Welcome to the Handbook of Human–AI Collaboration. This reference work, published with Springer Nature, brings together leading scholars and practitioners to provide a comprehensive overview of the principles, methods, and societal implications of collaboration between humans and advanced AI systems. The handbook adopts a human-centered AI perspective, which now underpin a wide range of collaborative technologies. It opens with the Foundations of Human–AI Collaboration, covering the historical, theoretical, and computational bases of joint human–AI activity, as well as design principles for effective collaboration and interfaces. The section on Multimodal Foundation Models introduces the architectures and capabilities of today’s large-scale generative systems, including models for text, vision and audio. It examines how these systems perceive, generate, and integrate information across modalities to support human tasks. In Learning and Reasoning with Foundation Models, the handbook explores emerging approaches to cognitive augmentation, personalized learning, and hybrid reasoning systems. Topics include skill composition, retrieval-augmented generation, and frameworks for extending human and machine reasoning capacities. The Evaluation of Human–AI Collaborations section provides methodological and empirical tools for assessing interaction quality, collaborative performance, human cognitive impacts, and benchmarking practices. It highlights metrics and evaluation strategies suited to dynamic human–AI teams. The section on Co-evolution of AI Systems and Society analyzes the broader transformations brought about by Foundation Models. Contributions address medium- and long-term societal effects, network dynamics in distributed collaborative learning, and the reconfiguration of tasks and roles in human–AI ecosystems. Finally, the Ethical, Legal, and Social Aspects section examines governance models, regulatory frameworks, ethical considerations, and socio-technical risks associated with generative and general-purpose AI. Topics include global governance, the EU’s legal architecture for AI, transparency and equity in metrics, sustainability, misinformation, and sectoral impacts in domains such as work, healthcare, and education. Together, these contributions offer an integrated perspective on how humans and AI systems learn, reason, act, and evolve jointly. The handbook aims to serve as a core reference for researchers, developers, policymakers, and practitioners seeking to understand and shape the future of Human–AI Collaboration. We look forward to working closely with the editorial board and all contributing authors throughout the development of this important volume.

Handbook of Human-AI Collaboration

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2026年

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摘要

Welcome to the Handbook of Human–AI Collaboration. This reference work, published with Springer Nature, brings together leading scholars and practitioners to provide a comprehensive overview of the principles, methods, and societal implications of collaboration between humans and advanced AI systems.

The handbook adopts a human-centered AI perspective, which now underpin a wide range of collaborative technologies. It opens with the Foundations of Human–AI Collaboration, covering the historical, theoretical, and computational bases of joint human–AI activity, as well as design principles for effective collaboration and interfaces.

The section on Multimodal Foundation Models introduces the architectures and capabilities of today’s large-scale generative systems, including models for text, vision and audio. It examines how these systems perceive, generate, and integrate information across modalities to support human tasks.

In Learning and Reasoning with Foundation Models, the handbook explores emerging approaches to cognitive augmentation, personalized learning, and hybrid reasoning systems. Topics include skill composition, retrieval-augmented generation, and frameworks for extending human and machine reasoning capacities.

The Evaluation of Human–AI Collaborations section provides methodological and empirical tools for assessing interaction quality, collaborative performance, human cognitive impacts, and benchmarking practices. It highlights metrics and evaluation strategies suited to dynamic human–AI teams.

The section on Co-evolution of AI Systems and Society analyzes the broader transformations brought about by Foundation Models. Contributions address medium- and long-term societal effects, network dynamics in distributed collaborative learning, and the reconfiguration of tasks and roles in human–AI ecosystems.

Finally, the Ethical, Legal, and Social Aspects section examines governance models, regulatory frameworks, ethical considerations, and socio-technical risks associated with generative and general-purpose AI. Topics include global governance, the EU’s legal architecture for AI, transparency and equity in metrics, sustainability, misinformation, and sectoral impacts in domains such as work, healthcare, and education.

Together, these contributions offer an integrated perspective on how humans and AI systems learn, reason, act, and evolve jointly. The handbook aims to serve as a core reference for researchers, developers, policymakers, and practitioners seeking to understand and shape the future of Human–AI Collaboration.

We look forward to working closely with the editorial board and all contributing authors throughout the development of this important volume.