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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.

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.

In this article we study the content and technological knowledge of teachers who teach or are preparing to teach computer science in a francophone environment. The research is based on the questionnaire elaborated with the reference to the TPACK model. The content knowledge is studied on the base of the five main domains of computer science (algorithms, languages and programming, machines and networks, data representation, and computer science and society) and the technological knowledge on the base of professional and educational programming languages currently used at school. The study presents a multiple correspondence analysis of the 339 responses that were collected and estimated as valid ones. The findings show that the knowledge declared by the teachers vary. The content knowledge can be grouped in 4 categories (from quite poor and badly structured knowledge to more complete knowledge with stronger links). The technological knowledge falls in two categories depending on the knowledge of professional and educative languages declared by teachers.

According to numerous researchers, a clear and direct correlation exists between Computational Thinking (CT) and courses falling under the purview of Science, Technology, Engineering, and Mathematics (STEM), thereby advocating for the integration of CT into the curricula of STEM courses. Nonetheless, it is noteworthy that only a few studies have scrutinized this correlation in-depth. Most such studies connect the correlation tacitly and predominantly concentrate on the empirical assessment of CT within the curriculum of one STEM discipline. This research seeks to evaluate the Computational Thinking abilities of 80 high school students in Greece and discern the extent of correlation with their academic performance in STEM and Greek language courses. A longitudinal survey was executed to accomplish this objective, commencing with administering a test designed to gauge the fundamental components of Computational Thinking. It is worth noting that this test draws its inspiration from internationally recognized computer competitions and serves as a credible assessment tool. Subsequently, an assessment was carried out to ascertain the degree of correlation between students’ Computational Thinking aptitude and their written performance in the subjects encompassed by the STEM category and the Greek language courses. The outcomes of this investigation revealed the presence of a statistically significant correlation between students’ Computational Thinking proficiency and their performance in these academic subjects, further extending to the academic direction of study chosen by the students. Based on the findings of this research, implications and pedagogical recommendations are delineated while concurrently acknowledging the limitations encountered during this study.

The current research study deals with students’ mental representations and cognitive schemata of light refraction. In the study, 213 ninth grade students participated who had taken basic Geometric Optics courses on refraction and Snell’s law. The students were given three tasks in which they were asked to predict and explain the phenomenon of refraction. The results showed that the vast majority of them articulated their responses based on representations that were not compatible with the Geometric Optics model. Quite interestingly, the Multiple Correspondence Analysis led to five distinct cognitive schemata resulting from a fixed combination of representations.

The rapid developments in early 2020 with the emergence of COVID-19 have led humanity into unexpected situations with significant implications at all levels. In trying to cope with the urgent need for distance education, the Greek educational community has incorporated various platforms and digital tools previously unknown to most teachers and students. This study uses a mixed research method to capture (a) the frequency of use of a Personal Learning Environment called ‘e-me’ by the teachers before, during, and after the quarantine, and (b) the reasons why some teachers did not use this environment during these three periods. A total of 902 Greek teachers from primary and secondary education participated in this research. The results showed an almost universal non-use of ‘e-me’ before the pandemic and universal usage during the pandemic. Moreover, about 40% of these teachers used this personal learning environment after the pandemic. The main reason for not using this learning environment in the three periods is teachers’ familiarity with the interface of another platform. Implications for educational policy and teacher development programs are discussed.

Over the past decade, the assessment of the general educational value of computer programming and computational thinking has been constantly increasing and, as a result, they are introduced to increasingly younger ages. In parallel, educational programming environments are significantly progressing, providing a variety of options for different ages. This paper presents an overview of the modern learning programming environments for primary education and proposes a classification system with categories corresponding to the technological and educational dimensions of the area. The paper aims to support teachers in learning design for the interdisciplinary approach of programming and the development of computational thinking.

Although web survey has been a popular method of data collection in the academic community, it presents meagre response rates, which primarily affect the validity of the results as well as the reliability of the outcomes. Surveys worldwide that study the response rate only of teachers have not been found in the relevant literature. In this survey, with a sample of 263 Greek teachers, we investigate possible factors that explain teachers’ intention to participate in web surveys that are conducted by online questionnaires indicating, therefore, the factors that probably influence the response rate of web surveys. Our findings support those factors such as (a) authority, (b) incentives, (c) survey structure/form, (d) ethical issues, (e) reminders and pre-notifications, and (f) survey time received, which seem to explain the teachers’ intention to participate in web surveys with questionnaires. Based on the findings, methodology implications and limitations for researchers are discussed.

The present paper presents a systematic review of the last 30 years that concerns records on Smart Toys and focuses on toys regarding early childhood and primary education children (3–12 years old). This paper aims to analyse and categorise smart toys (50 articles) in terms of their technological and educational affordances. The results show that the toys are designed based on four main technological affordances and their combinations. The educational affordances of smart toys are studied in terms of different use modes and their learning objectives aimed to identify specific objectives in different subjects and objectives based on transversal competencies such as problem solving, spatial thinking, computational thinking, collaboration and symbolic thinking. Finally, with the multiple correspondence analysis, the correlations between smart toys’ individual technological and educational affordances are grouped with the evolution of affordances related to their development date. In conclusion, in recent years, smart toys concern special sciences (programming) and some 21st-century skills (STEM and computational thinking). In contrast, in the first 20 years, the interest focused more on transverse skills, such as collaboration, emotional thinking, symbolic thinking, story-telling and problem solving.

This paper proposes a methodological framework for the study of how the Technological Pedagogical Content Knowledge (TPACK) model is integrated into educational activity design and implementation. The proposed framework was elaborated and applied in the context of a course in which student teachers from an early childhood education undergraduate program integrate TPACK into activity design and implementation using information and communications technologies (ICT). The specific methodological framework was designed to take into account the building blocks of TPACK for each part of the course (teaching, designing, and implementing) and to investigate and recombine these using appropriate methods and tools, such as thematic analysis for qualitative data processing and multidimensional data analysis. Findings show that after applying our initial methodological framework, several elements, for example the particular features and specificities of each subject matter in preschool education, needed to be revisited.