The field of large language models (LLMs) is moving towards more innovative and efficient applications in educational content generation and contextual knowledge enhancement. Recent research has explored the potential of LLMs to automate the generation of extended reading materials and relevant course suggestions, streamlining the process of creating educational materials. Additionally, there is a growing focus on improving the context-faithfulness of LLMs, enabling them to better reflect contextual knowledge in their generations. Novel methods, such as Context-aware Layer Enhancement, have been proposed to enhance the utilization of contextual knowledge within LLMs' internal representations. Furthermore, lightweight verification approaches, like LiLaVe, have been introduced to efficiently assess the correctness of outputs generated by LLMs. Noteworthy papers in this area include: Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement, which proposes a novel intervention method to enhance contextual knowledge utilization in LLMs. Lightweight Latent Verifiers for Efficient Meta-Generation Strategies, which introduces a novel lightweight verification approach that reliably extracts correctness signals from the hidden states of the base LLM.