The field of AI-enhanced education and energy efficiency is rapidly evolving, with a focus on developing personalized and adaptive learning systems. Recent studies have explored the potential of large language models (LLMs) in generating pedagogically rich interactions, evaluating their performance in tasks such as tutoring and annotation. Notably, LLMs have demonstrated promising results in energy retrofit decision-making, with some models showing high accuracy and consistency in recommending effective retrofits. Furthermore, the integration of generative AI into cybersecurity education has shown significant potential in enhancing student comprehension and skill acquisition. Overall, the field is moving towards the development of more sophisticated and human-like AI systems that can provide personalized support and guidance in various domains. Noteworthy papers include: RAG-PRISM, which presents a personalized and adaptive tutoring framework that combines generative AI with Retrieval-Augmented Generation. DeepSeek, which emerges as a top performer in analyzing longitudinal dental case vignettes, demonstrating superior faithfulness and higher expert ratings. IDEAlign, which introduces a scalable evaluation paradigm for comparing LLM-generated annotations with expert human annotations, showing significant improvements in alignment with expert judgments.