The field of human-AI collaboration is moving towards a deeper understanding of the complex interactions between humans and artificial intelligence. Recent research has highlighted the importance of task structure in determining the effectiveness of AI-human collaboration, with modular and sequenced tasks requiring different approaches. The development of novel frameworks and models has enabled the simulation of AI-human collaboration, providing insights into the complementary roles of humans and AI in decision-making processes. Furthermore, the integration of technical details from multi-agent coordination, knowledge management, and cybernetic feedback loops has led to the proposal of new conceptual architectures for human-AI collaboration. Noteworthy papers include:

• A study on modeling AI-human collaboration as a multi-agent adaptation, which reveals the importance of task decomposition in strategic decision-making.
• A position paper proposing a novel conceptual architecture for human-AI collaboration, which systematically interlinks technical details from various fields.
• A paper on optimal interactive learning on the job via facility location planning, which introduces a multi-task interaction planner that minimizes human effort.
• A study investigating whether large language models can improve analogical reasoning for strategic decisions, which suggests a productive division of labor between AI and humans.