No shortage of programs in the field
Today, if you typed the words “artificial intelligence in health care program” into a search engine, you will get a plethora of search results describing programs from schools like Stanford, MIT, University of Toronto, and many more. The role of artificial intelligence (AI) in health care education and practice even has its own place in the aptly named Healthcare Rounds, a series of webinars hosted by McMaster University’s School of Biomedical Engineering and Entrepreneurship. Interest in understanding and applying artificial intelligence in health care spaces seems to be having its moment in the sun.
Opportunities and pitfalls
Perhaps, but a literature search will also help us understand that technology mediated education in health care occupations is nothing new. Nursing education has used simulation for training since the 1990s. Since then, other technologies have become part of the way nursing education is delivered, including virtual reality, augmented reality, 360 video, and screen-based simulation (Aebersold, 2023). For those who are responsible for creating educational resources for nursing education, what opportunities and pitfalls does AI present?
One of the perceived benefits that we hear about when discussing machine learning is how to improve efficiency and productivity to free up human beings to perform tasks that machines cannot do as easily, or well. For example, an instructional designer can use AI to inform curriculum development and identify topics that should be integrated into course content. AI can also be used to help personalize or tailor assessments based on student performance. From a clinical perspective, AI can also support a nurse’s decision-making by analyzing data points to come up with the best course of action for a patient at a given time.
Learning outcomes and bias
As with most developments in learning and technology, there is need to review new practices with a critical lens. First, no matter how education is delivered, instructors need to be intentional about the desired learning outcomes intended. For example, if virtual reality isn’t the best tool to teach a certain skill, it shouldn’t be the only option for a student nurse to use to practice. With the use of AI, the presence of bias can profoundly alter the results of an analysis or recommendation. For example, in healthcare, if a certain ethnic population is overrepresented (or underrepresented) in the literature being analyzed, it is imperative to be aware of the biases being presented that could lead to poor health outcomes and experiences for marginalized groups (Aebersold, 2023).
Going forward, AI and other technology mediated instruction appear to be a growing part of training and development in health occupations. What isn’t as clear is how instructional designers will balance the benefits of using these tools, while still being learner-focused, and critically aware of the weaknesses in these tools.
References:
Aebersold, M., Gonzalez, L., (May 31, 2023) “Advances in Technology Mediated Nursing Education” OJIN: The Online Journal of Issues in Nursing Vol. 28, No. 2, Manuscript 6.