It is of great importance to integrate human-centered design concepts at the core of both algorithmic research and the implementation of applications. In order to do so, it is essential to gain an understanding of human–computer interaction and collaboration from the perspective of the user. To address this issue, this chapter initially presents a description of the process of human–AI interaction and collaboration, and subsequently proposes a theoretical framework for it. In accordance with this framework, the current research hotspots are identified in terms of interaction quality and interaction mode. Among these topics, user mental modeling, interpretable AI, trust, and anthropomorphism are currently the subject of academic interest with regard to interaction quality. The level of interaction mode encompasses a range of topics, including interaction paradigms, role assignment, interaction boundaries, and interaction ethics. To further advance the related research, this chapter identifies three areas for future exploration: cognitive frameworks about Human–AI Interaction, adaptive learning, and the complementary strengths of humans and AI.

Generative AI based on large language models (LLM) currently faces serious privacy leakage issues due to the wide range of parameters and diverse data sources. When using generative AI, users inevitably share data with the system. Personal data collected by generative AI may be used for model training and leaked in future outputs. The risk of private information leakage is closely related to the inherent operating mechanism of generative AI. This indirect leakage is difficult to detect by users due to the high complexity of the internal operating mechanism of generative AI. By focusing on the private information exchanged during interactions between users and generative AI, we identify the privacy dimensions involved and develop a model for privacy types in human–generative AI interactions. This can provide a reference for generative AI to avoid training private data and help it provide clear explanations of relevant content for the types of privacy users are concerned about.

As generative AI technologies continue to advance at a rapid pace, they are fundamentally transforming the dynamics of human–AI interaction and collaboration, a phenomenon that was once relegated to the realm of science fiction. These developments not only present unprecedented opportunities but also introduce a range of complex challenges. Key factors such as trust, transparency, and cultural sensitivity have emerged as essential considerations in the successful adoption and efficacy of these systems. Furthermore, the intricate balance between human and AI contributions, the optimization of algorithms to accommodate diverse user needs, and the ethical implications of AI’s role in society pose significant challenges that require careful navigation. This chapter will delve into these multifaceted issues, analyzing both user-level concerns and the underlying technical and psychological dynamics that are critical to fostering effective human–AI interaction and collaboration.