Recent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students’ use and intention to use Artificial Intelligence technology. For this purpose, a sample of 197 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. This study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

As artificial intelligence (AI) becomes increasingly prevalent, it has become a topic of interest in education. The use of AI in education poses complex issues, not only in terms of its impact on teaching and learning outcomes but also in terms of the ethical considerations regarding personal data and the individual needs of each student. Our study systematically analyzed empirical research on the use of AI in primary education, specifically for children aged 4–12 years old. We reviewed 35 articles indexed in SCOPUS, filtered them according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzed them, and categorized the findings. The research focused on the studies’ objectives, learning content, learning outcomes, learning activities, and the pedagogy of activities or the AI tools. Our categorization resulted in three main categories of research objectives regarding the creation, implementation, and evaluation of AI tools and five categories for learning content: AI and ML (machine learning) concepts in STEM and STEAM, language learning, mathematics, arts, and various other subjects. The learning activities were split into four categories: apply, engage, interact, use; project-based learning with multiple activities; experience and practice; and students as tutors. The learning outcomes were split into three levels: cognitive, affective, and psychomotor. The pedagogy of AI tools falls into four categories: constructivism, experiential learning, AI-assisted learning, and project-based learning. The implications for teacher professional development are discussed.

Despite the increasing demand for non-formal science learning activities, few studies report on practitioners' perspectives and experiences with designing and implementing such activities worldwide. This paper focuses on their challenges by drawing upon twenty-two interviews with practitioners involved in diverse science learning activities in various non-formal settings in seven European countries. By including diverse activities and settings, this study contributes to the existing knowledge base, addresses the aforementioned gap in the literature, and informs future practices. Our findings suggest that despite the existing and celebrated diversity, practitioners face similar challenges related to (a) the activities' organisation and management, (b) the competencies required to run such activities, and (c) the attitudes held by the parties involved in them. Direct interview quotes exemplify each theme, further pinpointing the interconnection of multiple factors that inform the organisation and implementation of non-formal science learning activities. The findings allowed for a deeper understanding of challenges reported in the literature and shed light on the challenges voiced by the practitioners including the multiple competencies required and the workload. We conclude with a discussion foregrounding the need to build a knowledge base of shared practices in the field of non-formal science learning.

Artificial intelligence (AI) education and literacy are gaining momentum over the past few years; AI systems are permeating our daily lives and mediate our social, cultural, and political interactions. The implications of AI extend beyond the technical aspects and involve ethical, cultural, and social issues such as misinformation and bias. Understanding how an AI system works and critical thinking skills have, therefore, become ever more crucial for children and young people in order to be able to identify the benefits and challenges of AI. The role of the educators is, at this point, critical. This chapter is situated in the context of AI education and literacy and aims to propose a framework for teacher training on AI and ML education. The design of the teacher training courses and initial findings are described. Through an exploratory approach, insights on the attitudes, the requirements, and the recommendations of the teachers emerged.