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.

Nowadays, artificial intelligence (AI) is becoming a powerful tool to process huge volumes of data generated in scientific research and extract enlightening insights to drive further explorations. The recent trend of human-in-loop AI has promoted the paradigm shift in scientific research by enabling the interactive collaboration between AI models and human experts. Inspired by these advancements, this chapter explores the transformative role of AI in accelerating scientific discovery across various disciplines such as mathematics, physics, chemistry, and life sciences. It provides a comprehensive overview of how AI is reshaping the scientific research – enabling more efficient data analysis, enhancing predictive modeling, and automating experimental processes. Through the examination of case studies and recent developments, this chapter underscores AI’s potential to revolutionize scientific discovery, providing insights into current applications and future directions. It also addresses the ethical challenges associated with AI in science. Through this comprehensive analysis, the chapter aims to provide a nuanced understanding of how AI is facilitating scientific discovery and its potential to accelerate innovations while maintaining rigorous ethical standards.

Informal caregivers such as family members or friends provide much care to people with physical or cognitive impairment. To address challenges in care, caregivers often seek information online via social media platforms for their health information wants (HIWs), the types of care-related information that caregivers wish to have. Some efforts have been made to use Artificial Intelligence (AI) to understand caregivers’ information behaviors on social media. In this chapter, we present achievements of research with a human–AI collaboration approach in identifying caregivers’ HIWs, focusing on dementia caregivers as one example. Through this collaboration, AI techniques such as large language models (LLMs) can be used to extract health-related domain knowledge for building classification models, while human experts can benefit from the help of AI to further understand caregivers’ HIWs. Our approach has implications for the caregiving of various groups. The outcomes of human–AI collaboration can provide smart interventions to help caregivers and patients.

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.