The recent advancements in the field of artificial intelligence, particularly in the context of Large Language Models (LLMs), have sparked significant discussions on the alignment between human and AI cognition, as well as the implications for creativity and education. A notable trend is the development of methods to measure and enhance the alignment between AI and human understanding, with a focus on representational similarity and interpretability. These methods aim to bridge the gap between AI's computational capabilities and human cognitive processes, offering insights into how AI models encode knowledge and make decisions. Additionally, there is a growing interest in understanding how AI impacts creativity, both as a tool for augmenting human capabilities and as a standalone creative entity. Studies are exploring the differential roles of human capital in AI-driven creative tasks, revealing complex interactions that challenge traditional notions of skill valuation and workforce hierarchies. Furthermore, the educational sector is witnessing innovations in detecting AI-generated content in assessments, with a focus on distinguishing between human and AI responses in multiple-choice questions. This development underscores the need for robust methods to ensure academic integrity in an era where AI tools are increasingly accessible. Overall, the field is moving towards a more nuanced understanding of AI's role in cognitive processes, creativity, and education, with a balance between leveraging AI's strengths and addressing its limitations.