The field of AI-enhanced education and large language models is rapidly evolving, with a focus on improving personalized learning, educational feedback, and teacher preparation. Recent developments highlight the potential of multi-agent collaborative platforms, AI-powered chatbots, and large language models in enhancing coding education, physics teacher education, and writing feedback. Noteworthy papers in this area include those that explore the strategic integration of AI chatbots in physics teacher education, the evaluation of large language models in astronomy, and the impact of perceived feedback source on pre-service teachers' feedback perception and uptake. Overall, these advancements demonstrate the potential of AI-enhanced education to improve learning outcomes, support teacher development, and enhance the overall educational experience. Notable papers include:

• CodeEdu, which introduces a multi-agent collaborative platform for personalized coding education, demonstrating substantial enhancements in students' coding performance.
• Decoding Instructional Dialogue, which presents a human-AI collaborative methodology for large-scale qualitative analysis of educator-AI interactions, revealing substantive patterns in how educators use AI to enhance instructional practices.