This study aims to evaluate the effectiveness of cooperative learning models in improving critical reading skills. This study uses a meta-analysis study method by analyzing 28 articles extracted from the databases of Scopus, Google Scholar, EBSCO, EmeraldInsight, Science & Direct, SpringerLink, Taylor & Francis, and ProQuest. The meta-analysis allows researchers to combine the results of previous research, providing a more comprehensive picture of how effective a particular approach is in teaching critical reading. The research findings show that cooperative learning models significantly improve essential skills of reading more effectively than traditional ones. This is shown by the effect sizes based on the fixed model, showing the overall standard difference in the mean is 0.784 (95% CI, 0.689 to 0.880) with p-values = 0.00 (<0.05). Using a cooperative learning model, The measure showed positive effect sizes on critical reading learning. Based on these results, it can be concluded that the cooperative learning model effectively improves essential reading skills. However, several factors, such as the quality of the facilitators and the teaching methods, influence the results. The implications of this study show the need for a broader application of cooperative learning models to improve critical reading skills in schools and other educational institutions, with adjustments to the needs and characteristics of students.

The COVID-19 pandemic has significantly changed the global educational landscape, prompting a need to explore emerging literature on home learning, literacy development, and parental involvement. This study aims to contribute to Sustainable Development Goals (SDG) 4: Quality Education, and SDG 10: Reduced Inequalities, by examining these aspects in the context of the pandemic and beyond through a bibliometric analysis. The analysis depicts 416 publications from the Web of Science Database between 2014–2023. The study utilized co-citation and co-word analysis techniques to identify key research clusters and trends related to home learning and literacy development. The analysis revealed that parental involvement can help mitigate learning loss, supporting SDG targets for equitable and inclusive education. Key research clusters identified include the influence of socio-economic status on literacy outcomes, continuity of literacy practices, and the long-term effects of traditional versus digital home learning environments. The findings highlighted a consensus on the importance of a supportive home literacy environment for reading skills and overall academic success. The need for intervention programs targeting low-income groups to ensure equitable access to learning resources, aligning with SDG 10, was also identified through the study. The findings have practical implications for enhancing the home literacy environment, increasing parental involvement, and supporting early literacy interventions, providing valuable insights for education stakeholders, policymakers, and researchers in the post-pandemic era.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.

Integrating generative artificial intelligence (GenAI) in education has gained significant attention, particularly in flexible learning environments (FLE). This study investigates how students’ voluntary adoption of GenAI influences their perceived usefulness (PU), perceived ease of use (PEU), learning engagement (LE), and student-teacher interaction (STI). This study employed a structural equation modeling (SEM) approach, using data from 480 students across multiple academic levels. The findings confirm that voluntary GenAI adoption significantly enhances PU and PEU, reinforcing established technology acceptance models (TAM). However, PU did not directly impact LE at the latent level—an unexpected finding that underscores students’ engagement’s complex and multidimensional nature in AI-enriched settings. Conversely, PEU positively influenced LE, which in turn significantly predicted STI. These findings suggest that usability, rather than perceived utility alone, drives deeper engagement and interaction in autonomous learning contexts. This research advances existing knowledge of GenAI adoption by proposing a structural model that integrates voluntary use, learner engagement, and teacher presence. Future research should incorporate variables such as digital literacy, self-regulation, and trust and apply longitudinal approaches to better understand the evolving role of GenAI inequitable, human-centered education.

The proliferating field of writing education increasingly intersects with technological innovations, particularly generative artificial intelligence (GenAI) resources. Despite extensive research on automated writing evaluation systems, no empirical investigation has been reported so far on GenAI’s potential in cultivating intermedial writing skills within first language contexts. The present study explored the impact of ChatGPT as a writing assistant on university literature students’ intermedial writing proficiency. Employing a quasi-experimental design with a non-equivalent control group, researchers examined 52 undergraduate students’ essay writings over a 12-week intervention. Participants in the treatment group harnessed the conversational agent for iterative essay refinement, while the reference group followed traditional writing processes. Utilizing a comprehensive four-dimensional assessment rubric, researchers analyzed essays in terms of relevance, integration, specificity, and balance of intermedial references. Quantitative analyses revealed significant improvements in the AI-assisted group, particularly in relevance and insight facets. The findings add to the research on technology-empowered writing learning.

Augmented Reality (AR) technology is emerging as a promising tool in education, offering immersive and interactive learning experiences that enhance students’ understanding of abstract scientific concepts. This quasi-experimental study investigated the impact of AR on student learning outcomes in chemistry topics, including acids, bases, pH, oxides, and salts, among lower secondary students in Vietnam. A total of 191 students participated in the study, divided into two groups: the experimental group (n = 94) received AR-integrated lessons. The control group (n = 97) received instruction through traditional methods, including lectures and discussions. Data were collected at three points: prior to the intervention (Test 0, baseline) to establish group equivalence; during the intervention (Test 1) to monitor interim changes; and after the intervention (Test 2) to evaluate overall impact. These were complemented by semi-structured surveys and interviews to assess students’ academic performance, conceptual understanding, and active engagement in the lessons. Mixed-effects ANCOVA revealed a significant Group × Time interaction, F(1,188) = 9.93, p = .002, partial η² = .050, indicating that the experimental group demonstrated significantly greater improvement than the control group. The large between-group effect size (partial η² = .231) confirms substantial practical significance of the AR intervention. Qualitative findings indicated that the use of AR enhanced students’ motivation, engagement, and conceptual understanding by enabling them to visualize three-dimensional molecular structures and conduct simulated experiments in a safe, controlled environment. Despite challenges such as limited technological infrastructure and the need for specialized teacher training, the study demonstrates that AR holds considerable potential for transforming chemistry education in Vietnam. These findings underscore the importance of continued research, targeted professional development, and supportive policies to optimize the integration of AR into diverse educational settings, ultimately improving students’ interest and learning outcomes.

This bibliometric analysis examines global trends and scholarly discussions on learning poverty, with a focus on numeracy and literacy skills among young learners over the past decade (2015-2023). All data is retrieved from the Scopus database, and quantitative methods were employed to evaluate the impact and influence of literature in this area. In conducting the bibliometric analysis, the study utilised Microsoft Excel for descriptive statistics, Harzing’s Publish or Perish for citation metrics, and VOSviewer to visualize networks related to the co-occurrence of keywords and co-word analysis. The analysis findings indicate a substantial increase in research output on learning poverty, particularly after 2020, driven by the educational disruptions caused by the COVID-19 pandemic. The key themes identified in the literature include the expansion of educational inequalities, the impact of the digital divide, and the role of educational technologies in addressing learning losses. Co-occurrence and co-word analyses reveal a strong focus on COVID-19, educational inequality, and the integration of advanced learning technologies in recent research. These findings underscore the critical importance of targeted interventions and equity-focused policies to combat learning poverty. The analysis also emphasizes the importance of ensuring that all children, regardless of their socioeconomic background, acquire essential literacy and numeracy skills. Furthermore, the study emphasises the need for resilient and adaptable educational systems to better prepare for future challenges, particularly in the face of global crises such as pandemics. Essentially, the conclusions discuss the sustained efforts to address educational disparities and promote equitable learning opportunities for all.

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.

Globalization, digitalization, and evolving national regulations have intensified the need for rigorous quality-assurance systems to secure accreditation in Higher Education Institutions (HEIs). This study asks: What theoretical contributions underpin HEI accreditation, and how have research themes evolved? Employing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and Bibliometric Analysis via Biblioshiny and Vantage Point, we examined 1,252 documents indexed in Scopus® (781) and Web of Science™ (471) from 2012 to May 2025. Findings delineate three production phases—Foundation Consolidation (2012–2017), Expansion and Diversification (2017–2020), and Sustained Transformation and Innovation (2020–2025)—and three thematic perspectives: (a) Teaching and Learning Quality, (b) Technology and Sustainability as Quality Catalysts, and (c) Governance, Management, and Accountability. Multiple Correspondence Analysis (MCA) identified three Motor Theme clusters—[1] Sustainable Development and Institutional Change, [2] Technological Pedagogy and Student Experience, and [3] Governance and Regulation—led by Spain, the United States, Chile, Colombia, the UK, Australia, and India. Conclusions underscore accreditation’s dual role as a strategic lever for institutional improvement and a competitive mechanism, with emerging focus on competency, e-learning, employability, machine learning, and sustainability. Future research should explore cross-border accreditation dynamics; the impact of AACSB and NAAC standards on business-school curriculum design and program quality; accreditation’s pedagogical effects; and leadership practices for effective implementation.

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.