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.

This study presents a comprehensive bibliometric and content analysis of research on autism and mathematics learning from 2010 to 2024. A total of 131 peer-reviewed articles were retrieved from the Web of Science (WoS) database using keywords such as autism, mathematics, learning, and intervention. Bibliometric analysis was conducted to quantitatively examine publication trends, leading authors, contributing countries, and co-authorship networks, offering a macroscopic overview of the field’s evolution. Visualisations generated using VOSviewer further illustrated keyword co-occurrence and thematic clustering. Complementing this, content analysis provided a qualitative synthesis of research themes and conceptual progressions across the literature. The findings revealed a clear thematic evolution. Early research (2010–2015) predominantly focused on behavioural interventions, structured instructional approaches, and basic numeracy development. Mid-phase studies (2016–2020) introduced inclusive pedagogies, social-emotional considerations, and differentiated instruction. Recent research (2021–2024) has shifted towards personalised, technology-enhanced instruction, Universal Design for Learning (UDL), and the integration of digital tools in mathematics education. Despite this growth, several gaps remain. Research remains limited in addressing cross-cultural diversity, long-term evaluations of digital interventions, and the adaptation of pedagogies in underrepresented regions. This study emphasises the need for future research to explore culturally responsive frameworks, the sustainability of technology uses, and equity in mathematics education for autistic learners.

This research exclusively aims to determine the type of digital media most often used in the teaching and learning process of history and the motivation to use digital media by teachers in teaching history based on the unified theory of acceptance and use of technology (UTAUT). This study demonstrates that online learning platforms have become a widely used tool among Indonesian history teachers, with the highest adoption of online learning platform use in Junior and Senior High School, reflecting the high access to this platform across types of schools. The key driving factor for technology in history learning is that the technology must be user-friendly and have sufficient support for its use by educators. This research, using the technology acceptance model (TAM), contributes to teachers about motivational and environmental factors on technology adoption in teaching. Accessibility and proper support are the primary drivers for using technology in education and were the most impactful factors for teachers incorporating technology into history learning. Along with this supportive infrastructure, an effort must also be made to provide a conducive environment, such as teachers working together in this direction, and sufficient infrastructure for teachers so that it becomes easier for them to access and utilize technology. These methods can all help teachers gain confidence in their use of technology.

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.

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 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.