This Open Access handbook provides a comprehensive and interdisciplinary perspective on Human–AI Collaboration, bringing together contributions from computer science, cognitive science, philosophy, social sciences, law, and design. As Foundation Models and Generative AI transform the capabilities of intelligent systems, understanding how humans and AI systems can effectively learn, reason, create, and make decisions together has become a central scientific and societal challenge.

The handbook begins by examining the foundations of Human–AI Collaboration, exploring the collaborative nature of intelligence and the cognitive, computational, philosophical, and design principles that underpin effective partnerships between humans and AI systems. It also highlights the importance of explainability and decision support in building trustworthy and collaborative AI.

The technological foundations of contemporary AI systems are presented through multimodal Foundation Models capable of processing and generating text, images, speech, audio, and code. Building upon these advances, the volume investigates how generative AI can augment human cognition and support creativity, problem solving, software development, and knowledge work.

Contributors further explore learning and reasoning with Foundation Models, including personalized learning, retrieval-augmented generation, and compositional reasoning frameworks. Methodologies and metrics for evaluating Human–AI interaction and teamwork are presented, together with benchmarking approaches for assessing collaborative performance.

Beyond technical aspects, the handbook examines the co-evolution of Human–AI Collaboration and society, addressing the long-term impact of generative AI, changing patterns of task allocation, and the emergence of distributed collaborative ecosystems. Ethical, legal, and governance dimensions are also discussed, including transparency, fairness, accountability, sustainability, misinformation, and responsible innovation in domains such as healthcare, education, work, and democratic participation.

By providing an integrated view of theory, technology, cognition, evaluation, society, and governance, this handbook offers a critical roadmap for researchers, practitioners, policymakers, and students seeking to understand and shape the future of collaborative intelligence.

Prof. Mohamed Chetouani is currently a Full Professor in signal processing and machine learning for human-machine interaction. He is the Deputy Director the Institute of Intelligent Systems and Robotics (CNRS UMR 7222), Sorbonne University (formerly Pierre and Marie Curie University). His activities cover social signal processing, social robotics and interactive machine learning with applications in psychiatry, psychology, social neuroscience and education. He was the coordinator of the ANIMATAS H2020 Marie Sklodowska Curie European Training Network (2018-2022). He was the President of the Sorbonne University Ethics Committee from 2019 to 2023. He was involved in several educational activities including organization of summer schools. He is member of the EU Networks of Human-Centered AI (HumanE AI NET) and Robotics (euROBIN). He was General Chair of ACM ICMI 2023 and VIHAR 2021. He is currently serving as Associate Editor of IEEE Transactions on Affective Computing. He co-published several books and proceedings, including the Human-Centered Artificial Intelligence (Springer, 2023). He is in charge of the inclusion of Students with Disabilities for the Faculty of Science and Engineering of Sorbonne University.

Professor Andrzej Nowak is a Full Professor of Psychology at both Florida Atlantic University and the University of Warsaw, where he is also the Director of the Center for Complex Systems and New Technologies, holding a Ph.D. in psychology.He has authored or edited over 15 books and numerous influential publications in top-tier journals in psychology, physics, and interdisciplinary journals.  His current research concentrates on artificial intelligence (AI), complex systems, human-computer interactions, social influence, social media, social influence, and self-structure. Dr. Nowak's pioneering work on AI includes developing models of feedback control in artificial neural networks, which has significantly advanced our understanding of how these systems can dynamically change their properties by adjusting to incoming information.  He has significantly contributed to understanding complex social systems, group dynamics, and the psychological foundations of human behavior through computational models and AI technologies. His work not only advances the field of psychology but also contributes to interdisciplinary dialogues between psychologists, computer scientists, physicists, and policymakers on how social interactions and AI shape human behavior and social processes.