The field of human-AI collaboration is rapidly evolving, with a growing focus on the complex interplay between human factors and AI systems. Recent research has highlighted the importance of considering human cognition, behavior, and decision-making dynamics in the development of AI-assisted decision-making systems. The incorporation of human factors into adversarial analysis has been identified as a key area of research, with the potential to strengthen the robustness of AI-assisted decision-making. Furthermore, the development of novel robustness analysis frameworks and the investigation of human-AI collaboration in various contexts, such as workplace negotiations and daily planning, are advancing our understanding of the complex relationships between humans and AI systems. Noteworthy papers in this area include: Position: Human Factors Reshape Adversarial Analysis in Human-AI Decision-Making Systems, which introduces a novel robustness analysis framework and highlights the importance of human factors in adversarial analysis. Discovering Self-Regulated Learning Patterns in Chatbot-Powered Education Environment, which reveals imbalanced self-regulated learning behaviors in students interacting with chatbots and highlights the need for adaptive and personalized scaffolds. Human-AI Teaming Co-Learning in Military Operations, which proposes a trustworthy co-learning model for human-AI teaming in military operations, encompassing adjustable autonomy, multi-layered control, bidirectional feedback, and collaborative decision-making.