The field of education is witnessing a significant shift towards personalized and adaptive learning, driven by advancements in artificial intelligence (AI) and machine learning (ML). Recent developments have focused on creating dynamic, feedback-driven, and personalized learning environments that cater to individual students' needs and abilities. One of the key trends is the integration of human-in-the-loop systems, which leverage generative AI to enhance student engagement and understanding. Another area of innovation is the use of AI-powered tools to generate personalized educational content, such as distractors and microlearning materials, that can help identify and address knowledge gaps. Additionally, researchers are exploring the potential of AI-driven approaches to improve student retention and engagement, particularly in STEM education. Noteworthy papers in this area include: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction, which proposes a novel approach to generating tailored distractors based on individual misconceptions. RPKT: Learning What You Don't -- Know Recursive Prerequisite Knowledge Tracing in Conversational AI Tutors for Personalized Learning, which presents a system that dynamically discovers prerequisite concepts in real-time to identify knowledge gaps.