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.

Work-based learning (WBL) is an important tool for enhancing students' employability skills in vocational education and training. Many studies have underlined the importance of a variable of WBL, self-efficacy, and vocational identity in developing vocational students' employability skills. Nonetheless, the research is limited and examined separately. Therefore, this study investigates how WBL, self-efficacy, and vocational identity influence employability skills and how self-efficacy moderates between WBL and employability skills. Four hundred and three state university students in Yogyakarta were involved in the data collection. This study used structural equation modeling (SEM) analysis to test its hypothesis. The results of the study revealed that the implementation of WBL did not have a direct effect on employability skills; however, self-efficacy was able to moderate the relationship between WBL and employability skills. However, WBL directly influences vocational identity, which in turn directly influences employability skills, while self-efficacy also directly influences employability skills. This research has important implications for improving learning that can improve students' self-efficacy skills in an effort to build students' employability skills in vocational education and training.

With the introduction of the new curriculum, primary school Croatian fine arts teachers faced many challenges. The fundamental problem, the lack of school lessons, is highlighted by the challenges of implementing problem and project work and integrating technology into the teaching process. Therefore, it is important to organise activities where pupils use modern tools and media for high-quality educational purposes. The animation was chosen as a fine arts technique that can fulfil these requirements. Qualitative action research was conducted in city Split in the school year 2023/2024. The 5th and 8th grade pupils (125) and their fine arts teachers (3) participated in the research. A total of 75.23% of respondents were positive about the occasional introduction of technology into fine arts classes. After completing the fine arts project, 77.98% of pupils stated that they now have the knowledge and skills to create an animation themselves, while 77% of pupils consider their digital stories to be interesting and original. All three fine arts teachers cite a lack of continuity due to the small number of lessons as a shortcoming. The results point to poor material conditions: overheating of old projectors, low-quality tablets, poor internet connection and lack of power sockets. The research has shown that there are discrepancies between the prescribed theoretical requirements for learning outcomes and the possibilities for their practical realisation. As a result, teachers need to take a keen interest and receive additional training to ensure that the projects designed are in line with the requirements of the curriculum.

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.

The field of engineering education is constantly evolving to meet the challenges of technological and societal advancements. Continuous research should be conducted to identify the potential match between the skills that employers seek and the ones engineering graduates gain at college. The purpose of this study is to identify areas of agreement and disagreement between the faculty and employers regarding the skills and knowledge that engineering students acquire during their undergraduate education. The study uses an explanatory sequential design method by employing a questionnaire that was developed based on the Engineering Competency Model (ECM) by the Employment and Training Administration of the USA Department of Labor and responded to by 125 volunteer engineering faculty. Additionally, interviews were conducted with 2 industry professionals to gain deeper qualitative insights. The study found that while faculty mainly stated that students acquire personal effectiveness, academic, and workplace competencies in college, employers disagreed with these perceptions, particularly regarding interpersonal skills, integrity, professionalism, writing, and communication. Additionally, the study found a significant mismatch between faculty and employer assessments of industry-wide competencies, with employers expressing concerns about graduates' preparedness in areas like design, business, and sustainability. These findings suggest significant updates and cooperation with industry experts in engineering curricula and their implementation.

Integrated learning has emerged as an approach to developing critical thinking and creativity skills. This meta-analysis synthesizes the effects of integrated learning on critical thinking and creativity, drawing from experimental studies published over the past decade. This study addresses the research question: What are the impacts and factors influencing integrated learning on students' critical thinking skills and creativity compared to conventional learning over the past decade? In the database, 403 articles were obtained, which were then supplemented by seven articles from manual searches, so that there were a total of 410 articles. After a strict inclusion process, 8 articles were selected for analysis. The inter-rater reliability test using Cohen's Kappa coefficient produced a value of 0.78, which indicates a substantial level of agreement. The analysis includes data from 497 participants for critical thinking and 266 for creativity. Heterogeneity in critical thinking skill data was 96%, and in creative skills, it was 86%; then, a subgroup analysis of education level was carried out to find out where the high heterogeneity was. The results show a significant positive effect of integrated learning, with an SMD of 1.48 (p = .004) for critical thinking and 1.60 (p = .0001) for creativity. Funnel plots and Egger's regression tests indicate no risk of publication bias. In addition, this study also synthesized the causal factors of how integrated learning affects critical thinking skills and creativity. Further research is recommended to explore its long-term impact and implementation strategies in various educational settings.  

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.

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.

Critical thinking is highly valued as an integral skill for promoting students’ development, and problem-based learning (PBL) is widely used as an essential method to facilitate the development of critical thinking. However, since individual studies cannot determine the precise overall effect size of PBL on the development of critical thinking, it is difficult to systematically analyze the various influencing factors that hinder PBL from achieving sufficient effectiveness. Therefore, this study adopts a meta-analysis method to examine PBL in depth, aiming to clarify the crucial methods and elements of applying PBL to enhance critical thinking and address the shortcomings of existing studies. This study investigates two primary questions: first, the efficacy of PBL in enhancing critical thinking skills in comparison to traditional pedagogical approaches, and second, the influence of moderating variables on the effectiveness of PBL. To address these questions, a total of 25 studies were selected for meta-analysis. The findings revealed an overall effect size of 1.081 under the random-effects model, with a confidence interval of [0.874, 1.288] and p < .05, indicating that PBL significantly outperforms traditional methods. The analysis demonstrated that the effectiveness of PBL is not significantly influenced by learning stage, sample size, or measurement tools, thereby broadening the applicability of PBL and challenging preconceived limitations associated with its implementation. However, the results also indicated that PBL effectiveness is moderated by teaching methods and subject types, which offers critical insights for educators seeking to adapt their instructional strategies when employing PBL.

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.

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.

Mindfulness, recognized as a protective factor against learning burnout in higher education, has garnered considerable attention, yet its underlying mechanisms remain underexplored. This study examined the relationship between mindfulness, regulatory emotional self-efficacy, and learning burnout. Data from 461 Chinese university students were collected using a correlational design and cluster sampling method, employing the Five Facet Mindfulness Questionnaire, University Student Learning Burnout Scale, and Regulatory Emotional Self-Efficacy Scale. Hypotheses were tested using partial least squares structural equation modeling. Results showed that Participants exhibited above-average mindfulness (M=3.090), learning burnout (M=3.278), and regulatory emotional self-efficacy (M=3.417). Results revealed that mindfulness is directly and negatively related to learning burnout (β=-0.679, t = 28.657, p < .001). Regulatory emotional self-efficacy (β = -0.357, t = 8.592, p < .001) was significantly and negatively related to learning burnout. Mindfulness was significantly and positively related to regulatory emotional self-efficacy (β = 0.638, t = 24.306, p < .001), and regulatory emotional self-efficacy (R2: from .461 to .537) partially mediated the relationship between mindfulness and learning burnout. Besides, the Importance-Performance Matrix Analysis revealed that managing negative emotions significantly contributes to learning burnout but performs poorly, whereas non-reacting demonstrates both the lowest contribution and performance. Findings suggest that mindfulness indirectly alleviates learning burnout through regulatory emotional self-efficacy, providing evidence-based insights for targeted mindfulness interventions in higher education.

The aim of this study is to investigate how postgraduate English as a Foreign Language (EFL) students perceive the impact of the Postgraduate Professional Diploma in Teaching program on promoting effective education for sustainable development (ESD). The study employed a descriptive approach, involving 228 participants. The study employed a 28-item questionnaire, and the data were analyzed using IBM SPSS Statistics software. The findings suggest that participants believe the program employs strategic actions and policies to achieve its long-term development goals for effective English teaching. The results also showed that participants place a high value on the program because it encourages innovative efforts to enhance the quality of learning, provides them with a range of assessment tools to help teach English effectively in the long term, and offers students opportunities to practice feedback techniques that enhance learning in the classroom. Furthermore, because participants were exposed to a pre-service training program in an effective manner, the results demonstrated statistically significant differences among participants regarding the program's impact on promoting effective ESD based on the study's variables. According to the study, it is also necessary to incorporate sustainable development concerns into English instruction and provide participants with information about the benefits of sustainable development.

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.