The field of Large Language Models (LLMs) is rapidly evolving, with a focus on improving code generation and education. Recent developments have shown that LLMs can be used to enhance code generation by leveraging bidirectional comment-level mutual grounding, allowing for more accurate and efficient code generation. Additionally, LLMs are being explored as tools to support K-12 teachers in culturally relevant pedagogy, assisting in adapting AI literacy curricula to students' cultural contexts. In the area of education, LLMs are being used to provide personalized feedback to students, with studies showing that usage patterns can significantly impact student learning and autonomy. Furthermore, LLMs are being integrated into design workflows, prompting students to reflect on creative responsibility and reliability. Noteworthy papers include Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding, which demonstrated a 17.1% pass@1 improvement for code-davinci-002 on HumanEval, and Will Your Next Pair Programming Partner Be Human?, which showed that students achieved the highest assignment scores when using LLM-based tools as collaborators in pair programming.