The Adaptive Personalization Theory of Learning: Revolutionizing Education with AI

Abstract

This study investigates the potential of artificial intelligence (AI) to revolutionize personalized learning by developing and empirically evaluating the Adaptive Personalization Theory of Learning (APT) model. The APT paradigm uses AI-powered personalized learning algorithms, real-time adaptive assessments, learner engagement strategies, cognitive scaffolding, and ethical safeguards to provide flexible, personalized learning experiences. The study confirms the model's constructs through qualitative methods such as case studies, interviews, and classroom observations. It illustrates how AI improves learning outcomes by continuously tailoring content and evaluations to individual learner needs. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. However, the study also identifies ethical concerns, such as potential biases in AI algorithms, emphasizing the significance of establishing transparent, fair systems. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research, emphasizing long-term impacts, ethical frameworks, and practical implementation tactics, and establishing the APT model as a significant contribution to AI in education.