This chapter explores how Artificial Intelligence (AI) and Machine Learning (ML) can enhance cognitive learning by providing ongoing, data-driven assessments of student progress. Within this domain, it reveals an essential technical gap in conventional education institutions: unlike more clinical environments, no system or classroom structure provides timely and adaptive feedback for personalized learning. In efforts to improve this process, we examine the use of AI technologies, including Natural Language Processing (NLP), Deep Learning (DL), and Predictive Analytics (PA), to observe cognitive performance, identify patterns of learning, and facilitate adaptive responses. This chapter describes the delineation of a structure, encouraging AI-informed alterations to assessment tools, to assess comprehension, engagement, and knowledge retention. To illustrate these approaches, we provide an AI-based application example that can identify cognitive overload or constraints in student learning, enabling timely and personalized support. A case study is presented to exemplify the practical application of these strategies, while fostering equitable learning environments that promote meaningful engagement and informed pedagogical responses. Finally, we consider the ethics of AI and education, keeping a focus on balancing automation and human responsibility to avoid a future of complacency. We discuss the challenges and opportunities for an AI cognitive educational system, in conjunction with the possibilities for future research and recommendations for ethical guidelines, to support this initiative and facilitate its deployment.