International Journal of Technology in Education and Science
The International Journal of Technology in Education and Science (IJTES) is a peer-reviewed scholarly online journal published quarterly in Winter, Spring, Summer, and Fall. IJTES is an international journal that welcomes original and unpublished research papers in the field of educational technology. The journal publishes a wide range of scholarly contributions, including original theoretical studies, literature reviews, research reports, review articles, book reviews, and studies addressing social, psychological, curricular, and learning environment–related issues that involve the use or application of technology in education and science. The primary focus of the journal is educational technology. Therefore, submissions from science-related fields are required to clearly demonstrate their relevance to educational technology contexts or applications. The journal aims to cover a broad range of topics related to educational technology. All manuscripts submitted to IJTES must be original, unpublished, and not under consideration for publication elsewhere at the time of submission. The journal adheres to international standards of publication ethics and follows the guidelines of the Committee on Publication Ethics (COPE). Authors are expected to ensure the originality of their work, avoid plagiarism, and disclose any potential conflicts of interest. The journal encourages submissions from researchers worldwide and is committed to promoting diverse international perspectives in scholarly research. Editors and reviewers devote substantial time and effort to the evaluation of submissions. Accordingly, manuscripts may not be withdrawn without the approval of the Editorial Office at any stage of the publication process, including peer review, revision, production after acceptance, or post-publication. All submissions undergo an initial editorial screening, which is typically completed within two weeks. Manuscripts that pass this stage are subsequently evaluated through a double-blind peer-review process, which generally takes two to four months. Each manuscript submitted for consideration in a regular or special issue is reviewed by at least two independent international reviewers with expertise in the relevant subject area. Authors are responsible for ensuring that the final accepted version of their manuscript is thoroughly proofread for language quality and fully complies with APA style guidelines. Manuscripts that fail to meet language and/or APA style standards at any stage of the review or publication process will be rejected. The galley proof will be shared with the authors before publication. Any final requests for changes should be made at this stage. Once the paper has been published, no further changes can be made. The corresponding author must use an institutional email address. Submissions or requests submitted using generic or non-institutional email addresses (e.g., @gmail.com, @yahoo.com, @hotmail.com) will not be processed, as such addresses do not allow for unambiguous identification of the author. Use of the journal template is not mandatory at the submission stage. The template may be used after a manuscript has been accepted for publication.
The Effect of AI-Supported Patriotism Education Based on Folk Tales on Hearing-Impaired Primary School Students: An Experimental Study
This study aims to examine the effect of AI-assisted patriotic education based on Kazakh folk tales on the academic achievement and attitudes towards the lesson of hearing-impaired 3-4th-grade primary school students. The research was conducted using a quasi-experimental design with a pre-test-post-test control group. The study group consisted of a total of 31 hearing-impaired students attending a primary school in Almaty. In the experimental group, 16 students received AI-assisted instruction, while the 15 students in the control group received the same content using traditional methods. The experimental process was conducted for six weeks; AI avatars, visual generation tools, chatbots, story completion applications, and sentiment analysis tools were used in the experimental group. In the control group, teacher-centered instruction, printed visuals, and traditional activities were preferred. The Patriotism Concept Achievement Test and the Attitude Scale towards the Lesson were used as data collection tools. The obtained data were analyzed using non-parametric tests. The findings showed no significant difference between the experimental and control groups before the intervention. However, after the intervention, it was determined that the experimental group had significantly higher scores in both academic achievement and attitude towards the lesson compared to the control group. The results show that AI-supported and culturally-oriented teaching applications facilitate the understanding of abstract concepts by hearing-impaired students, increase their learning motivation, and make values education more effective. The research offers important implications for the use of AI-supported cultural teaching applications in special education.
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Evidence on the Metaverse’s Impact in Higher Education: A Systematic Review and Meta-Analysis
This study presents a systematic review and meta-analysis of twenty peer-reviewed empirical studies that examined the use of Metaverse technologies in higher education. The study aims to examine current research trends, academic fields, benefits, challenges, and the overall impact of Metaverse applications on student learning outcomes. The meta-analysis results indicate that Metaverse-based educational interventions have a significant positive impact on learning effectiveness (Hedges’ g = 0.758, p < .001), with varying effect sizes across different academic disciplines. The strongest effects were detected in arts and humanities, health and welfare, and business, administration and law. The included studies revealed several educational benefits, most notably improved learning outcomes, increased student motivation, and enhanced engagement and interaction within immersive learning environments. On the other hand, several challenges emerged, including technical issues, high implementation costs, and cognitive overload. Moreover, the results highlighted significant research gaps, including a limited number of studies addressing the needs of students with disabilities or gifted students, and insufficient attention to issues related to inclusion and accessibility. The results generally indicate that the Metaverse has promising potential for reshaping learning experiences in higher education, but further longitudinal and experimental studies are needed to explore its effectiveness across diverse disciplines and educational contexts.
Developing an Ethno Mandailing-Realistic Mathematics Education Model Assisted by Adobe After Effects to Improve Numeracy Literacy of Elementary School Students
This study developed the Ethno Mandailing-Realistic Mathematics Education (Ethno Mandailing-RME) Model, assisted by Adobe After Effects, to improve elementary school students' numeracy literacy skills by integrating local Mandailing culture, especially Bagas Godang and Sopo Godang, into realistic mathematics learning through visual animation media. The study used a research and development approach based on the Plomp model, which includes the initial investigation, design, development, implementation, and evaluation stages, involving 125 fifth-grade students from three public elementary schools in Mandailing Natal Regency, comprising 69 students in the experimental class and 56 in the control class. The results of expert validation showed that the developed model was in the very valid category with an average score of 4.58. At the same time, the practicality aspect was reflected in the high teacher response rate (91%), student response rate (89%), and learning implementation rate (93%). The effectiveness test showed that the experimental class achieved higher average posttest and N-Gain scores than the control class, and statistical analysis confirmed a significant difference in numeracy literacy improvement. Thus, the Ethno Mandailing-RME Model, assisted by Adobe After Effects, has been proven valid, practical, and effective in improving numeracy literacy skills among elementary school students.
Mapping the Intersection of MOOCs, AI Literacy, and Multilingual Academic Writing: A Bibliometric Analysis of Global Research Trends and Knowledge Structures
This study aims to examine scientific publications produced at the intersection of massively open online courses (MOOCs), AI literacy, and multilingual academic writing using bibliometric methods, covering the period from 2020 to 2026. Thirty-eight publications obtained from the Web of Science and Scopus databases and scanned according to the PRISMA protocol form the focus of the bibliometric analysis performed via VOSviewer software and the qualitative content analysis conducted using a mixed inductive-deductive approach. The findings reveal a significant and accelerating upward trend in publication volume during the period examined. This increase, particularly accelerating from 2023 onwards, coincides with the widespread integration of generative AI tools into academic contexts. Geographic distribution shows a dominance of China-based institutions; however, studies from countries such as Rwanda, Kazakhstan, and Indonesia also contribute to discussions on linguistic justice and multicultural representation in the literature. Keyword co-occurrence analysis revealed four core conceptual clusters: AI literacy frameworks and academic writing norms; technology-assisted language learning and student outcomes; ethical concerns and critical gaps; and language MOOCs and cultural context. Content analysis findings were structured around three interconnected thematic frameworks: multilingual academic writing and AI-assisted literacy tools; technological and institutional variables influencing AI adoption; MOOC design and the pedagogical implications of AI literacy in multilingual language education. The study emphasizes the need for MOOC design to be sensitive to the identities of multilingual learners and for the systematic integration of AI literacy components into higher education programs. As one of the first bibliometric studies to address these three areas together, the research provides a unique reference framework for the fields of educational sciences, Applied Linguistics and science of science.
Bridging Scientific Literacy and Sustainability Awareness via 7E Inquiry Learning: A Digital Simulation Approach
As global educational standards shift toward addressing environmental crises, integrating sustainability values into science curricula has become imperative. This study develops a 7E inquiry learning cycle model integrated with digital simulations designed to enhance students' scientific literacy, anchored in sustainability awareness and the PISA 2025 framework. Using the Plomp research and development model, the study involved Science Education students selected via purposive sampling. Data were collected through expert validation sheets, scientific literacy tests, and a sustainability awareness questionnaire. The results indicate that the developed model and digital simulation-based learning devices achieved "very high" validity and practicality, with one-to-one (81.96%) and small group (85.45%) evaluations confirming their readiness for classroom implementation. Effectiveness tests revealed that the 7E digital simulation model, sustainability awareness, and their interaction significantly improved students' scientific literacy. The effect size (partial eta squared) demonstrated a robust influence from the learning model (ηp2 = 0.83) and sustainability awareness (ηp2 = 0.74).These findings suggest that bridging inquiry learning with digital tools effectively fosters the dimensions of explaining phenomena and interpreting scientific data within a value-based framework. This research contributes to the discourse on how higher education can cultivate responsible citizenship by embedding sustainability and digital-interactive inquiry as core values in science learning, aligning with the evolving requirements of the PISA 2025 framework.
An Explainable AI-Based Decision Support System for Teaching and Classifying Hair Loss Types
Hair loss is a multifactorial condition that requires accurate classification to support reliable and personalized decision-making. However, many existing machine learning approaches suffer from data leakage and limited interpretability, reducing their robustness and practical applicability in decision support systems. This study proposes a leakage-resistant machine learning framework for multi-class hair loss classification, integrating explainable artificial intelligence to enhance transparency and reliability. The framework employs a unified preprocessing pipeline within nested cross-validation to prevent information leakage, while SMOTEENN is used to address class imbalance. Several algorithms, including Decision Tree, Random Forest, K-Nearest Neighbors, Logistic Regression, and Extreme Gradient Boosting, are evaluated within this pipeline. Experimental results indicate that Extreme Gradient Boosting achieves the best performance, with an accuracy of 0.8300, F1-score of 0.7908, and AUC of 0.9305 in nested cross-validation. Evaluation on a holdout dataset demonstrates stable generalization, achieving an accuracy of 0.8250, F1-score of 0.7517, and AUC of 0.9182. Furthermore, the integration of explainable artificial intelligence enables interpretable predictions that can be utilized in a rule-based decision support system. Overall, the proposed framework provides a robust, transparent, and leakage-resistant solution for reliable machine learning-based classification in practical applications.
Examining the Relationships between University Students’ 21st Century Skills, Artificial Intelligence Literacy and Artificial Intelligence Attitudes
The purpose of the present study is to examine the relationships between 21st century skills, artificial intelligence literacy, and artificial intelligence attitudes of fine arts faculty students. The relational survey model, one of the quantitative research models, was used in the research. The population consists of students studying at Fine Arts Faculties in Turkey, and the sample includes 215 students from three universities determined by convenience sampling. To collect data, “Artificial Intelligence Attitude Scale,” “Artificial Intelligence Literacy Scale” and “21st Century Skills Scale” were used. According to the results, there was no significant difference in artificial intelligence literacy and general attitudes toward artificial intelligence regarding gender, though male students’ social responsibility and leadership skills were higher. Grade level did not significantly affect artificial intelligence literacy or positive attitudes, except for negative attitudes. Similarly, no significant difference was found in 21st century skills regarding career awareness, entrepreneurship, innovation, and total scores.
Attitudes towards artificial intelligence
A Literacy Proposal to Prevent the Negative Effects of Digital Games: The Digital Game Literacy Model
The aim of this study is to develop a digital game literacy model with educational and instructive content as a solution to prevent the negative effects of digital games. Since media literacy is included in the middle school curriculum, this developed model is planned to be applied to middle school students. The model will consist of four stages: the first stage is identifying the case (problem) and acting, the second stage is literacy education, the third stage is design and software education and the fourth stage is establishing connections, making sense, and offering solutions. Before applying the model to students, a comprehensive training for trainers will be implemented for middle school teachers who will provide the training. This 14-week program will provide training on media and communication, education and literacy, and design and software. After this training, the model will be piloted in a selected region, and if the results are positive, a general training program will be initiated. It is believed that the implementation of the digital game literacy model will create social awareness regarding the negative aspects of digital games and transform students into conscious individuals capable of evaluating games from all perspectives.
Impact of Smart Governance on Institutional Sustainability: Empirical Evidence from Higher Education
The objective of this study is to examine the impact of smart governance on institutional sustainability in Saudi higher education. The study employs a quantitative cross-sectional survey design and a stratified random sample of 611 faculty members. In particular, the study estimates a linear model of institutional sustainability where smart governance in conjunction with the contextual variables of ICT and institutional culture are exogenous, and that the impact of smart governance on institutional sustainability is mediated through institutional performance. The results show that smart governance has a significant positive impact on institutional sustainability in Saudi higher education institutions. This supports the growing body of literature suggesting that digital transformation and participatory governance enhance organizational effectiveness and long-term sustainability. The study also finds that institutional performance mediates this relationship, indicating that smart governance tends to augment sustainability indirectly by enhancing efficiency, innovation, and responsiveness. Furthermore, the results of the study are consistent with the claim that Institutions with strong technological capabilities and supportive cultures are better positioned to leverage smart governance for sustainability. Toward this end, the study contributes to the extant literature on the subject by stretching smart governance literature into the Saudi higher education context, integrating governance, performance, and sustainability into a single framework, and documenting empirical evidence supporting the mediation mechanism of institutional performance and contextual effects of information technology integration and institutional culture.
The Effectiveness of AI-Based Learning Media (AI REG) in Improving Rhythmic Gymnastics Activities among Students
The development of Artificial Intelligence (AI) technology has influenced various fields of life, including the field of sports education. Currently, AI is beginning to be used to detect movement patterns, analyze performance, and provide real-time feedback to learners. Educators using AI can support learners in mastering the accuracy of complex movements in gymnastics through digital body movement visualization, particularly in sports education. The aim of this study is to develop AI-based learning media that can shape both learners and educators in rhythmic gymnastics activities. The research method used is an experiment with a one-group pretest-posttest design. The data collection technique uses test questions. The data analysis technique uses the paired sample t-test. The research results show that there is a significant difference in the average before and after learning using AI-based learning media. Students have better rhythmic gymnastics skills compared to before the learning. These results prove that AI-based learning media has a significant effect on rhythmic gymnastics skills. This research shows that the media used by educators, when easily implemented on students, will have a positive impact. AI-based learning media can effectively improve rhythmic gymnastics skills in students.
The Role of Technology-Enhanced Learning Media in Supporting Innovative Teaching Methods in Higher Education
This study conducts a Systematic Literature Review (SLR) to analyze the role of technology‑enhanced learning media in supporting innovative teaching methods in higher education during the digital transformation era (2015–2025). Guided by the PRISMA framework, it synthesizes 20 peer‑reviewed studies from international and national (SINTA‑indexed) journals that examine the integration of digital media such as Learning Management Systems (LMS), interactive multimedia, mobile applications, and AI‑based tools with pedagogical models such as blended learning, flipped classroom, project‑based learning, and problem‑based learning. The findings reveal that technology‑enhanced learning media significantly improve student engagement, motivation, critical thinking, and learning outcomes when embedded within learner‑centered pedagogies and supported by sound instructional design. However, the review also identifies challenges such as limited infrastructure, unequal digital literacy, and insufficient institutional strategies that hinder optimal implementation. Conceptually, three major insights emerge: (1) technology acts as a pedagogical amplifier rather than a substitute; (2) its effectiveness is context‑dependent, mediated by digital competence and institutional readiness; and (3) the evolution of intelligent ecosystems is shifting higher education toward AI‑driven personalization. Future research should explore longitudinal, discipline‑specific impacts and cross‑context comparisons to strengthen sustainable and equitable digital learning ecosystems.
Does Digital Pedagogical Training Enhance Teacher Resilience, Teacher Well-Being, and Teacher Performance? A Quasi-Experimental Study
Teachers face increasingly strong demands to adapt to digital-based learning, yet empirical evidence regarding the influence of digital pedagogical training on teacher resilience, teacher well-being, and teacher performance remains limited. This study aims to analyze the influence of digital pedagogical training on teacher resilience, teacher well-being, and teacher performance. This study employs a quantitative approach through a quasi-experimental method with a pretest-posttest non-equivalent control group design. The research sample consisted of 60 teachers affiliated with two Teacher Working Groups (Kelompok Kerja Guru/KKG) across two sub-districts in Serang City, Banten Province, selected through purposive sampling and cluster sampling. Data were collected using a five-point Likert scale questionnaire. Instrument quality was established through content validity based on expert judgment, construct validity using Pearson Product Moment correlation, and reliability using Cronbach's Alpha; all final instruments were declared valid and highly reliable. Data analysis was conducted through descriptive statistics, prerequisite tests comprising Kolmogorov–Smirnov normality testing, Levene's Test for homogeneity, and an initial equivalence test using an independent sample t-test, followed by MANCOVA and ANCOVA. The findings indicate that (1) digital pedagogical training has a significant effect on teacher resilience; (2) digital pedagogical training has a significant effect on teacher well-being; and (3) digital pedagogical training has a significant effect on teacher performance. Overall, digital pedagogical training exerts a positive and significant influence, both simultaneously and individually, on teacher resilience, teacher well-being, and teacher performance. The implications of this study suggest that digitally-based teacher professional development should be designed not only to strengthen technological and pedagogical competencies, but also to support teachers' psychological capacity and professional sustainability.
Digital Competence and Burnout in Technology-Mediated Higher Education
Teacher burnout is a persistent concern in higher education, particularly as instructors strive to maintain effective teaching in technology-intensive work environments. This quantitative correlational study examined the relationships among teacher self-efficacy, digital competence, and burnout among 43 EFL instructors at two Turkish state universities. Participants completed an online survey measuring all three constructs via validated instruments. Pearson correlations and multiple regression were used to test associations and to model the simultaneous contributions of self-efficacy and digital competence to burnout variance. Instructors reported high self-efficacy, moderate-to-high digital competence, and moderate burnout. Self-efficacy was positively associated with burnout, whereas digital competence was negatively associated with it. The two predictors were not significantly correlated with each other. In the regression model, both predictors were independently significant and jointly accounted for 36.6% of the variance in burnout (R² = .366). The positive self-efficacy-burnout association, which runs counter to the general literature, may reflect role overload in technology-demanding environments or a mismatch between the general teaching self-efficacy measure and technology-specific demands. Digital competence appeared to function as a buffering resource consistent with job demands-resources (JD-R) predictions. Findings suggest that higher education institutions should consider targeted digital professional development and workload management as complementary strategies for supporting instructor well-being.
EFL Students’ Perceptions on Using Artificial Intelligence (AI) as a Tool in Developing Reading Comprehension
The growing integration of artificial intelligence (AI) in education has increasingly shaped English as a Foreign Language (EFL) learning experiences. While prior research has largely focused on vocabulary and writing skills, limited attention has been given to the role of AI in developing reading comprehension, particularly within Saudi higher education contexts. This study aimed to investigate Saudi EFL learners’ perceptions and acceptance of AI tools for reading development through the Technology Acceptance Model (TAM). A quantitative approach was employed, with data collected through a structured questionnaire measuring the TAM constructs in addition to reading strategy support. To examine relationships among the constructs, data were analyzed using descriptive and correlational methods. The findings revealed generally positive perceptions toward the use of AI tools, along with significant relationships among TAM variables and the emergence of Perceived usefulness as a key factor influencing the usage of AI tools. Results support the applicability of TAM in explaining AI adoption in EFL reading contexts and highlight the importance of aligning AI tools with specific instructional objectives to ensure effective integration in Saudi higher education. Furthermore, it is recommended that educators integrate AI tools in ways that explicitly support core reading strategies such as skimming, scanning, and summarization. In addition, higher education institutions should establish clear pedagogical guidelines to ensure the effective and responsible integration of AI in EFL learning contexts.
Investigating Research Skills and Artificial Intelligence Attitudes Among Residents in Surgical Medicine Departments
This research aims to examine the relationship between scientific research skills and attitudes towards artificial intelligence among residents in surgical medicine departments, considering gender and seniority. A quantitative research approach with a comparative correlational survey model was adopted; data was collected via online questionnaires from 127 surgical residents in training and research hospitals and university public hospitals in Ankara, Konya, and İzmir provinces. The findings revealed that participants' scientific research skills were at a moderate level, while their attitudes towards artificial intelligence were at a high level. Male participants showed significantly higher scientific research skills compared to their female peers, while no gender-based difference was found in attitudes towards artificial intelligence. As seniority increased, scientific research skills also increased significantly; however, attitudes towards artificial intelligence did not change with seniority. Regression analysis showed that attitudes towards artificial intelligence did not significantly predict scientific research skills. Based on these results, it is recommended to strengthen research methodology training in the early stages of specialist training, to investigate the causes of gender inequality using qualitative methods, to integrate artificial intelligence tools into the curriculum, and to expand the study to include a wider sample and different disciplines.
Flexible Learning as a Short-term Strategy for Instructional Continuity: Addressing Micro-Disruptions in Higher Education
Higher education continuity is frequently jeopardized by "micro-disruptions"—transient crises such as climate events, personal health issues, and logistical challenges—that extend beyond traditional classroom settings. While literature addresses long-term pandemic closures, a significant gap remains regarding short-term distance learning as a targeted intervention to maintain the academic schedule. This study evaluates the perceived relevance of online, offline, and blended learning modalities as short-term strategies for instructional continuity at Cebu Technological University Daanbantayan Campus across twelve specific challenging circumstances. Utilizing a quantitative descriptive-evaluative design, data were collected from 347 respondents, including 327 pre-service teachers and 20 faculty members, through a structured survey instrument grounded in CHED Memorandum Order No. 4, Series of 2020. Findings reveal a strategic "perception gap": students identified online learning (GM = 3.95) as the most adaptable mode for institutional disruptions, whereas faculty prioritized offline modular learning (GM = 4.01) as an indispensable "equity safety net" for financial and environmental recovery. Notably, an independent-samples t-test revealed no statistically significant difference between students' and teachers' perceptions of online learning (p = 0.1303), indicating shared institutional alignment regarding the utility of digital technologies. However, for mental health disruptions, students prioritized offline modalities as a restorative "analog" space to mitigate "Zoom fatigue," while teachers favored blended learning (M = 4.05) to maintain academic connection. The study concludes that distance learning must be reframed from an emergency exigency into a permanent, strategic framework. It is recommended that the institution formalize a "Precision-Application" policy that empowers faculty to transition strategically between modalities, ensuring that instructional delivery remains inclusive, resilient, and uninterrupted in the post-pandemic higher education landscape.
Transforming Higher Education: The Collaborative Potential of AI for Students and Faculty
Artificial Intelligence (AI) is rapidly transforming higher education by enhancing teaching, learning, and academic collaboration. This study examines the collaborative potential of AI in fostering effective interactions between students and faculty. Using a quantitative research design, data were collected from 100 purposively selected participants, including IT students and faculty members, through a structured online survey. The findings reveal a high level of AI adoption, with 82.5% of respondents utilizing AI-powered tools such as ChatGPT, content generation platforms, and collaboration systems. Results indicate that AI significantly improves communication, enables faster feedback, and supports personalized learning experiences, contributing to enhanced academic engagement. However, challenges such as limited training, privacy concerns, resistance to adoption, and accessibility issues remain significant barriers. Thematic analysis further highlights AI’s role in bridging communication gaps, promoting adaptive learning, and reshaping traditional educational dynamics into a more interactive and collaborative model. Despite ethical and technical concerns, participants expressed optimism regarding AI’s future integration in higher education. The study concludes that AI holds substantial potential to transform student-faculty collaboration, provided that institutions address issues related to digital literacy, infrastructure, and ethical governance.
Development of Artificial Intelligence-Based Interactive Learning Media to Improve Elementary School Students’ Civic Skills
This study aims to develop AI-based interactive learning media to improve students’ civic skills. The study was conducted in elementary schools in the Banyumanik Subdistrict, Semarang Regency. The research focused on fifth-grade elementary school students. The ADDIE model was employed in this study. Data were collected through observation, interviews, tests, questionnaires, and expert validation rating scales. The data were analyzed qualitatively and quantitatively. The qualitative analysis emphasized descriptive methods, while quantitative analysis employed statistical tests, including normality and homogeneity tests as prerequisites, followed by the Welch T-Test for hypothesis testing. The product was found to be feasible, with feasibility percentages of 71% for media, 85% for content, and 83% for language. It also demonstrated high practicality, with teacher and student practicality scores of 90.8% and 93%, respectively. The experimental group achieved higher mean posttest scores than the control group in both cognitive outcomes (89.02 vs. 81.51) and psychomotor outcomes (83.02 vs. 78.27). Welch’s t-test showed that these differences were statistically significant, with p < .001 for cognitive outcomes and p = .006 for psychomotor outcomes. These findings indicate that the AI-based interactive learning media is feasible, practical, and effective in improving elementary school students’ citizenship skills.
Mapping the Research Landscape of Computational Thinking Skills in Technical Education: A Bibliometric Analysis
Computational Thinking Skills (CTS) have become a key competency in technical and vocational education as digitalisation, automation, and Industry 4.0 continue to reshape workforce demands. Despite increasing scholarly attention, CTS research within technical education remains fragmented, with limited synthesis of publication trends, intellectual structures, and thematic focus. To address this gap, this study presents a bibliometric analysis of CTS research in technical education published between 2018 and 2025. Data were retrieved exclusively from the Scopus database, yielding 48 peer-reviewed articles after systematic screening. Data cleaning and harmonisation were conducted using OpenRefine and biblioMagika® to ensure consistency in bibliographic records. Bibliometric indicators and network analyses were then applied, with VOSviewer used to visualise co-authorship, citation, and keyword co-occurrence networks. The findings reveal a clear growth trend in CTS-related publications, particularly from 2023 onwards, indicating rising global interest in integrating computational thinking into technical and vocational learning. Thematic mapping highlights dominant research areas, including educational robotics, programming, STEM integration, and problem-solving pedagogies, reflecting the applied nature of CTS in technical education. However, the analysis also identifies key challenges, such as uneven regional research impact, limited standardisation of CTS assessment, and disparities in institutional readiness. Overall, this study provides a systematic overview of the CTS research landscape, offering insights to support curriculum development, professional training, and policy initiatives in technical education.
Artificial Intelligence-Supported English as a Foreign Language Learning: A Narrative Review of Personalisation, Feedback, Engagement and Pedagogical Collaboration
The rapid advancement of artificial intelligence (AI) has substantially reshaped educational practices substantially, particularly within English as a Foreign Language (EFL) learning contexts. This narrative review synthesises peer-reviewed research published between 2020 and 2025 to examine how AI supports EFL learners across six key dimensions: learning personalisation and adaptation; assessment and feedback; student engagement and motivation; study support and skills development; teacher–student–AI pedagogical collaboration; and critical challenges and ethical considerations. Drawing on recent high-quality peer-reviewed journal articles, the review employs a thematic synthesis approach to integrate diverse theoretical perspectives, empirical evidence and pedagogical insights. The findings indicate that AI-supported technologies can enhance EFL learning by enabling adaptive instruction, delivering timely and formative feedback, fostering learner engagement and motivation, and supporting self-regulated learning and language skill development. Simultaneously, the review underscores the importance of human-centric pedagogical design, highlighting that AI is most effective when positioned as a complement to, rather than a substitute for, teacher expertise and learner agency. Key challenges – including academic integrity, ethical concerns, data privacy, algorithmic bias, over-reliance on technology and inequitable access – also are identified as significant constraints on effective AI integration. Overall, this review offers an integrative and pedagogically grounded account of AI-supported EFL learning and poses implications for instructional practice, teacher education and future research aimed at promoting responsible, equitable and sustainable uses of AI in language education.
Generative AI Adoption in Higher Education: Aligning Academic Tasks, Technology, and Learning
Generative Artificial Intelligence (GenAI) is reshaping education by transforming teaching, learning, and assessment practices, making it vital to examine its effective integration within academic contexts. This study explores the adoption of GenAI tools and their impact on academic performance among ICT students at a private Australian higher education institute. Drawing on the Theory of Planned Behavior (TPB) and Task–Technology Fit (TTF) frameworks, it investigates how behavioural intentions, pedagogical factors, and technological characteristics collectively influence students’ use of GenAI and academic performance. A positivist, quantitative design was adopted, using an anonymous self-administered questionnaire completed by 235 students across Melbourne and Sydney campuses. Data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings highlight three insights. First, Perceived Behavioural Control emerged as the strongest predictor of students’ intention to adopt GenAI, highlighting that confidence and a sense of control are critical drivers of adoption. Attitudes also positively influenced intention, while social influence was weaker, underscoring the need to strengthen students’ self-efficacy and AI literacy. Second, TTF, shaped by pedagogical task variables and technological characteristics, directly affected both use and academic performance. Extending TTF to include learning outcomes and assessment methods demonstrates that curriculum design is central to technology-task alignment. Third, TTF emerged as the strongest driver of academic performance, exceeding both behavioural intention and actual use. Meaningful gains occur when GenAI is effectively aligned with tasks. By integrating TPB and TTF, this study provides a comprehensive framework for GenAI adoption and guidance for embedding AI into higher education.
Apprehensions of Integrating Artificial Intelligence into Higher Learning: A Systematic Review
The integration of artificial intelligence in higher learning has been studied and commented upon by several researchers. One thing that is obvious is that there are several authors who are hopeful that artificial intelligence has positive prospects for higher learning. On the other hand, there are also several authors who think that artificial intelligence is negatively affecting higher learning. While some authors report on hopes only and others on fears only, some others report both fears and hopes. This systematic review engages a total of 61 reports, including journal articles and grey literature, to address three issues. The first is about the extent to which researchers admit to the existence of negatives of AI in higher education. The second is about the factors that these researchers associate with the fears they admit. The third is a summarized identification of sources of this fear. Literature for this review was collected from various online sources, and the PRISMA 2020 flowchart was used for screening. Finally, content analysis was conducted using NVIVO computer software. Results show that, to a large extent, authors admit to the existence of various kinds of disadvantages in using AI in higher learning. These disadvantages lead to fears related to ethics and compromise established standards of higher education. Higher education policy makers and managers are, therefore, called upon to take necessary precautions in designing AI-supported systems and put in place reliable and effective guidelines for their use.
Emerging Trends in Virtual Simulation for Education: A Systematic Review of Technology–Pedagogy Alignment
Virtual simulation has become a transformative approach in education, particularly in contexts that require practical, skill-oriented learning. This systematic review examines emerging trends in virtual simulation for education by synthesizing evidence on educational technologies and instructional design approaches applied in virtual laboratory environments. Guided by the PRISMA protocol and the PSALSAR framework, 20 peer-reviewed journal articles published between 2020 and 2024 were systematically selected and analyzed. The results indicate a growing emphasis on virtual laboratories and immersive technologies, especially virtual and augmented reality, to support hands-on learning across science, engineering, medical, and vocational education. These technologies are predominantly implemented through learner-centered instructional designs, including experiential, inquiry-based, and constructivist approaches, which facilitate active engagement, repeated practice, and meaningful knowledge construction. While immersive and simulation-based technologies dominate current implementations, the integration of AI-supported and analytics-driven systems remains limited, suggesting an emerging area for future development. This review contributes a synthesized conceptual framework that illustrates how virtual simulation technologies are pedagogically operationalized through instructional design themes to produce meaningful learning outcomes. By integrating technological and pedagogical perspectives, the findings provide a structured foundation to inform evidence-based instructional design, implementation, and future research on virtual simulation–based education.
Analysis of the Influence of Utilizing STEM-Based Physics Learning Media: A Systematic Literature Review
21st-century education offers numerous benefits to society across various sectors, including the field of education itself. The STEM (Science, Technology, Engineering, and Mathematics) approach is designed to support teachers in realizing effective education in the 21st century. This study examines 40 articles published between 2018 and 2024, which have undergone a filtering stage based on inclusion criteria. The method used in this research is a literature review. The main objective of this study is to identify the variables that influence the use of STEM-based physics learning media. The most frequent publication years for the articles were 2021 and 2022. The academic level with the most prevalent use of STEM-based learning media is the high school level. The most dominant research method used in the analyzed articles is the quasi-experiment, as this method tends to be easier in evaluating student learning outcomes. The material most frequently taught using the STEM approach is electrical circuits. The most widely used learning media with the STEM approach is prototypes, as prototypes are easily accessible and can utilize existing school facilities. The most frequently used instrument in the research is tests, as the dominant focus of measurement is on students' conceptual understanding.
Cross-Age Peer Tutoring in Educational Robotics to Support Technological Literacy in Young Learners’ STEAM Education
Technological literacy is essential in a society where even the youngest children interact with devices and technologies. This study explores cross-age peer tutoring as an approach to support technological literacy in educational robotics (ER). The study focuses on the mediation practices employed by cross-age peer tutors (aged 11–12) when guiding younger tutees (aged 7–8). To examine these practices Science, Technology, Engineering, Arts, and Mathematics (STEAM) education, a multiple case study was conducted with six cross-age teams. These teams conducted ER task with Sphero Indi robot while tutors guided the actions. Video recordings of the actions were analyzed through micro-level analysis. The results highlight three mediation practices: tutor-directed, tutee-directed, and collaborative practices. These practices identify six distinct types of mediators among the tutors labelled: gentle mediators, persistent facilitator, strict mediators, linear instructors, humour-based mediators, and hesitating mediators. The results suggest that cross-age peer tutoring as a pedagogical practice can support technological literacy through joint verbal and non-verbal mediation and collaborative mediation practices. Recommendations for organizing tutoring to support technological literacy are provided.
Teachers' Perceptions of the Utilization of Artificial Intelligence (AI)-Based Learning Media in Elementary Schools
The perceptions of elementary school teachers are the main focus of this research, particularly regarding the use of Artificial Intelligence (AI) as a learning support medium in the classroom. This research involved two classroom teachers and used a qualitative approach through interviews, observations, and documentation to describe teachers' acceptance, understanding, and assessment of AI utilization. The research findings indicate that AI is primarily used for creating questions, student worksheets, and interactive media such as Wordwall, Canva, and learning videos. The teacher stated that AI helps speed up the process of preparing materials and increases student attention and engagement due to more appealing visual presentations. However, this study also found limitations such as limited technological facilities, a lack of shared devices, and the risk of student dependence if AI is used excessively. Additionally, differences in training experience affect teachers' understanding of AI usage, particularly in their ability to create appropriate prompts. Overall, AI is considered to provide significant benefits in terms of learning efficiency and quality, although it still requires wise management, improved teacher competency, and the provision of adequate supporting facilities for its optimal and sustainable utilization.
AI vs Human Writers: A Comparative Analysis of Story Rewriting through Readability Metrics
This study examines how generative artificial intelligence (GenAI) might be used to rewrite narratives in comparison to texts written by humans, with an emphasis on writing style, readability, and verbosity. GenAI's performance is evaluated by means of quantitative analysis and readability metrics. The results indicate that GenAI often generates writings with higher verbosity and readability scores than stories written by humans. Furthermore, the examination of lexical density and diversity reveals subtle variations in writing styles between human, ChatGPT, and Gemini; GenAI exhibits competitive performance in these metrics. Although the results point to potential applications of GenAI in narrative, more research is needed to determine how effective the technology is when compared to human authors.
AI-Enhanced Problem-Based Learning: Differential Effects of AI Usage Patterns on Core Competency Development Through Psychological Mechanisms
This study investigates how different levels of AI usage influence PBL core competencies through psychological mechanisms, establishing a theoretical framework that integrates Self-Determination Theory, Social Cognitive Theory, and Cognitive Load Theory. A cross-sectional survey design examined 412 university students across diverse academic disciplines. Participants completed validated instruments measuring AI usage patterns (assistive, dependent, substitutive), basic psychological need satisfaction, intrinsic motivation, self-efficacy, and five PBL core competencies (collaborative learning, autonomous learning, critical thinking, knowledge integration, communication skills). Structural equation modeling tested direct and mediated relationships. AI usage patterns demonstrated significant differential effects on PBL competencies. Assistive AI usage positively influenced all five core competencies, while substitutive AI usage negatively affected critical thinking abilities. Psychological mechanisms accounted for 64-73% of total effects through a sequential mediation pathway: basic need satisfaction → intrinsic motivation → self-efficacy → competency development. Competence need satisfaction emerged as the most critical mediator. The findings reveal that AI's educational impact depends critically on usage patterns and psychological mechanisms rather than technology presence alone. The study establishes theoretical foundations for AI-PBL implementation that prioritizes learner agency while leveraging technological affordances.
Exploring How Digital and Physical Tools Differently Influence Preservice Teachers’ Understanding
This study examines the effectiveness of using a digital tool (GeoGebra) and a physical tool (tracing paper) to support preservice teachers’ understanding of rotation. A quasi-experimental, pretest–posttest design was conducted with 17 preservice teachers, divided into two class groups – one using a digital tool and the other a physical tool intervention – at a state university in the United States. A content assessment was administered to all participants both before and after their participation in one of the interventions. Results from the Wilcoxon Signed-Rank test indicate significant improvement in preservice teachers’ relevant content knowledge after using either tool. No significant difference was found between the two intervention groups, suggesting that both digital and physical tools can provide comparable positive effects on key aspects of rotation. Further descriptive statistics revealed that these two tools helped learners understand rotation in varied yet complementary aspects. Therefore, it is promising to integrate both into instruction for more effective learning outcomes.
AfriML: A Culturally Relevant AI Tool for Teaching Machine Learning Concepts
This paper introduces AfriML, a culturally relevant artificial intelligence tool designed to enhance machine learning (ML) education through African cultural contexts. While tools like Teachable Machine and LearningML simplify ML concepts, they often lack cultural integration. AfriML addresses this gap by using artifacts from major Nigerian ethnic groups (Hausa, Igbo, Yoruba, Ibibio, and Efik) to teach ML classification problems. The tool was implemented with students at a tertiary institution and evaluated using a survey to assess learners’ confidence, satisfaction, and perceptions of cultural relevance. Results showed a significant increase in confidence scores from 2.43 to 3.28. Participants also reported improved understanding of ML classification (mean = 3.09) and rated the tool's effectiveness at 3.36. Cultural relevance was rated positively (mean = 3.45). These findings suggest that AfriML supports both conceptual understanding and cultural engagement in ML education. Future work will expand AfriML’s cultural scope and its impact across broader educational contexts.