? How do we evaluate whether an AI-supported process is improving learning, research, care, access, or decision-making, rather than simply increasing speed or output?

These questions are especially urgent in education, research, and healthcare, where AI is increasingly being used to support writing, analysis, feedback, communication, and judgment. In these settings, the central issue is not whether AI can produce plausible outputs. It is whether AI use strengthens human understanding, supports better decisions, protects trust, and remains accountable to the people it is meant to serve.

In learning design, I am interested in how AI can support authentic work without replacing the expertise that makes learning meaningful. AI can help teams brainstorm, analyze patterns, draft materials, test assumptions, and work more effectively with subject-matter experts. Used well, AI can make thinking more visible. Used poorly, it can obscure the difference between fluency and understanding.

My work with AI is rooted in this broader set of questions: how we build systems that help people reason, learn, and act with greater care. I co-authored the IACET white paper Leveraging Artificial Intelligence in Continuing Education and Training (2025), serve on the IACET AI in Learning and Development and Open Digital Credentials task forces, and have presented at AECT on using AI and subject-matter-expert workflows to design authentic learning.

My work spans education and health. As a patient-partner lead and site coordinator on the American Institutes for Research Patient-Centered Measurement Program, a Robert Wood Johnson Foundation-funded initiative, I helped implement patient-reported measures of respect and autonomy in maternity care across multiple sites, supporting providers and patient-partners in designing and carrying out the work. I represented the project at the National Quality Forum Patient and Family Advisor Summit and the PFCC Virtual Skills Exchange.

I have co-authored peer-reviewed research on patient decision aids, shared decision-making, and competing-interest disclosure, published in BMJ Evidence-Based Medicine and Contraception.