This systematic review investigates language development problems encountered by primary school students during the literacy process, based on studies published between 2016 and 2025. Using PRISMA guidelines, 36 empirical studies were selected from major academic databases, including Taylor Francis, Springer, Elsevier, and ERIC. The analysis focuses on challenges such as phonological awareness deficits, vocabulary limitations, and difficulties in written expression. Findings indicate that these problems are shaped by cognitive, linguistic, socio-cultural, and technological factors. Phonological difficulties were the most frequently reported and were associated with poor reading fluency. Vocabulary problems were prevalent among students from language minority or low socio-economic backgrounds. Issues in written expression were linked to limited exposure to rich linguistic environments and weak instructional strategies. External factors like the COVID-19 pandemic and lack of access to online support further exacerbated learning gaps. Most reviewed studies employed quantitative designs, with limited qualitative insights. The review emphasizes the need for early interventions, phonological training, and vocabulary-enriched instruction tailored to individual needs. These findings inform educators and policymakers aiming to reduce literacy-related inequalities in early primary education.

Recent discussion of generative AI in education has focused on student use and AI literacy in the curriculum. Less attention has been paid to a different shift: teachers can now use large language models to build simple classroom tools by specifying functions in natural language. This matters in early childhood and primary education, where teachers often need localised, developmentally appropriate resources that commercial software does not provide. Current teacher AI literacy and digital competence frameworks address selection and responsible use of AI systems, but they say much less about teachers who use AI to build tools for their own classrooms. Teachers' use of generative AI for small-scale classroom tool creation is professional competence, in a limited sense. The claim is that some forms of low-level tool creation are now accessible to non-programmers, and that this shifts the professional demand from code writing to pedagogical specification and iterative testing. The Teacher as Micro-Developer (TMD) framework structures this shift, organised around three layers: Pedagogical Intent, Prompt Architecture, and Critical Curation. Keywords: vibe coding, generative AI, early childhood education, primary education, teacher professional development, natural language programming, prompt engineering

Artificial intelligence (AI) is reshaping how learning environments are designed and experienced, offering new possibilities for personalization, creativity, and immersive engagement. This systematic review synthesizes 43 empirical studies (Scopus, Web of Science) to examine the training needs and practices of primary and secondary education teachers for effective AI integration and overall professional development (PD). Following PRISMA guidelines, the review gathers teachers’ needs and practices related to AI integration, identifying key themes including training practices, teachers’ perceptions and attitudes, ongoing PD programs, multi-level support, AI literacy, and ethical and responsible use. The findings show that technical training alone is not sufficient, and that successful integration of AI requires a combination of pedagogical knowledge, positive attitudes, organizational support, and continuous training. Based on empirical data, a four-level, process-oriented PD framework is proposed, which bridges research with educational practice and offers practical guidance for the design of AI training interventions. Limitations and future research are discussed.

When we first conceptualized this Special Issue, the educational community was arguably in a state of reaction—reacting to the sudden accessibility of generative AI, the maturing of immersive hardware, and the urgent post-pandemic need for digital resilience [...]

Computational thinking (CT) and coding are increasingly recognized as foundational literacies that shape how children think, communicate, and participate in digital society. Yet in early childhood education, they are still too often taught as isolated technical skills rather than as expressive tools for creativity, ethical reflection, and civic engagement. This paper presents a university seminar for preservice kindergarten teachers built around the Coding as Another Language (CAL) framework and the ScratchJr environment. Grounded in constructionism, socio-emotional learning (SEL), and the Positive Technological Development (PTD) framework, CAL reframes coding as a language through which children can tell stories, explore values such as empathy and justice, and critically engage with technology. The seminar integrates unplugged and plugged activities, design-based learning principles, reflective practice, and culturally contextualized materials to help future educators design and facilitate values-oriented coding experiences. Analysis of student reflections, project work, and instructor observations shows how preservice teachers’ conceptions of coding evolve from procedural skills to a broader humanistic literacy. Their reflections reveal a marked shift in how they understand coding’s expressive, ethical, and pedagogical potential before and after the course. Situated within current research on CT, curriculum design, and multidimensional assessment, the paper argues for sustainable teacher preparation models that treat coding as both a cognitive tool and a medium for fostering human values, critical thinking, and inclusive participation from the earliest years.

Despite the growing adoption of generative artificial intelligence (GenAI) tools in higher education, limited research has examined how future educators perceive and use these technologies in their academic practices. This study investigates the practices, perceived benefits, and challenges associated with the use of GenAI tools—such as ChatGPT—among undergraduate students enrolled in programs that confer teaching qualifications. Using a mixed-methods design, data were collected from 314 students from the Early Childhood Education, Philosophy, and Philology departments. The findings indicate that the majority of students use GenAI tools primarily for academic purposes, most commonly for information searching, data analysis, study advice, and exam preparation. Students reported several perceived benefits, including rapid access to information, time efficiency, improved comprehension of complex concepts, enhanced study organization, and support with assignments and research-related tasks such as summarizing or translating academic texts. At the same time, participants expressed notable concerns, particularly regarding over-reliance on AI, reduced personal effort, risks to academic integrity, diminished critical thinking, and weakened research skills. Additional challenges included misinformation, reduced creativity, improper use of AI-generated content, skill underdevelopment, and potential technological dependence. The study concludes that teacher education programs should systematically integrate AI literacy and responsible-use training to prepare future educators to address the pedagogical and ethical implications of GenAI in educational settings.

This study adapted the Meta-AI Literacy Scale (MAILS) for Greek early childhood educators and examined whether its nine-factor structure could be replicated in a new linguistic, professional, and educational context. A total of 475 participants took part in two samples: an exploratory factor analysis with one sample (n = 133) and a confirmatory factor analysis with an independent sample (n = 342). The analyses supported the original nine-factor structure. Internal consistency was satisfactory across subscales (McDonald’s ω = 0.84–0.96), and evidence for convergent and discriminant validity was acceptable. Positive attitudes toward AI were associated with most MAILS dimensions, whereas negative attitudes showed only weak and mostly non-significant associations. These findings suggest that apprehension toward AI may not be reducible to perceived competence alone. The study provides initial evidence that the nine-factor structure of the MAILS can be replicated with Greek early childhood educators. Because measurement invariance was not tested, these findings do not establish metric or scalar equivalence with the original version, and scores from the two versions cannot yet be compared directly. Further work is needed on invariance testing, predictive validity, and behavioral indicators of AI literacy.

This narrative review examines the integration of computational thinking and coding within early science, technology, engineering, and mathematics curricula in the context of the artificial intelligence (AI) era. While the importance of foundational skills—such as decomposition, pattern recognition, and algorithmic thinking—is well-established, effective pedagogical strategies for young learners remain fragmented. This article synthesizes current literature to outline effective instructional strategies, such as hands-on inquiry and game-based learning. It further discusses systemic challenges, including resource equity and teacher training, proposing a holistic framework for inclusive access. The review concludes that early exposure to these competencies is critical not only for future technical proficiency but also for nurturing the creative problem-solving skills required in an AI-driven workforce.

Gamification has become an innovative method used to enrich instructional designs, moving far beyond its initial motivational purposes. Given its promising impact on the contemporary educational context, gamification has attracted numerous studies seeking ways to enhance the quality of instruction and learning outcomes across diverse fields. Despite the growing volume of gamification research, longitudinal analyses tracing how the conceptual priorities of the field have shifted across historically distinct periods remain scarce. This study addresses that gap by mapping the thematic evolution of gamification research through science mapping analysis of articles published in peer-reviewed journals across three time periods: 2006–2019, 2020–2022, and 2023–2025. The findings indicate that early research focused primarily on gamification’s impact on student motivation and engagement, while subsequent research focused on course evaluation and digital learning environments. Recent research has focused more on blended and domain-specific methods, testing the adaptability of different applications to specific fields. These findings suggest that gamification research has evolved from more superficial practices, such as encouraging course participation or boosting motivation, to its integration into personalized learning environments, and in recent years, towards ethical data use and the development of context-specific applications.

This review explores artificial intelligence (AI) literacy in early childhood education (ECE) and K-12 education by systematically analyzing 162 publications from January 2020 to March 2026. The findings revealed a rapid increase in research interest, focusing on integrating AI into formal curricula, developing age-appropriate pedagogies, and supporting education stakeholders. Nine primary themes and eight interconnected topics related to AI literacy were identified, ranging from foundational concepts and curriculum design to practical classroom implementation, including pedagogical approaches and teaching strategies, ECE considerations, and teachers' training and competencies. An emphasis on the impact of AI literacy on students' learning, cognitive development, and awareness of ethical and social implications and on shaping human-AI interaction was revealed. The study highlights AI literacy as a multidimensional competency, emphasizes the need to support education stakeholders, and points out its importance across preschool education, primary education, and secondary education.

This systematic review aims to present trends in the use of Chatbots based on Natural Language Processing (NLP) in primary education. Their use in various subjects, including Language, Mathematics, and Social Studies, is analyzed, high-lighting positive effects on students' academic performance, self-regulation, and autonomy. At the same time, challenges arise regarding the accuracy of infor-mation, its acceptance by teachers, learners, and parents, and the ethical implica-tions of its use in the educational context. The findings indicate that Chatbots can function supportively in the educational process, without replacing the human factor.

This research aimed to investigate the explanatory and predictive relationships between digital literacy components and digital data security. The study was carried out with 322 students enrolled in the education faculties of eight randomly selected universities. In the research, the attitudes, cognitive and social variables of digital literacy were exogenous. A mediation model has been established in which the technical variable is the mediator, and the digital data security awareness is endogenous. According to the analysis results of the mediation model, the technical variable fully mediates between the attitude, cognitive and social variables, and the awareness of digital data security. These results show that developing digital literacy skills and if these skills are supported with technical knowledge can contribute to developing digital data security awareness. As a result, digital literacy skills will only have the expected effect on creating digital data security awareness with the technical information tool variable. According to our mediation model, the technical competence to be developed with the support of digital literacy skills and the creation of digital data security awareness can guide researchers and instructors working in the field.

Mobile learning (m-learning) is transforming higher education, reshaping traditional teaching methods with unprecedented flexibility and accessibility. This study delves into the dynamic realm of m-learning research, exploring its evolution, challenges, and future prospects through a comprehensive bibliometric analysis guided by PRISMA guidelines. By examining publications, citations, keywords, and global trends from 2007 to 2023, our research highlights the United States, Australia, and the United Kingdom as leaders in mobile learning, supported by their significant citation rates. Key themes such as mobile devices, higher education, and blended learning emerge as pivotal in shaping this educational frontier. Innovative methodologies such as augmented reality (AR), gamification, and integrating social media content and learning management systems (LMS) underscore the diverse landscape of m-learning. Beyond enhancing educational practices and student engagement, m-learning promotes interactivity, lifelong learning, and student motivation. However, challenges persist, including the need to strengthen internet infrastructure, improve content quality, foster digital literacy, and ensure privacy in digitally immersive learning environments. This study highlights the transformative power of m-learning and calls for continuous innovation to fully harness its potential in advancing global education in the digital era.

Principal leadership behaviors affect the improvement of school outcomes significantly, not only by providing psychological and professional support for teachers but also by facilitating a positive learning environment at school. This has cultivated both policy and research interest in understanding how principals' leadership behaviors can leverage student outcomes both directly and indirectly through teachers. In this quest, three leadership models have become prominent due to their close relation to classroom instruction and learning: instructional, transformational, and distributed leadership. The current study aims to reveal the relationships between these leadership behaviors of principals and student outcomes as well as assess the mediating effect of teacher self-efficacy on this relationship. Utilizing meta-analytic structural equation modelling (MASEM) methodology, the study analyzes data from prior studies to offer a more comprehensive and holistic analysis of the complex relationships between the variables. The analysis of data from 90 studies showed that all three leadership behaviors affected student outcomes both directly and indirectly through teacher self-efficacy. They also had a moderate direct influence on teacher self-efficacy, while teacher self-efficacy had a moderate effect on student outcomes. These results reiterate the significance of principals' leadership for learning to facilitate student outcomes and suggest that the integrated practice of leadership in accordance with contextual requirements could leverage the effectiveness and improvement of schools.

The integration of Artificial Intelligence (AI) technologies in students’ lives necessitates the systematic incorporation of foundational AI literacy into educational curricula. Students are challenged to develop conceptual understanding of computational frameworks such as Machine Learning (ML) algorithms and Decision Trees (DTs). In this context, unplugged (i.e., computer-free) pedagogical approaches have emerged as complementary to traditional coding-based instruction in AI education. This study examines the pedagogical effectiveness of an instructional intervention employing unplugged activities to facilitate conceptual understanding of DT algorithms among 47 9th-grade students within a Computer Science (CS) curriculum in Greece. The study employed a quasi-experimental design, utilizing the Structure of Observed Learning Outcomes (SOLO) taxonomy as the theoretical framework for assessing cognitive development and conceptual mastery of DT principles. Quantitative analysis of pre- and post-intervention assessments demonstrated statistically significant improvements in student performance across all evaluated SOLO taxonomy levels. The findings provide empirical support for the hypothesis that unplugged pedagogical interventions constitute an effective and efficient approach for introducing AI concepts to secondary education students. Based on these outcomes, the authors recommend the systematic implementation of developmentally appropriate unplugged instructional interventions for DTs and broader AI concepts across all educational levels, to optimize AI literacy acquisition.

There is an ongoing need to evaluate whether commonly used educational software effectively supports inquiry-based learning and computational thinking skills development, which are key objectives in secondary STEM curricula. This research establishes criteria for characterising digital technologies, such as modelling and simulation software, virtual laboratories, and microcosms, to ensure their suitability in supporting students’ computational thinking through inquiry-based activities in STEM courses. The main criteria focus on six key areas: (a) production of meaning, (b) support in problem formulation, (c) ability to manage processes easily, (d) support in expressing solutions, (e) support in executing and evaluating solutions, and (f) ability to articulate and reflect on processes and solutions. Using this evaluation framework, two widely used software tools, Tracker 6.1.3 and GeoGebra 5, commonly employed in high school physics and mathematics, were assessed. The trial evaluation results are discussed, with recommendations for improving the software to support these educational objectives.

Many programming environments are promoted as tools for teaching Computational Thinking (CT) and coding skills to young children, particularly those in preschool. However, their effectiveness is still in the early stages. In a study conducted over three weeks, a teaching intervention was used to explore how ScratchJr can improve CT and basic coding skills in a group of preschoolers (N = 34, aged 4–6). The mean pretest performance of the control group (M = 7.07, SD = 2.58) was significantly higher than that of the test group (M = 5.35, SD = 1.58), t(22.64) = 2.23, p = .036. Nevertheless, careful examination of the data indicates a statistically significant improvement in preschoolers exposed to the ScratchJr educational intervention. The findings provide evidence for the efficacy of the programming environment in fostering CT and coding abilities among young children in preschool. Both teaching approaches equivalently foster the fundamental coding concepts of control structures and modularity. The experimental cohort performs statistically significantly better than the control group in understanding the powerful ideas of algorithms, hardware/, and representation. In contrast, the control group outperforms the test group in understanding the debugging concept. The results strongly support the effectiveness of the programming environment ScratchJr, highlighting its ability to develop CT and coding skills in preschoolers.

This chapter explores the influence of educational robotics and artificial intelligence in maker education. According to the findings, integrating artificial intelligence and educational robotics in maker education can enrich the educational process, create collaborative learning environments, and provide benefits for both teachers and students. In such cases, students can also benefit from personalized learning opportunities and acquire a deeper understanding through interactive and experiential learning activities. By involving students in such maker activities, their learning outcomes can be increased, their learning engagement, focus, and motivation can be enhanced, and their critical thinking and creativity can be improved. Additionally, students can improve their soft skills, digital skills, and artificial intelligence literacy.