This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.

eTwinning is a community of European schools that promotes networking and transnational collaboration projects. Therefore, as part of the Initial Teacher Education (ITE) initiative, we decided to explore how the research projects on scientific topics familiarize pre-service primary school teachers with eTwinning. 251 pre-service primary school teachers from Spain, Iceland, and Slovenia designed and carried out joint research projects on scientific topics. The aim was to gain insight into their self-assessment of their knowledge of the platform, the limitations of working with it, and their responses. An electronic questionnaire was used as the main instrument for data collection. It turned out that the pre-service teachers enjoyed this international collaboration, but also found it challenging. By the end of the projects, they were familiar with the eTwinning platform, although the least engaged prospective teachers recognized that they needed strong support and considered the platform a non-intuitive environment. Overall, they were willing to use the platform with pupils in the future, which is in line with the aim of the ITE initiative.

This comprehensive systematic review delves into the increasing prevalence of integrating chatbots into language education. The general objective is to assess the current landscape of knowledge regarding chatbot utilisation and its influence on three crucial elements: students' skills, attitudes, and emotions. Additionally, the review seeks to scrutinise the advantages linked to incorporating chatbots in foreign language teaching, exploring their potential benefits while considering limitations and potential negative impacts on specific skills or user experiences. Consequently, this research offers valuable insights into the application of chatbots in foreign language education, shedding light on their potential advantages and areas that warrant further exploration and enhancement. The integration of chatbots in language learning, despite certain limitations, generally yields positive outcomes and enhances educational results in students' skills. Its characteristics can also influence a language learner's attitude, impacting factors such as motivation, interest, autonomy in learning, and engagement or even their sense of fun. Additionally, chatbots prove to be helpful in creating emotionally positive learning environments and can contribute to boosting students' self-esteem and self-confidence.

The world is looking forward to advancements in artificial intelligence (AI) technology, with significant research underway regarding the application of AI in education. This study analyzed publications on the potential of AI in secondary schools, focusing on its bibliometric aspects. Data from the Scopus database revealed 1,764 publications from 2019 to 2024. The analysis showed a steady annual growth in publications in this area. China and the USA were the leaders in the number of publications. Xiaoyue Wang was the most prolific researcher, having authored 71 AI-related articles. Yueying Li, Xiaoxu Chen, Yanzhu Zhang, and Yi Liu contributed to the field with 56, 55, 53, and 51 articles, respectively. The themes that emerged from 2019 to 2022 are related to media, application, study, institutions, artificial, digital, learning, factors, development, technologies, medical, automated, perception, support, and sustainability. From 2023 to 2024, the topics discussed in AI are related to students, education, perception, algorithms, digital, prediction, networks, challenges, writing, teachers, AI-powered, curriculum, century, integration, technology, and framework. The difference in research in 2019-2022 and 2023-2024 is focusing the theme's focus from the general to the specific. The co-occurrence analysis revealed that prominent keywords appeared in 3 clusters. Cluster 1 is the most popular in recent times. It deals with the application, assessment, and management of AI. Cluster 2 relates to AI relationships and models, while Cluster 3 relates to AI data sources.

The concept of technological pedagogical content knowledge (TPACK) is presented as a framework that guides how to effectively integrate technologies in the educational environment. Through this model, we investigate the ethical implications related to the use of digital tools in teaching, and we outline the necessary knowledge that educators should have to address these issues of ethics and technology in the classroom. We assess the professional, ethical knowledge of pre-service teachers regarding their use of technologies using a descriptive and exploratory mixed-methods approach. The data for this research come from a Likert-scale questionnaire administered to 616 teacher-training students in Spain, as well as from personal interviews with 411 of them. From these data, we identify four of the eight dimensions of ethical knowledge: professional, ethical knowledge, ethics in the use of technologies, pedagogy for their integration in the classroom, and the use of content specific to the disciplines of pre-service teachers. The results obtained indicate that the preparation of educators with professional, ethical knowledge in training is insufficient, which highlights the need to address this issue in the post-pandemic context of the 21st century. Among the difficulties detected, it should be noted that this study is limited to a European university and a sample chosen for convenience, so it would be advisable to extend the study to other European universities.

The integration of AI tools in education is reshaping how students view and interact with their learning experiences. As AI usage continues to grow, it becomes increasingly important to understand how students' perceptions of AI technology impact their academic performance and learning behaviours. To investigate these effects, we conducted a correlational study with a sample of 44 students to examine the relationship between students' perceptions of ChatGPT’s utility—focusing on usage frequency, perceived usefulness, accuracy, reliability, and time efficiency—and key academic outcomes, including content mastery, confidence in knowledge, and grade improvement. Additionally, we explored how these perceptions influence student behaviours, such as reliance on ChatGPT, procrastination tendencies, and the potential risk of plagiarism. The canonical correlation analysis revealed a statistically significant relationship between students' perceptions of ChatGPT's utility and their academic outcomes. Students who viewed ChatGPT as reliable and efficient tended to report higher grades, improved understanding of the material, and greater confidence in their knowledge. Furthermore, the bivariate correlation analysis revealed a significant relationship between dependency on ChatGPT and procrastination (r = 0.546, p < .001), indicating that a higher reliance on AI tools may contribute to increased procrastination. No statistically significant association was identified between ChatGPT dependency and the risk of plagiarism. Future research should prioritize the development of strategies that promote the effective use of AI while minimizing the risk of over-reliance. Such efforts can enhance academic integrity and support independent learning. Educators play a critical role in this process by guiding students to balance the advantages of AI with the cultivation of critical thinking skills and adherence to ethical academic practices.

This study aims to design, produce, and validate an information collection instrument to evaluate the opinions of teachers at non-university educational levels on the quality of training in artificial intelligence (AI) applied to education. The questionnaire was structured around five key dimensions: (a) knowledge and previous experience in AI, (b) perception of the benefits and applications of AI in education, (c) AI training, and (d) expectations of the courses and (e) impact on teaching practice. Validation was performed through expert judgment, which ensured the internal validity and reliability of the instrument. Statistical analyses, which included measures of central tendency, dispersion, and internal consistency, yielded a Cronbach's alpha of .953, indicating excellent reliability. The findings reveal a generally positive attitude towards AI in education, emphasizing its potential to personalize learning and improve academic outcomes. However, significant variability in teachers' training experiences underscores the need for more standardized training programs. The validated questionnaire emerges as a reliable tool for future research on teachers' perceptions of AI in educational contexts. From a practical perspective, the validated questionnaire provides a structured framework for assessing teacher training programs in AI, offering valuable insights for improving educational policies and program design. It enables a deeper exploration of educational AI, a field still in its early stages of research and implementation. This tool supports the development of targeted training initiatives, fostering more effective integration of AI into educational practices.

The digital age has sparked interest among educators in utilizing information technology, especially artificial intelligence (AI) chatbots. Due to the constant technological involvement, students must also acquire solid digital skills, especially AI proficiency, for learning and everyday life. However, there are few studies on models applying AI in teaching to develop mathematical abilities for high school students. Therefore, this paper proposes a theoretical framework for incorporating AI chatbots into education, boosting students’ mathematical problem-solving competence. Based on student data analysis, this framework will cover teaching, assessment, feedback, and dynamic learning activity adjustment. The paper then explains the operations of AI chatbots to provide personalized feedback. This process emphasizes the importance of error handling and information security, ensuring safety and efficiency in the learning process. This theoretical model supports the integration of AI chatbots in personalized teaching, specifically for improving mathematical skills.

Research fronts are dynamic, knowledge-driven clusters of scholarly activity that emerge in response to pressing problems and/or groundbreaking discoveries. Clarivate Analytics provided a valuable tool based on Citation Productivity and Trajectory (CPT) indicator, which successfully identified particularly hot research fronts on a global scale. To enhance the accuracy and comprehensiveness of identifying both active and emerging research trends, this study develops an extended Clarivate formulation incorporating a novel Impact Factor (IF) metric. The refined approach incorporates growth rates, publication productivity, and the average publication gap between published and citing publications. This method is applied to exploring key research fronts in the digital education ecosystem using bibliometric data from the Scopus database in the period of 2019-2023. The results reveal that artificial intelligence and online learning are the most prominent and influential fields, with virtual reality, blockchain, hybrid learning, and digital literacy representing fast-growing areas. By analyzing both quantitative and qualitative aspects, this work informs key stakeholders about the evolving priorities and trends in the digital educational landscape.

 This study examines the research trends of Design Thinking (DT) in education during the period 2014–2024 through a systematic literature review. This study aims to analyze annual publication patterns, implementation across educational levels, research methodologies, authorship distribution, geographical spread, journal type distribution, and key themes from highly-cited publications in DT education research. The results show a significant increase in publications, especially in 2023–2024, reflecting growing academic interest in DT as an innovative approach to developing 21st-century skills. Qualitative research methods dominate, with most studies involving collaborative authorship. DT application was initially focused on higher education but expanded in secondary education while remaining limited in primary education. Asia leads in research contribution, while Africa shows lower output. Publications are distributed across educational, design-focused, and interdisciplinary journals. These findings underscore the importance of cross-disciplinary and global collaboration to accelerate DT adoption equitably. This study recommends strengthening educator training, developing holistic evaluation methods, and expanding quantitative research for more inclusive DT implementation.

This article aims to answer the research question: How do 5th grade students experience the use of artificial intelligence (AI) tools to create a comic strip describing the survival struggle between the Myna and the Lesser Kestrel? This study utilized a case-study approach to examine the advantages and challenges experienced by 5th grade students using AI tools to create a comic strip about the Lesser Kestrel's survival struggle. Data were collected through qualitative methods, including student reflections, drawings, and analyses of the comic strips they created. Additionally, a questionnaire was used to assess students' attitudes towards the four components of 21st century skills: Creativity, critical thinking, collaboration, and communication. The study indicates that the development of 21st century skills among students requires a collaborative effort involving both parents and teachers. It is not sufficient to rely solely on technological tools; there must be intermediary processes and support from teachers, who are obliged to adjust their teaching methods. Additionally, a teaching approach that supports the creation of a future citizen with a humanistic outlook and awareness of the complexity of life, is essential. This approach develops students' environmental citizenship, which is also an important 21st century skill. This involves integrating ethical, inclusive, and holistic perspectives to address complex problems, such as the survival struggle between the Lesser Kestrel and Myna.

The higher education system in Vietnam is undergoing a significant shift from training based on university capacity to training based on labor market demands. In a developing economy dominated by small and medium-sized enterprises, it is a big challenge to train graduates to meet changing and very diverse competence requirements. AI and machine learning tools are applied in three stages: (a) processing survey data: Expected learning outcomes (pELOs) are quantified into credit values, with each module's contribution determined using the apriori algorithm and expert methods; (b) Optimizing framework curricula (FC): A genetic algorithm identifies module combinations that meet all pELOs while minimizing redundancy within a specified study duration; (c) Framework curriculum adjustment (FCA): An FCA tool, using genetic algorithms, enables schools to update FC annually and allows learners to personalize their programs. WEKA is used to implement the apriori algorithm (https://www.cs.waikato.ac.nz/ml/weka). The PASCAL language is used to write GA and its associated subroutines. Foreign language bachelor's degree programs at the University of Languages and International Studies Vietnam National University, Hanoi, (ULIS-VNU) were used to test algorithms and procedures. According to the calculations, present FCs have caused overstudy and can be modified for every employment post to reduce the surplus credit values that have accrued. Furthermore, FCA can assist in making the curriculum more flexible so that students can more easily switch out FC modules based on their skills and circumstances while still meeting all of the stated ELOs. Under project number N.21.13, this research piece was finished with assistance from ULIS-VNU.

This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.

This research aims to systematically review the integration of artificial intelligence (AI) in English language teaching and learning. It specifically seeks to analyze the current literature to identify how AI could be utilized in English language classrooms, the specific tools and pedagogical approaches employed, and the challenges faced by educators. Using the PRISMA-guided Systematic Literature Review (SLR) methodology, articles were selected from Scopus, Science Direct, and ERIC, and then analyzed thematically with NVivo software. Findings reveal that AI enhances English teaching through tools like grammar checkers, chatbots, and language learning apps, with writing assistance being the most common application (54.55% of studies). Despite its benefits, challenges such as academic dishonesty, over-reliance on AI (27.27% of studies), linguistic issues, and technical problems remain significant. The study emphasizes the need for ethical considerations and teacher training to maximize AI’s potential. It also highlights societal concerns, including the digital divide, underscoring the importance of equitable access to AI-powered education for learners of all socioeconomic backgrounds.

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 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.