Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

It is possible to address learning challenges in a way that is unique to the needs and preferences of students through adaptive learning environments. Using these platforms, learning content can be personalized to reflect a user's interest, past knowledge, present abilities, and strengths and limitations. Conversely, the efficiency of adaptive learning systems depends on the techniques adopted to classify and present the content according to students’ needs and preferences. Artificial Intelligence (AI) techniques have recently been applied in personalized adaptive education systems to address content delivery-related learning challenges. However, not much is known about content adaptation based on bioinspired optimization algorithms. This study offers a comprehensive evaluation of the literature on personalized adaptive learning Management systems based on bioinspired optimization algorithms. The study examined conference proceedings and journal papers published in Scopus, Web of Science, and IEEE databases between 2013 and 2023. Nevertheless, Web of Science yielded no papers that were connected to this investigation. Web of Science was thus left out of the research. 5442 were screened in total, 303 were evaluated, and 6 were deemed eligible for the systematic review. Our findings suggest that there have been a limited number of research or personalized adaptive learning systems based on bioinspired algorithms.