The impact of adaptive educational platforms with artificial intelligence elements on the learning efficiency of higher education students

Abstract

The purpose of the article is to analyze the potential and effectiveness of using adaptive educational platforms with artificial intelligence elements in the medical training system, with a focus on their impact on students’ learning outcomes. Methods: analysis and synthesis to review scientific literature on the use of educational platforms in medical training of students; comparative analysis to compare the functionality of adaptive platforms and to identify their advantages and disadvantages.The study’s results demonstrate that adaptive educational platforms incorporating AI elements represent an innovative tool in higher medical education, capable of transforming content delivery and providing individualization tailored to the cognitive characteristics of each learner. The uniqueness of such platforms lies in the ability of AI algorithms to self-learn, predict difficulties in material assimilation, form personalized learning trajectories, and create conditions for the visualization, modeling, and simulation of clinical situations. It was found that the specifics of professional training of medical students require a high level of cognitive integration of knowledge and the ability to make clinical decisions quickly, applying critical and analytical thinking. Therefore, in this context, the proposed adaptive educational platforms – Osmosis, AMBOSS, Lecturio, Smart Sparrow, and Ada Health – demonstrate high effectiveness due to their capacity to model real clinical situations, conduct real-time knowledge assessment, and develop clinical thinking through the practice-oriented application of theoretical knowledge. Conclusions. Adaptive educational platforms with AI elements not only enhance the effectiveness of knowledge assimilation but also significantly transform approaches to developing professional competence in future medical professionals. Prospects for further research include empirical evaluation of the impact of AI functions on the dynamics of educational outcomes in real learning environments.