AI-Driven Personalized Education Systems: Enhancing Adaptive Learning Through Intelligent Tutoring

The diligent move to e-learning has ensured the relevance of sophisticated learning systems that can cater to each student.Most traditional e-learning systems still stick to rigid templates that do not take individual learning and engagement levels and comprehension skills into consideration.Therefore, these students are liable to suffer from lack of motivation, low retention, and poor learning outcomes.In an attempt to mitigate these shortcomings, this paper proposes an AI-enabled personalized education system that adjusts students' learning experiences in real time by analyzing their behavior, performance, and preferences.To test the validity of AI powered education system, we carried out a vast number of real world experiments using elearning data sets.Our research focused on the central KPIs, including learning retention, user engagement, and overall learning productivity, contrasting AI-assisted learning to conventional learned models.Our findings indicate that the proposed system achieved 30% improvement in retention and 25% increase in engagement, which means that these features surpass traditional measures.Additionally, the content adapted approach has also minimized drop-out rates among students, as the content was more in-tune with their abilities and learners' interests.While the potential of AI systems in education is enormous, they Moreover, these technologies bring up important issues regarding data privacy, algorithmic bias, and an over dependence on AI-based instruction.To be able to trust intelligent tutoring systems, it is important to ensure fairness and transparency in AI generated recommendations.To address these issues, we use explainable AI approaches and privacy preserving techniques like federated learning, which enables decentralized model training without access to sensitive student information.