Advances in data analytics and intelligent technologies are enabling smart learning environments that promote personalized learning. Personalized learning systems where learners engage with information in a manner tailored to their unique needs, goals, and abilities have garnered significant academic research attention. If students can achieve their objectives faster than with traditional learning methods, it would increase their motivation and reduce their likelihood of dropping out. It can also offer educators a better understanding of each student's learning process, enabling them to teach more effectively. Artificial intelligence (AI) plays a vital role in the development of personalized learning systems. Rapid advancements in AI technologies enable tracking and modifying of each student's learning environment. Machine learning algorithms facilitate the determination of students' learning styles, abilities, and progress throughout the learning process. One of the major challenges to effective personalization is the resistance of machine learning models to adapt to non-stationary data streams. Machine learning models for personalized learning systems are susceptible to the concept drift phenomenon, a deterioration of the model's performance over time due to changes in data distribution. These arise due to factors affecting learning ability, including changes in family structure, parental involvement, peer relationships, learner behavior, personal interests, environmental influences such as nutrition and sleep, and so on. For successful personalization, it is critical that underlying predictive and classification models be able to adapt successfully to data changes that contribute to the drift phenomenon. This research proposes a method to address concept drifts in personalized learning systems that involve training using sequential features extracted automatically, noting when concept drifts are causing model deterioration, and automatically adjusting the trained model to improve model performance in the presence of drift. Unlike large language models (LLMs), which usually lack inherent capabilities for indicating that a concept drift has occurred, the approach presented in this dissertation can detect and point out instances of concept drift. Detecting concept drift is important for initiating specific interventions, whereas large language models tend to obscure or overlook such changes in the data. The proposed approach aims to enhance the accuracy and effectiveness of predictive models, ensuring personalized learning systems deliver pertinent and useful recommendations even when student preferences change. While conducting experiments using a real-world dataset related to students' interactions with educational systems, the proposed model shows impressive results in managing concept drifts. Moreover, the proposed model shows resilience against two major types of drift: incremental and sudden drifts. This indicates that by using the proposed approach, we can ensure that the predictive models maintain their effectiveness in the presence of different types of drift. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]