The field of large language models (LLMs) is rapidly evolving, with a growing focus on their application in various domains. Recent developments have seen LLMs being integrated into architectures for learning outcomes, social simulation, and collaborative learning, demonstrating their potential to support critical thinking, student autonomy, and emergent social ties. Notably, LLMs are being used to generate personalized feedback, organize collaborative interactions, and offer adaptive cognitive scaffolding. Furthermore, their implementation in agent-based simulations has shown promise in predicting social information diffusion and modeling realistic human behavior in complex systems. Overall, the field is moving towards exploring the capabilities of LLMs in enhancing human-centered applications. Noteworthy papers include: The paper on LLM-based in-situ thought exchanges for critical paper reading, which highlights the potential of LLMs in improving critical thinking skills. The paper on integrating LLM and diffusion-based agents for social simulation, which demonstrates a hybrid simulation framework that strategically integrates LLM-driven agents with diffusion model-based agents.