Open schooling is a recent educational approach that has been recently introduced in European policies, so the literature on its transfer to the education system is still limited. The aim of this study is to configure an overview of how open schooling has been implemented in science education in terms of its definition, the topics addressed, the pedagogical aspects considered, and the benefits obtained for the teaching-learning processes. A systematic literature review was carried out using the PRISMA 2020 methodology, in which 27 documents published between 2015 and 2024 were analysed. Open schooling is defined mainly as an approach in which students, teachers, and other stakeholders, especially students’ families, collaborate to provide solutions to real-life issues in search of the community’s well-being. The open schooling experiences usually addressed environmental and health issues through scientific practices and contextualization mainly, developing activities of data collection, synthesis and analysis, dissemination, and information and communication technologies with the students. Companies and local businesses are the preferred stakeholders involved, followed by experts and researchers. Questionnaires are the preferred instruments for data collection, and the documents analysed report benefits for students’ learning and motivation and also for teachers and schools. 

Developing students' competency in constructing scientific explanations is a critical aspect of science learning. However, limited research has been conducted to explore the role of Generative Artificial Intelligence (Gen AI) in fostering this competency. Moreover, the factors influencing this competency development in the Gen AI-assisted learning environment remain underexamined. This study aimed to compare students' competency in constructing scientific explanations before and after participating in phenomenon-based learning with Microsoft Copilot and to investigate the factors influencing the development of this competency. A pretest-posttest quasi-experimental design was employed with 23 eighth-grade students from an all-girls school in Thailand. The research instruments included lesson plans for phenomenon-based learning with Microsoft Copilot, a competency test for constructing scientific explanations, and a mixed-format questionnaire. The results from the Wilcoxon Signed-Ranks Test revealed a statistically significant improvement in students' competency in constructing scientific explanations after the learning intervention (Z = 4.213, p < .001). Thematic analysis identified four key factors contributing to this development: (a) the role of Microsoft Copilot in enhancing deep understanding, (b) connecting theories to real-world phenomena through learning media, (c) collaborative learning activities, and (d) enjoyable learning experiences and student engagement. These findings suggest that the integration of Gen AI technology with phenomenon-based learning can effectively enhance students’ competency in constructing scientific explanations and provide valuable insights for the development of technology-enhanced science education. 

The current study sought to evaluate the impact of programs used to enhance the self-regulation and social interaction for children with Autism Spectrum Disorders (ASD), as perceived by their families. The descriptive approach was used to collect and analyze data and derive conclusions after developing the study instruments. The study sample consisted of 150 families of children with ASD enrolled in special education centers in Amman, Jordan. The study participants were purposefully selected to respond to the two provided measurement scales. To measure the impact of the intervention programs, the researchers developed the Self-Regulation Behavior Scale and the Social Interaction Scale, ensuring the validity and reliability of both scales. The results of the study indicated that from the families’ perspective, programs for children with ASD had a moderate impact on enhancing self-regulation and a high impact on social interaction. Additionally, the findings of the study revealed statistically significant differences in the degree of improvement in self-regulation and social interaction behaviors related to the child’s gender and the severity of their disorder. However, no statistically significant differences were found related to the child’s age and gender in their level of improvement in self-regulation and social interaction behaviors.

This preliminary study examines how three generative AI tools, ChatGPT-4, Google Gemini, and Microsoft Copilot, support B+ level English as a Foreign Language (EFL) students in opinion essay writing. Conducted at a preparatory school in Türkiye, the study explored student use of the tools for brainstorming, outlining, and feedback across three essay tasks. A mixed methods design combined rubric-based evaluations, surveys, and reflections. Quantitative results showed no significant differences between tools for most criteria, indicating comparable performance in idea generation, essay structuring, and feedback. The only significant effect was in the feedback stage, where ChatGPT-4 scored higher than both Gemini and Copilot for actionability. In the brainstorming stage, a difference in argument relevance was observed across tools, but this was not statistically significant after post-hoc analysis. Qualitative findings revealed task-specific preferences: Gemini was favored for clarity and variety in brainstorming and outlining, ChatGPT-4 for detailed, clear, and actionable feedback, and Copilot for certain organizational strengths. While the tools performed similarly overall, perceptions varied by task and tool, highlighting the value of allowing flexible tool choice in EFL writing instruction.

This article aims to establish a research map of the flipped classroom (FC) model in general education for the period 2014-2024, exploring publication trends, influential authors, organizations, countries, and prominent research topics, while also identifying academic gaps in this field. The study focuses on three main aspects: (1) publication trends and influential authors, organizations, and countries; (2) key research topics and academic gaps; (3) international collaboration networks in FC research. The research employs a bibliometric analysis method, utilizing the Scopus database and the VOSviewer visualization tool, to synthesize information and identify research trends. The results indicate that research on FC in K-12 education increased sharply from 2019, reflecting the impact of digital transformation in education during and after Covid-19. The United States, Hong Kong, and Taiwan are the leading research centers. Authors such as Bergmann, Bishop, and Hew have been highly influential. Prominent research trends include self-regulated learning, learner satisfaction, gamification, and the application of artificial intelligence. The international collaboration network in this field is growing, with strong participation from institutions from Southeast Asia, including Vietnam. The study recommends expanding the scope of analysis beyond Scopus and using qualitative methods and systematic reviews to further evaluate the FC model. The research will provide policymakers, teachers, and researchers with useful evidence for improving programs, enhancing professional development, and promoting digital transformation in general education.

The rapid integration of artificial intelligence (AI) technologies into the field of higher education is causing widespread public discourse. However, existing research is fragmented and lacks systematic synthesis, which limits understanding of how college and university students adopt artificial intelligence technologies. To address this gap, we conducted a systematic review following the guidelines of the PRISMA statement, including studies from ScienceDirect, Web of Science, Scopus, PsycARTICLES, SOC INDEX, and Embase databases. A total of 5594 articles were identified in the database search; 112 articles were included in the review. The criteria for inclusion in the review were: (i) publication date; (ii) language; (iii) participants; (iv) object of research. The results of the study showed: (a) The Technology Acceptance Model and the Unified Theory of Technology Acceptance and Use are most often used to explain the AI acceptance; (b) quantitative research methods prevail; (c) AI is mainly used by students to search and process information; (d) technological factors are the most significant factors of AI acceptance; (e) gender, specialty, and country of residence influence the AI acceptance. Finally, several problems and opportunities for future research are highlighted, including problems of psychological well-being, students’ personal and academic development, and the importance of financial, educational, and social support for students in the context of widespread artificial intelligence.

This study examines the transformation of higher education through digital solutions, with a specific focus on developing a user-centered digital student handbook prototype for Surin Vocational College in Thailand, with potential scalability nationwide. Utilizing a mixed-methods approach, the research integrates qualitative and quantitative data to design a digital tool that enhances accessibility, usability, and personalization for students. The prototype features key components, including mobile accessibility, real-time updates, interactive notifications, and integration with academic tools, designed to enhance student engagement, career readiness, and academic performance. Data collection involved students, faculty, and experts to ensure a comprehensive understanding of user needs and preferences. The findings indicate that the digital format offers significant advantages over traditional paper-based handbooks, particularly in terms of accessibility, real-time content updates, personalized experiences, and environmental sustainability. However, variability in user feedback suggests areas for further refinement,  emphasizing that there must be continuous improvement. This research offers interesting perspectives on the role of digital solutions in higher education, contributing to the ongoing evolution of learning tools that support academic success, student engagement, and institutional sustainability.

Text readability assessment stands as a fundamental component of foreign language education because it directly determines students' ability to understand their course materials. The ability of current tools, including ChatGPT, to precisely measure text readability remains uncertain. Readability describes the ease with which readers can understand written material, while vocabulary complexity and sentence structure, along with syllable numbers and sentence length, determine its level. The traditional readability formulas rely on data from native speakers yet fail to address the specific requirements of language learners. The absence of appropriate readability assessment methods for foreign language instruction demonstrates the need for specialized approaches in this field. This research investigates the potential use of ChatGPT to evaluate text readability for foreign language students. The examination included selected textbooks through text analysis with ChatGPT to determine their readability level. The obtained results were evaluated against traditional readability assessment approaches and established formulas. The research aims to establish whether ChatGPT provides an effective method to evaluate educational texts for foreign language instruction. The research evaluates ChatGPT's capabilities beyond technical aspects. The study examines how this technology may influence students' learning experiences and outcomes. The text clarity evaluation capabilities of ChatGPT might lead to innovative approaches for developing educational tools. The implementation of this approach would generate lasting benefits for educational practices in schools. For example, ChatGPT’s readability classifications correlated strongly with Flesch-Kincaid scores (r = .75, p < .01), and its mean readability rating (M = 2.17, SD = 1.00) confirmed its sensitivity to text complexity.

The global context of digital transformation has compelled education systems to rapidly adapt and innovate. In Vietnam, establishing an effective digital education ecosystem is key to achieving sustainable development goals, particularly Sustainable Development Goal 4 (SDG 4) on Quality Education. However, there is a recognized lack of quantitative research on the key drivers shaping this ecosystem in the Vietnamese context. This study aims to explore and identify these main drivers. Drawing on survey data from 699 Vietnamese students and applying Exploratory Factor Analysis, we identified four prominent factors: Technological Infrastructure, Stakeholder Collaboration, Policy Orientation, and Financial Accessibility. The findings suggest these factors play a foundational role in fostering the digital transformation of higher education. These results provide preliminary empirical evidence on the underlying structures that shape Vietnam's digital education environment. This research can serve as a basis for future in-depth analyses and offers initial insights for policymakers to focus on the identified crucial factors.

This study examined the capability and scientific consistency of ChatGPT and Gemini using a two-tier test. The capability and scientific consistency of ChatGPT and Gemini were compared with those of students. The study used 60 new chats with ChatGPT and Gemini, 120 students in 8th and 9th grade, 129 students in 11th and 12th grade, 260 undergraduate elementary teacher education students (across four cohorts), and 51 students from the professional education program for elementary school teachers. Data were collected through online testing for student participants and prompting processes for ChatGPT and Gemini using a 25-item two-tier test. Quantitative data analysis was employed to compare capability and consistency scores across all subjects. Qualitative-descriptive analysis was also conducted to examine the aspects of capability and scientific consistency behavior of ChatGPT and Gemini. Data analysis showed that the capability and scientific consistency of ChatGPT-4 and Gemini in responding to the test type were categorized as low and below the entry threshold, and higher than those of the students. Both generative AI systems performed better at providing theoretical justifications or reasoning than at answering factual questions about static fluids. ChatGPT outperformed Gemini only in the combined scores for Tier-1 and Tier-2 items. Both generative AI systems demonstrated conceptual insights and understanding of static fluids, though these insights sometimes contained biases and contradictions. As AI systems built on large language models, ChatGPT and Gemini heavily rely on availability and require a more extensive and diverse database containing static fluid cases.

Despite the increasing integration of artificial intelligence (AI) into education, a comprehensive understanding of how scholarly discourse has evolved over time remains limited. Most existing studies focus on technical implementation or short-term pedagogical outcomes, often lacking longitudinal scope or thematic synthesis. This study addresses that gap by offering a 25-year bibliometric analysis of AI-related educational research, mapping its conceptual development, publication trends, and emerging priorities from 2000 to 2025. Using data sourced from Lens.org and processed through Biblioshiny (R-Studio) and VOSviewer, 350 peer-reviewed articles were analyzed based on their thematic focus, keyword evolution, authorship patterns, and citation networks. The novelty of this study lies in its integration of bibliometric mapping with temporal thematic evolution, enabling a detailed understanding of how foundational concepts, such as lifelong learning, AI literacy, and ethics, have transitioned from peripheral concerns to central research themes. Findings show a sharp increase in publication volume after 2018, reflecting the impact of cloud-based AI platforms and the pandemic-induced pivot to remote education. While “artificial intelligence” and “education” remain dominant keywords, emerging themes such as “student well-being,” “digital competency,” and “personalized learning” suggest a shift toward more human-centered and ethically conscious AI applications. The study concludes by identifying persistent gaps related to pedagogical effectiveness, global equity, and critical digital literacy, offering a roadmap for future interdisciplinary research and inclusive educational policy.

The emergence of artificial intelligence (AI) has transformed higher education, creating both opportunities and challenges in cultivating students’ critical thinking skills. This study integrates quantitative bibliometric analysis and qualitative systematic literature review (SLR) to map global research trends and identify how critical thinking is conceptualized, constructed, and developed in the AI era. Scopus served as the primary data source, limited to publications from 2022 to 2024, retrieved on February 8, 2025. Bibliometric analysis using Biblioshiny R and VOSviewer followed five stages—design, data collection, analysis, visualization, and interpretation—while the SLR employed a deductive thematic approach consistent with PRISMA guidelines. A total of 322 documents were analyzed bibliometrically, and 34 were included in the qualitative synthesis. Results show that Education Sciences and Cogent Education are the most productive journals, whereas Education and Information Technologies have the highest citation impact. Several influential documents and authors have shaped global discussions on AI adoption in higher education and its relationship to critical thinking. Thematic mapping identified five major research clusters: pedagogical integration, ethical and evaluative practices, technical and application-oriented AI models, institutional accountability, and socio-technical systems thinking. Conceptually, critical thinking is understood as a reflective, evaluative, and metacognitive reasoning process grounded in intellectual autonomy and ethical judgment. Across the reviewed literature, strategies for fostering critical thinking converge into three integrated approaches: ethical curriculum integration, pedagogical and assessment redesign, and reflective human–AI collaboration. Collectively, these strategies ensure that AI strengthens rather than replaces human reasoning in higher education.