The field of AI-driven education is rapidly evolving, with a growing focus on personalized learning, ethical considerations, and the development of innovative educational technologies. Recent research has highlighted the potential of AI to enhance student learning outcomes, improve teacher support, and increase access to quality education. A key direction in this field is the integration of AI with existing educational frameworks, with a emphasis on creating more effective and efficient learning pathways. Another important area of research is the investigation of AI's impact on educational equity, with a focus on addressing the digital divide and ensuring that all students have access to AI-driven educational resources. Noteworthy papers in this area include: Uncertainty-Aware Knowledge Tracing Models, which demonstrates the importance of capturing predictive uncertainty in knowledge tracing models for improved student assessment. Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education, which proposes an AI-driven model for enhancing student progress modeling and delivering adaptive feedback. A principled way to think about AI in education: guidance for action based on goals, models of human learning, and use of technologies, which advances a framework for guiding the use of AI in teaching and learning.