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.

This chapter mainly investigates the role of Artificial Intelligence (AI) in augmenting search interactions to enhance users’ understanding across various domains. The chapter begins by examining the current limitations of traditional search interfaces in meeting diverse user needs and cognitive capacities. It then discusses how AI-driven enhancements can revolutionize search experiences by providing tailored, contextually relevant information and facilitating intuitive interactions. Through case studies and empirical analysis, the effectiveness of AI-supported search interaction in improving users’ understanding is evaluated in different scenarios. This chapter contributes to the literature on AI and human–computer interaction by highlighting the transformative potential of AI in optimizing search experiences for users, leading to enhanced comprehension and decision-making. It concludes with implications for research and practice, emphasizing the importance of human-centered design principles in developing AI-driven search systems.

AI-supported crowdsourcing for knowledge sharing is a collaborative approach that leverages artificial intelligence (AI) technologies to facilitate the gathering, organizing, and sharing of information or expertise among a large group of people, known as crowd workers. Despite the growing body of research on motivations in crowdsourcing, the impact of AI-supported crowdsourcing on workers’ motives remains unclear, as does the extent to which their participation can effectively address societal challenges. A systematic review is first conducted to identify trends and gaps in AI-supported crowdsourcing. This chapter then employs a case study through a crowdsourcing platform to look for missing children to demonstrate the pivotal role of AI in crowdsourcing in managing a major societal challenge. Emerging trends and technologies shaping motivations in AI-supported crowdsourcing will be discussed. Additionally, we offer recommendations for practitioners and researchers to integrate AI into crowdsourcing projects to address societal challenges.

This chapter aims to provide a comprehensive overview of the current state of credibility research in human–generative AI interactions by analyzing literature from various disciplines. It begins by exploring the key dimensions of credibility assessment and provides an overview of two main measurement methods: user-oriented and technology-oriented. The chapter then examines the factors that influence human perceptions of AI-generated content (AIGC), including attributes related to data, systems, algorithms, and user-specific factors. Additionally, it investigates the challenges and ethical considerations involved in assessing credibility in human–generative AI interactions, scrutinizing the potential consequences of misplaced trust in AIGC. These risks include concerns over security, privacy, power dynamics, responsibility, cognitive biases, and the erosion of human autonomy. Emerging approaches and technological solutions aimed at improving credibility assessment in AI systems are also discussed, alongside a focus on domains where AI credibility assessments are critical. Finally, the chapter proposes several directions for future research on AIGC credibility assessments.

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.

Misinformation on social media is a recognized threat to societies. Research has shown that social media users play an important role in the spread of misinformation. It is crucial to understand how misinformation affects user online interaction behavior and the factors that contribute to it. In this study, we employ an AI deep learning model to analyze emotions in user online social media conversations about misinformation during the COVID-19 pandemic. We further apply the Stimuli–Organism–Response framework to examine the relationship between the presence of misinformation, emotions, and social bonding behavior. Our findings highlight the usefulness of AI deep learning models to analyze emotions in social media posts and enhance the understanding of online social bonding behavior around health-related misinformation.

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.

David Freeman Engstrom (Stanford) and Daniel B. Rodriguez (Northwestern) argue that current structure of American legal services regulation, known as “Our Bar Federalism,” is outdated. Fifty states maintain their own rules and regulatory apparatus for a legal profession and industry that are now national and multinational. This fragmented system is a key factor in the American civil justice system’s access-to-justice crisis, where restrictive state rules support the lawyers’ monopoly. With new legal services delivery models and AI, this scheme will seem increasingly provincial and retrograde. This chapter argues it’s time to rethink "Our Bar Federalism," and explore hybrid state-federal regulatory system.

David Engstrom and Jess Lu (both Stanford Law) first show that an otherwise fast-growing and dynamic “legal tech” industry has not generated significant “direct-to-consumer” technologies designed to help self-represented litigants navigate a complex legal system. They then interrogate that puzzle: Why is it that better consumer legal tech hasn’t flourished? They ultimately settle on the idea that rule reforms alone may not stimulate high-scale, direct-to-consumer technology. Instead, other policy interventions may be necessary, including standardizing what is currently a checkerboard of court technology and data infrastructures. Perhaps more importantly, direct-to-consumer legal tech may have trouble overcoming some of the problems that are inherent to markets that are attempting to serve individuals with episodic attachment to the civil justice system and limited ability to pay. The result is an important meditation on whether reforms to UPL, Rule 5.4, or something else entirely are necessary to unlock the potential of potent new technologies in order to narrow the justice gap.