A detailed analysis of how AI can be used in the classroom, including methods for measuring knowledge and abilities, student-adapted teaching with AI, general principles for technology use, and considerations of reading, writing, digital distractions, effective methods, and desirable difficulties. Research from the global north is included in the chapter.

A comprehensive chapter exploring AI's role in education, with insights into democratic, ethical, and social reflections, different views on technology in schools, and an analysis model for AI as a resource in school, including ethical reflections and management. The chapter gives room especially for global educational guidelines and presents guidelines and standards for how AI should be implemented and used responsibly in educational environments.

The concluding chapter, with a humanistic perspective on learning and technology, emphasizes the unique human aspects that AI cannot replicate, such as factuality, creativity, and humanity.

An investigation of how AI can be applied within specific subjects and disciplines, including TPACK, subject didactic opportunities and problems, and a focus on search criticism and source awareness.

A panoramic view of the digital era and how AI affects today's teaching, introducing the opportunities and simultaneous challenges that technology brings.

An introduction to AI, including an overview of essential technologies such as machine learning and deep learning, and a discussion on generative AI and its potential limitations. The chapter includes an exploration of AI's history, including its relationship to cybernetics, its role as a codebreaker, periods of optimism and “AI winters,” and today's global development with generative AI. Chapter 1 also include an analysis of AI's role in the international and national context, focusing on potential conflicts of goals and threats that can arise from technology.

The chapter highlights the importance of AI literacy. Opportunities and challenges that AI creates in the educational context, such as strategies for technology use, and what AI tools like ChatGPT can enable and hinder in the learning process.

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.

In the technological wave of the twenty-first century, artificial intelligence (AI), as a transformative technology, is rapidly reshaping our society, economy, and daily life. Since the concept of AI was first proposed, this field has experienced many technological innovations and application expansions. Artificial intelligence has experienced three booms in the past half century and has developed rapidly. In the 1960s, marked by the Turing test, the application of knowledge reasoning systems and other technologies set off the first boom. Computer scientists at that time began to explore how to let computers simulate human intelligence. Early AI research focused on rule systems and logical reasoning. The rise of expert systems and artificial neural networks brought a second wave of enthusiasm (McDermott, 1982). The third boom is marked by deep learning and big data, especially the widespread application of artificial intelligence-generated content represented by ChatGPT. During this period, AI technology shifted from traditional rule systems to methods that relied on algorithms to learn patterns from data. The rise of deep learning enabled AI to achieve significant breakthroughs in areas such as image recognition and natural language processing.