The association of Artificial Intelligence (AI) and education has attracted researchers’ attention, as it is very beneficial for learners affecting the whole teaching procedure. ChatGPT, an AI language model developed by OpenAI and based on the Generative Pre-trained Transformers (GPT)-3.5 architecture, though newly introduced in November 2022, became very popular even within the first two months. Researchers have already explored and acknowledged the affordances of ChatGPT for language learning and teaching. In this context, the study aimed to examine the effectiveness of ChatGPT as a feedback tool on foreign language writing, especially for socially vulnerable populations, such as refugees/migrants, who face extra language difficulties. The study was conducted in a Junior High School (Gymnasium) classroom in an urban city in south Greece, which consisted of 8 students with a migrant/refugee background aged 15 years old who were attending German as a foreign language learning. The focus of the study was to analyze how the use of ChatGPT could improve foreign language writing in terms of vocabulary and grammar. After the students' first writing attempt, we asked ChatGPT to enhance the vocabulary and grammar of the texts. In contrast, the students received an improved version in terms of language and grammar, and then, they were asked to produce another writing piece of work on a similar topic to their first attempt. The study results indicated an increase in the total number of words, the unique words and the average word number per sentence in the improved version of their work. Overall, the study results were promising, indicating that ChatGPT could act as a language learning tool and support the teaching procedure, especially for students with a refugee/migrant background. However, further research is needed to investigate its use and effectiveness in diverse contexts.

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.

The COVID-19 pandemic has caused an unprecedented crisis. Worldwide, preschool teachers were invited to develop the students’ learning experience in a new digital environment for mathematics. This research investigates preschool teachers’ mathematics practices during remote teaching and the use of digital tools in teaching mathematics after their return to actual classes. Views from sixteen Greek preschool teachers were collected with semi-structured interviews and analyzed using thematic analysis. The results showed that mathematical activities such as Numbers and Operations, Geometry, and Measurement occurred during distance learning in digital preschool classrooms. They made little reference to activities related to Algebra, while they did not refer to Data Analysis and Probability. They also seemed to prefer mathematical activities based on Connections and Representation processes in their digital classrooms. Preschool teachers reported that parents supported this process by their presence, and digital learning communities supported learning activities by providing guidelines and innovative approaches to them in digital times. However, after returning to face-to-face schooling, preschool teachers seemed to use digital tools to a lesser degree. They also mention that in face-to-face schooling, they prefer to utilize the authentic communication frameworks emphasizing problem-solving activities to enhance all mathematical processes. Implications for preschool teacher professional development are discussed.

Attempts to detect socially desirable responding bias have mainly focused on studies that explore sensitive topics. However, researchers concur that the sensitive character of the survey could be affected by the social context within which the research is conducted. Little research has been reported worldwide investigating the potential effects of social desirability on students’ self-reports, considering the social context within which the survey is conducted. In this paper, we investigate the potential effects of social desirability on students’ self-reports in two social contexts within which the survey was conducted. More specifically, with a sample of 111 Greek students, we explored the effects of social desirability on students’ attitudes towards statistics in two cases: when the questionnaire was administered to participating students after attending (a) lectures and (b) both lectures and laboratory classes. Only in the second case were the items’ attitudes toward statistics associated with a score of socially desirable responding; moreover, social desirability accounted for the relationship between attitudes toward statistics and perceived competence in mathematics. Implications and limitations are also discussed.