Successfully solving reality-based tasks requires both mathematical and text comprehension skills. Previous research has shown that mathematical tasks requiring language proficiency have lower solution rates than those that do not, indicating increased difficulty through textual input. Therefore, it is plausible to assume that a lack of text comprehension skills leads to performance problems. Given that different sociodemographic characteristics and cognitive factors can influence task performance, this study aims to determine whether text comprehension mediates the relationship between these factors and competence in solving reality-based tasks. Additionally, it examines the impact of systematic linguistic variation in texts. Using an experimental design, 428 students completed three reality-based tasks (word count: M = 212.4, SD = 19.7) with different linguistic complexities as part of a paper-pencil test. First, students answered questions about the situation-related text comprehension of each text, followed by a mathematical question to measure their competence in solving reality-based tasks. The results indicate that: a) Tasks with texts of lower linguistic complexity have a significantly higher solution rate for both text comprehension (d = 0.189) and mathematical tasks (d = 0.119). b) Cognitive factors are significant predictors of mathematical solutions. c) Text comprehension mediates the relationship between the impact of students’ cultural resources and cognitive factors and their competence to solve reality-based tasks. These findings highlight the importance of linguistic complexity for mathematical outcomes and underscore the need to reinforce text comprehension practice in mathematical education owing to its mediating role.

Augmented reality offers diverse potential applications for STEM education, enabling students to engage directly with virtual elements in real-time and providing them with immersive, natural experiences. This study conducted a bibliometric analysis of articles on this topic on the Scopus database to determine some quantitative information, such as general information about publications, countries, institutions, authors with the most contributions, and key trends in applying augmented reality technology in STEM education. An analysis of 201 studies published from 2005 to 2023 using Biblioshiny software and VOSviewer reveals that the United States leads in the number of studies conducted on this issue. Kryvyi Rih National University, Ukraine, has the most studies. The authors who contributed the most studies with the most citations on this issue are Lindner, C. and Rienow, A. from Ruhr University Bochum, Germany. Two primary research trends emerge, focusing on how Augmented Reality technology is utilized, particularly in STEM fields like Chemistry, which combines learning forms with other learning support tools and media such as mobile applications. Secondly, integrating augmented reality and virtual reality technologies into STEM programs at the university level, design of games, and virtual tools. This study offers important data for researchers looking to explore future applications of augmented reality technology within STEM education.

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 develop and validate a green behavior instrument based on local potential using structural equation modeling (SEM) with smart partial least squares (SmartPLS). The instrument consists of 40 statements covering five main indicators: environmental maintenance, waste reduction, saving natural resources, sustainable mobility and consumption, and community education. This study addresses a gap in existing research by creating a context-specific tool for assessing green behavior, incorporating local cultural and ecological factors. While prior studies emphasize global sustainability principles, they often overlook the significance of local practices and values, which are essential for effective environmental education. By integrating local potential, this instrument bridges global sustainability goals with regional contexts, enabling meaningful and practical student engagement. The instrument was validated through content validity testing, exploratory and confirmatory factor analyses, and construct validity and reliability testing using SEM with SmartPLS. The results indicate strong content validity, with content validity index (CVI) values ranging from .80 to .90. After analysis, 34 valid items were retained from the initial 40. This study contributes to the literature by developing an instrument that aligns with global sustainability goals while integrating local cultural practices and ecological contexts. It offers insights into how local knowledge enhances sustainability education, providing a holistic framework for assessing green behavior across diverse regions.

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.

This study evaluates the impact of an educational intervention strategy – Project-Based Learning (PBL) – designed to enhance university students' knowledge of the Sustainable Development Goals (SDGs), their integration into academic curricula, and their relevance for future professional and personal applications. The research is motivated by the recognised importance of the SDGs in education and the current limited integration and understanding within higher education settings. The study applied a pre-test and post-test experimental design used, involving 199 first-year students from the University of Cordoba (Spain), enrolled in Primary and Early Childhood Education programmes. The intervention comprised PBL activities aimed at increasing knowledge and perceptions of the SDGs. Data were collected using a questionnaire assessing three dimensions: knowledge of the SDGs, the importance of their inclusion in the curriculum, and the perceived relevance of applying SDG principles in professional and personal contexts. The findings indicate that the intervention strategy effectively improved, albeit partially, students' understanding and perception of the SDGs. There was a significant improvement in students' knowledge. However, regarding the perceived importance of integrating the SDGs into their curriculum and the relevance of the SDGs for their future professional and personal lives, no effects were observed. These results underscore the partial efficacy of PBL in promoting sustainability competences and global citizenship among students, suggesting the need to explore other pedagogical methodologies for greater effectiveness. The study advocates the integration of SDGs into higher education curricula to better prepare students for future challenges, emphasising the need for further research to explore the long-term impacts and broader applicability of such educational intervention.

 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 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.

: In this study, we present a didactic experience carried out in a public university with 60 students enrolled in the 2nd year of the Primary Education Teaching Degree. This experience consisted of implementing a teaching and learning sequence in which proportionality problems (mathematics content) and pure substances and mixtures (experimental science content) were addressed together in an ecodidactic garden context. This work presents the results obtained through the analysis of the students' responses. Our findings suggest that pre-service teachers have difficulties similar to those of primary and secondary students in the use of measuring instruments and conversion units, as well as in the calculation of proportions in mathematics and conceptual errors at a microscopic and macroscopic level in experimental sciences. This study highlights the need to design and implement strategies to support students in their formative process in relation to the contents of proportion and matter. As an added value in our work, we emphasize the interdisciplinary connection between mathematics and experimental sciences, offering a more real-life perspective of science.

This article provides a comprehensive guide to conducting and documenting systematic reviews (SRs) in educational research. While SRs are increasingly recognized for their value in synthesizing and evaluating literature on specific research questions or topics, there remains a notable scarcity of research-based papers that guide their development within the field of education. Systematic reviews, distinguished from traditional literature reviews by their standardized processes—including systematic searching, selection, and critical appraisal of relevant studies—offer a more accurate and comprehensive understanding of the research landscape by integrating findings from multiple sources. This paper underscores the importance of adhering to established methodologies and guidelines to ensure the quality and reliability of SRs. Essential elements discussed include defining research questions, developing search strategies, applying inclusion and exclusion criteria, and synthesizing results. The paper also highlights the role of frameworks such as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in enhancing transparency and methodological rigor. By following this structured approach, researchers can produce systematic reviews that provide valuable insights into educational practices and policies, thereby supporting evidence-based decision-making and advancing the field of education.

The use of technology in education aims to improve students' problem-solving skills so that they have the skills needed by 21st century society in dealing with various kinds of challenges and problems. This study was carried out aiming to obtain the characteristics of augmented reality-based learning media for mathematics learning in student problem-solving skill, and obtain the results of the average difference in the problem-solving abilities of junior high school students. This research and development (R&D) study, employing the (Analyze, Design, Develop, Implement, and Evaluate) ADDIE model, investigated Augmented Reality (AR)-based learning media for mathematics to enhance junior high school students' problem-solving skills. The aim was to identify characteristics of effective AR media and assess their impact on problem-solving abilities. Quantitative data, collected from student problem-solving tests, were analyzed using a t-test in SPSS. The AR media featured virtual manipulation, virtual measurement tools, and real-world problem exploration. While the experimental group using AR showed a higher average problem-solving score compared to the control group using Problem-Based Learning (PBL), the difference was not statistically significant (t (df) = 0.638, Cohen's d = 0.0876). This lack of significance, despite the higher mean, is likely due to a small sample size and the limited integration timeframe, coupled with pre-existing skill variations. Future research should address these limitations to further explore the potential of AR in mathematics education.

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.

Research in mathematics education and interdisciplinarity is varied and extensive, covering multiple approaches that reflect a growing interest in this type of perspective. The objective of this study is to systematize the findings of research on interdisciplinary mathematics education published between 2019 and 2024. The review was carried out following the guidelines of the PRISMA statement, allowing us to identify 49 articles published in journals indexed in the Web of Science (WOS) and Scopus databases. Subsequently, a content analysis was carried out to identify methodological and theoretical aspects present in the studies reviewed, such as methodology employed, education level of participants, disciplines integrated with mathematics, and types of interdisciplinary tasks proposed. Additionally, four main research themes were identified: (a) understanding of interdisciplinarity; (b) pedagogical strategies for interdisciplinary development in mathematics education; (c) interdisciplinarity for the development of mathematical skills; and (d) professional development of mathematics teachers. The results reveal a sustained increase in the number of publications, which reflects a growing interest in the interdisciplinary approach in mathematics education. Finally, several challenges and opportunities are highlighted for future research, including the need to develop an interdisciplinary teacher training model, the creation of pedagogical strategies that promote greater interconnection between disciplines, and the need to carry out more studies focused on early childhood and primary education in this area.

In recent years, particularly over the last 10–15 years, the scientific value of oral history in relation to historical and social knowledge has been increasingly recognized. Since the 1980s, oral history researchers have pioneered new approaches to interpreting human experience, highlighting oral biographical narratives and recounted experiences as valuable material for social research. According to Tsiolis, the biographical narrative approach has become an established scientific methodology for studying how social actors interact with their environment and respond to social changes, as these are reflected through their subjectivity. Based on the above, this study involved four academic lecturers who narrated their academic and professional journeys, the factors that influenced them, the challenges they faced, and the positive impact of their engagement with teaching in the context of Greek higher education. The research employed the methodology of narrative biography, allowing the participants to recount their academic trajectories spontaneously, recalling personal experiences and memories. Through these narratives, qualitative data emerged, which were analyzed and coded into common themes, leading to conclusions about the conditions, aspirations, and disappointments experienced by academics in higher education institutions. The paper concludes some limitations of the current study and offers suggestions for future research.

This study aims to develop a competency framework for teaching natural science under the blended learning (BL) model for Natural Science education students at Thai Nguyen University of Education. Recognizing the increasing importance of BL in the context of modern education and the challenges teachers face during implementation, the modified Delphi method was employed to collect expert opinions, involving three rounds of surveys with 50 participants, including university lecturers and secondary school educational administrators. The research identifies seven core competency groups, including specialized knowledge, lesson design and evaluation competencies, classroom organization and management, student assessment and feedback, information technology competencies, experiment and simulation utilization in teaching, and basic knowledge of BL. The findings highlight the necessity of blending traditional teaching methods with modern technology to effectively implement the BL model, enhancing both the teaching process and students' learning outcomes. This framework is expected to serve as a crucial basis for teacher training universities to adjust their curricula and support educational administrators in fostering and enhancing the capacity of natural science teachers at the secondary level. This competency framework aims to support the professional development of Natural Science teachers and education students, ensuring their preparedness for the evolving demands of modern education. Furthermore, the study provides insights into the skills and knowledge that teachers need to acquire to adapt to the continuously evolving educational environment.