The recent advancements in the field of software engineering and computing education have seen a significant shift towards leveraging Large Language Models (LLMs) for various tasks, including bug fixing, fault localization, and synthetic data generation. The integration of LLMs into these processes aims to enhance automation, accuracy, and privacy preservation. Notably, there is a growing emphasis on the development of flexible frameworks that can adapt to different types of bug-related information and work with open-source LLMs, addressing the limitations of proprietary models in terms of data privacy. Additionally, the use of LLMs to generate synthetic data for educational purposes is gaining traction, offering a promising solution for creating large-scale, privacy-preserving datasets. These developments not only advance the technical capabilities of software maintenance and education but also contribute to more efficient and effective learning environments. Furthermore, the continuous learning capabilities of LLMs are being explored to improve the reproduction of defective code, ensuring that models can adapt to unique and evolving errors specific to individual repositories. Overall, the field is moving towards more adaptive, privacy-conscious, and educationally supportive tools and methodologies.