Large language models (LLMs) are increasingly being utilized to develop tools and services in various domains, including education. However, due to the nature of the training data, these models are susceptible to inherent social or cognitive biases, which can influence their outputs. Furthermore, their handling of critical topics, such as privacy…

Explicable AI in education (XAIED) has been proposed as a way to improve trust and ethical practice in algorithmic education. Based on a critical review of the literature, this paper argues that XAI should be understood as part of a wider socio-technical turn in AI. The socio-technical perspective indicates that explicability is a relative term.…

New ideas and technologies enable new ways of doing as well as new forms of language. The rise of Artificial Intelligence (AI) is no exception. The implications of changing activity and language take on new gravity in certain fields to which AI is applied, such as education (AIEd). Terms like "smart," "intelligence," and…

This scoping review examines the integration of artificial intelligence (AI) tools into scientific education practices in school settings. Following the PRISMA statement guidelines, a literature search was conducted in the Web of Science and Scopus databases, identifying 2892 articles published between 2020 and 2024. After applying the eligibility…

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to…