@inproceedings{chen-2025-human,
    title = "Human{--}Agent Teaming for Higher-Order Thinking Augmentation",
    author = "Chen, Chung-Chi",
    editor = "Heinzerling, Benjamin  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Tutorial Abstract",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-tutorials.4/",
    pages = "20--24",
    ISBN = "979-8-89176-302-9",
    abstract = "Human-agent teaming refers to humans and artificial agents working together toward shared goals, and recent advances in artificial intelligence, including large language models and autonomous robots, have intensified interest in using these agents not only for automation but also to augment higher-order cognition. Higher-order thinking involves complex mental processes such as critical thinking, creative problem solving, abstract reasoning, and metacognition, and intelligent agents hold the potential to act as genuine teammates that complement human strengths and address cognitive limitations. This tutorial synthesizes emerging research on human-agent teaming for cognitive augmentation by outlining the foundations of higher-order thinking and the psychological frameworks that describe it, reviewing key concepts and interaction paradigms in human{--}AI collaboration, and examining applications across education, healthcare, military decision-making, scientific discovery, and creative industries, where systems such as language models, decision-support tools, multi-agent architectures, explainable AI, and hybrid human{--}AI methods are used to support complex reasoning and expert judgment. It also discusses the major challenges involved in achieving meaningful augmentation, including the calibration of trust, the need for transparency, the development of shared mental models, the role of human adaptability and training, and broader ethical concerns. The tutorial further identifies gaps such as limited evidence of long-term improvement in human cognitive abilities and insufficient co-adaptation between humans and agents. Finally, it outlines future directions involving real-time cognitive alignment, long-term studies of cognitive development, co-adaptive learning systems, ethics-aware AI teammates, and new benchmarks for evaluating collaborative cognition, offering a comprehensive overview of current progress and a roadmap for advancing human-agent teaming as a means of enhancing higher-order human thinking."
}