How will AI & machine learning change medical education?

 

The value proposition of adaptive and active learning models is not lost on today’s medical schools. Backed by a growing body of evidence, these models are improving knowledge retention and graduation rates as well as helping students improve scores on key exams and boards.

The impact is understandable. As lecture halls give way to learning environments that leverage multimedia, collaboration and personalized approaches to assignments, medical students are becoming more engaged and empowered to learn when, where and how it is most efficient.

Artificial intelligence (AI) is playing a significant role in advancing these models. A term that has become colloquially interchangeable with machine learning, or the ability of computers to become smarter over time, AI capabilities used within the confines of an education platform help instructors evaluate and predict student performance and address gaps to improve learning and outcomes. They empower students to master skills at their own pace, turning mistakes into learning opportunities and ultimately generating efficiencies across educational processes by reducing mistakes on exams for example.

Forward-looking higher education institutions are already considering how to integrate these evolving innovations as they prepare for the future of personalized medical education. Use of advanced learning models supported by smarter technological frameworks will continue to become an important component in curriculum design that fosters success.

The AI advantage for students
Adaptive learning is best defined as a non-linear approach to education that considers individual performance data among other factors to design and adjust the learning environment to best suit a student’s needs and overall learning progress. Conceptually, this approach runs in sharp contrast to a conventional “one size fits all” approach that has existed for decades.

Complementing adaptive models, active learning strategies strive to improve class participation and interaction, with the larger goal of fostering critical thinking and clinical reasoning. In active learning, students become responsible for their own learning path and progress. They must get engaged, set learning objectives, and solve problems, working together with other students to foster discussions around critical thinking. Here, instructors place emphasis more on developing skills than simple knowledge acquisition to help students engage in higher-level thought processes.

With the right infrastructure in place, AI can power these approaches in a more intuitive and impactful way. For instance, one efficacy study found students using a solution built on a strong foundation of AI studied more efficiently and scored nearly one standard deviation higher on standardized certification exams—including the United States Medical Licensing Examination (USMLE). Building from a USMLE Step 1 practice exam and daily review algorithms, the platform was able to precisely benchmark student success potential and then design learning to address strengths and weaknesses.

Significant positive correlations have also been established between engagement on adaptive flashcard-style questions with both weekly formative quizzes, and high-stakes summative exams. Multiple efficacy studies have also established a strong correlation between self-reported mastery of key medical concepts and performance on related higher-order clinical case-style questions.

In other words, by aligning learning to student skill and adjusting question difficulty, the platform ensured that students had a greater chance of answering the related clinical case question correctly. AI and machine learning make these improvements and efficiencies possible.

Taking hold of better education infrastructures
Research backing the promise of AI and adaptive learning speaks for itself. For example, studies published by the American Psychological Association demonstrate that the traditional, formal chapter-by-chapter approach to learning is less effective with 21st Century students. Instead, they fare better with an experiential and exploratory approach that incorporates multimedia, collaboration, personalized and customized assignments, which build foundational knowledge and increase retention.

Active learning approaches have also been associated with greater academic achievement as evidenced by the findings of a study in Proceedings of the National Academy of Sciences (PNAS) Online that suggested active learning increased exam performance while lecturing increased failure rates.

The good news is that adaptive learning platforms are already beginning to deliver the promise of AI and machine learning to help medical students master course work, succeed on exams and become confident in delivering the best quality, evidence-based care to their patients. Higher education institutions are using these solutions for a variety of clinical specialties—including nursing—and realizing the positive impact.

These solutions provide a comprehensive, personalized study plan mapped to the school’s curriculum, ensuring ongoing retention of course content and remediating in areas of deficiency. Through use of virtual simulations, advanced quizzing mechanisms and evidence-based practice information, advanced solutions are equipping students with the experience and critical thinking skills needed for fast-paced and evolving clinical environments. They also equip faculty with the sophisticated analytics needed to assess students, benchmark progress and determine student and classroom preparedness for finals and certification exams.

Learning institutions just beginning to explore adaptive and active learning should start with a plan. An essential first step is to review current program and course outcomes to determine where advanced learning models can make the greatest difference. The key is taking the first step now in order to be ready for the future of medical education and ensuring advanced learning platforms that are built on AI are a central part of that equation.

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