By By Personalized Learning Algorithms: Theoretical Foundations, Applications, and Future Directions
Sani Iyal A. & Afarasimu A. I.
Abstract
Personalized learning algorithms represent a revolutionary development in education
technology, allowing instructors to customize lessons based on each student’s individual
needs, preferences, and learning styles. This paper investigates the theoretical foundations,
practical uses, and potential impact on teaching and learning outcomes of personalized
learning algorithms. In our discussion of the essential elements of personalized learning
algorithms, we emphasize the importance of artificial intelligence methods, including
machine learning, natural language processing, and recommender systems. These methods
include learner modeling, content adaptation, and feedback mechanisms. We analyze the
success of personalized learning algorithms in a range of educational contexts, including
corporate training settings, higher education, and elementary classrooms, using empirical
research and case studies as examples. Additionally, we address significant issues and
challenges related to the development and deployment of personalized learning algorithms,
such as scalability, privacy, and equity problems. Lastly, we conclude by outlining prospects
for research and practice in this rapidly developing topic, stressing the need for
interdisciplinary collaboration and evidence-based approaches to fully utilize the potential of
personalized learning algorithms to enhance educational outcomes for all learners.
