This book systematically explores the dynamics of human–AI interaction and collaboration across various domains, addressing both opportunities and challenges posed by AI technology. It begins with an introduction to the background and importance of human–AI collaboration, followed by an examination of design principles and user-centric methods for improving collaboration through optimized interaction experiences. The book delves into privacy and credibility concerns within generative AI applications, proposing methods to identify user privacy and assess credibility.

Further chapters focus on AI’s role in enhancing crowdsourcing knowledge sharing, and how AI can improve users’ search experience and understanding. The discussion extends to AI’s involvement in both the spread and control of misinformation, highlighting strategies for mitigating its impact.

Additionally, the book explores the practical applications of human–AI collaborative intelligence in industries such as healthcare, manufacturing, and scientific research. Case studies demonstrate AI’s potential in improving work efficiency, solving complex problems, and facilitating personalized health services.

In its later chapters, the book tackles the challenges generative AI faces in real-world applications, offering suggestions for improvement. It concludes with a summary of the key insights and future research directions, aiming to provide academia and industry with valuable guidance to promote the development and responsible application of AI technology.