The increasing use of digital tools in education has opened new opportunities to enhance student engagement, particularly for neurodivergent learners who face challenges with traditional instructional methods. Attention measurement is a critical aspect of adaptive learning, yet current approaches rely on static models that fail to capture real-time cognitive fluctuations. This paper presents a literature review on AI-driven attention tracking and adaptive learning systems. We analyse key methodologies, including eye-tracking, head-pose estimation, and machine learning-based engagement models, highlighting their advantages and limitations. Our findings emphasize the need for more robust, personalized AI systems that dynamically adjust content to improve student concentration. This review sets the foundation for future research on AI-driven adaptive education tailored to individual cognitive profiles.