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 IJTES is an international journal that welcomes original and unpublished research papers in the field of educational technology.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 All manuscripts submitted to IJTES must be original, unpublished, and not under consideration for publication elsewhere at the time of submission.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 Accordingly, manuscripts may not be withdrawn without the approval of the Editorial Office at any stage of the publication process, including peer review, revision, production after acceptance, or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 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.
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.
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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.
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.
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.
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.
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.
Experimentation and Potential for Generative AI Tools in Gateway Mathematics
With the aid of tools like computer algebra systems, routine mathematical calculations and symbolic manipulations can be quickly and accurately performed. In the advent of rapid growth of Generative AI tools, we examine how ChatGPT performs in solving a variety of mathematical problem-types and methods of representations in gateway mathematics courses. Our survey on the current use of ChatGPT among learners and experimentation with this generative AI tool in a learning environment, compels us as educators to examine how ChatGPT incorporated in teaching and learning, can be used to enhance acquisition of factual, procedural and conceptual knowledge in gateway mathematics. Applying ChatGPT in mathematical discourse elicits some potential issues with both discipline-specific and general instructional practices while enhancing benefits associated with elements of High Impact Practices (HIPs). We focus on discipline-specific instructional practices that are characterized by problem-solving, problem-posing, open-ended questions with multiple solutions, modeling project activities, and technology integration. When applied to a class learning environment, the question of the quality of the ChatGPT output and what can be offloaded to ChatGPT becomes important to both learners and educators. Our analysis of the students’ survey and the experimentation with ChatGPT shows that using mathematical problem-based tasks, with instructor-curated content and learners equipped with refined prompts enriches the quality of gateway mathematics education in AI environments.
Critical Factors That Shape the Pedagogical Integration of ICT in Technology-Rich Senior High Schools
The common belief is that providing technological tools and equipment leads to the pedagogical integration of ICT in schools. This study tested this idea by examining key factors and contextual barriers that influence how ICT is integrated pedagogically in technology-rich Senior High Schools in Ghana. Using a cross-sectional survey design, data were collected from 103 teachers in both public and private SHSs with modern ICT infrastructure. The study investigated important factors such as learning styles and needs, teacher motivation and incentives, teachers’ perceived ease of use of technology, and ICT infrastructure. A multiple regression analysis revealed that teacher motivation/incentives and ICT infrastructure are significant predictors of the level of pedagogical ICT integration. These factors together explain 59.1% of the variation in ICT integration, while perceived ease of use and learning styles/needs were not significant predictors. Despite the availability of ICT resources, teachers highlighted contextual challenges, including a lack of technical skills, misalignment of technology with curriculum goals, difficulties in shifting to interactive learning methods, and promoting digital citizenship. These findings demonstrate that resources alone are insufficient; addressing pedagogical and systemic barriers is essential for meaningful ICT integration in SHSs. The implementation of sustainable technology integration in schools requires establishing policies which promote ICT-enhanced teaching methods, professional development and teacher motivation.
Integrating Web 2.0 Tools into Science Teaching: Implications for Conceptual Understanding and Student Perspectives
The aim of this study is to develop a guiding material supported by Web 2.0 tools for the “Conduction of Electricity” unit in the 6th grade science course and to examine conceptual learning and student opinions about the process. The action research method, one of the qualitative research designs, was used in the study. The sample of the study consisted of 21 6th grade students. A Conceptual Understanding Test consisting of 15 open-ended questions developed by the researcher and a semi-structured interview consisting of 5 open-ended questions were used as data collection tools. According to the results of the Wilcoxon signed-rank test conducted on the conceptual understanding pre- and post-test scores, a statistically significant difference was obtained (z=-4.02, p<.05). After further analysis of the conceptual understanding test, it was determined that students made significant progress from the "No Understanding" level to the "Complete Understanding" level on the basis of all acquisitions; interactive and student-centered teaching practices supported by Web 2.0 tools strengthened conceptual understanding. In addition, student opinions reveal that Web 2.0 tools contribute positively not only to academic achievement but also to students' attitudes towards learning, class participation, digital competence, and interdisciplinary skills.
Online Learning Community: University English Teachers’ Engagement in Using Facebook Page
In addition to providing a wide range of information, Facebook has also been used as a significant learning platform in various educational contexts. This article examines the experiences of University English Teachers (UETs) in Nepal who engaged in a Facebook page as an Online Learning Community (OLC). A four-week intervention was conducted among 19 UETs, who shared professional development content and related activities on the page. Qualitative data were collected through online semi-structured interviews with 11 UETs and were analyzed thematically based on interpretative phenomenological analysis. The findings revealed that teachers initially had limited awareness of the concept of OLC; they gradually identified it as a valuable space for sharing experiences, solving professional challenges, and developing collaborative skills. Their engagement increased on the page through activities such as following shared content, commenting, and uploading their learning materials. UETs shared mixed experiences on the Facebook page's effectiveness for academic discussion and professional development. Moreover, they also expressed challenges related to technical devices and skills for active engagement in the OLC. The study suggests that professional online social communities can enhance English teachers' professional knowledge and collaborative skills.
The Role of Mobile Technology in Language Learning: A Study on University Students
This study examines the influence of Mobile-Assisted Language Learning (MALL), a branch of mobile learning (M-learning), on language acquisition among university students in Azerbaijan. As technology becomes increasingly embedded in higher education, the research investigates the extent to which mobile applications and platforms enhance language skills, with a particular focus on student engagement and learning outcomes. Quantitative data were collected through online surveys completed by Azerbaijani university students, capturing both the benefits and limitations of using mobile learning tools. Positive feedback emphasized gains in listening skills, vocabulary expansion, and learning flexibility. Conversely, negative experiences highlighted issues such as limited content variety, insufficient opportunities for conversational practice, and distractions caused by advertisements and notifications. The results suggest that MALL can be highly effective in supporting language learning when combined with engaging, interactive, and well-structured content. Nonetheless, challenges related to repetitive tasks, technical errors, and content design must be addressed to optimize its educational potential and ensure a more sustainable and productive learning experience.
Talking Technology Tutors: The Perceptions of Conversational AI in Education through the Eyes of Parents and Teachers Worldwide
As AI becomes an increasingly prevalent tool across society, conversations have begun on whether it should be implemented into education. Teachers and parents are at the forefront of this dilemma, their opinions being an integral part of the ongoing discussion. This study explores parents’ and teachers’ perspectives on the uses of conversational AI in education through an exploratory survey. Survey results (111 parents, 109 teachers) showed that the majority of parents and teachers believed conversational AI could support their child’s/students’ development, they were likely to use conversational AI for their child/student, and were comfortable with their child/students using it. In addition, there was a moderate amount of apprehension from both parents and teachers toward AI in education to due privacy, plagiarism and lack human contact concerns. Overall, the findings suggest that while there is enthusiasm for integrating AI into education, concerns about its implications need to be carefully addressed.
The Use of Artificial Intelligence in Early Intervention
The use of Artificial Intelligence (AI) is steadily increasing. This paper explores the extent to which AI is currently utilized in Early Childhood Intervention (ECI) services. Data were collected through an online survey conducted in German-speaking countries (Germany, Austria, Switzerland) and Turkey (n = 123). Results indicate that up to 50% of professionals in ECI services already use AI, predominantly ChatGPT. AI use is associated with younger age and professional background. Home visitors report less frequent use compared to professionals working in kindergarten settings. Non-users primarily cite a lack of information and general skepticism toward AI tools. Among users, AI is applied in methodological research, translation processes, and, to some extent, in planning interventions. Time efficiency is perceived as the main advantage of AI use; however, concerns remain regarding the validity of information and data protection. Overall, there is a clear demand for more training and well-defined guidelines concerning the handling of personal data when using AI in professional practice.
Impact of Artificial Intelligence in Academia: Navigating the Perceptions and Usage of ChatGPT among University Students
This research delves into the perceptions, usage behaviors, and experiences of ChatGPT among students, encompassing both undergraduates and postgraduates, enrolled at a public university. Employing a convenient sampling technique, we collected responses from 255 participants via a structured questionnaire. The collected data underwent analysis through descriptive statistics and correlation assessments. The results of this investigation illuminate a prevalent positive perception of ChatGPT across all dimensions explored. Most notably, our study found no significant relationship between students' educational levels (undergraduate or postgraduate) and their perceptions, utilization patterns, or experiences with ChatGPT. These findings underscore the widespread acceptance and utility of ChatGPT within the academic sphere, transcending educational distinctions and reaffirming its value as a versatile tool for a diverse range of university students. This research contributes to the growing body of knowledge surrounding the reception and integration of AI-driven language models, such as ChatGPT, within the educational landscape.
Trust in ChatGPT and Perceived Academic Writing Improvement: A TAM-based Quantitative Study in a ESL Context
This study examines the impact of undergraduate students' trust in ChatGPT on their perceived improvement in academic writing and their intention to utilize the tool in future writing tasks. Grounded in the Technology Acceptance Model (TAM) and the Trust in Technology Framework, the study employs a quantitative approach to examine student perceptions within a Functional English course at a public-sector university in Pakistan. A total of 225 students from the Telecommunication Engineering, Computer Science, and Chemistry departments participated in a structured survey. Descriptive statistics, Pearson's correlation, and multiple regression analysis revealed that trust in ChatGPT significantly correlates with perceived improvements in clarity, vocabulary, and organization. Moreover, overall trust and Acceptance were strong predictors of students' future intent to use ChatGPT. The findings suggest that students' confidence in AI feedback enhances their writing development, underscoring the importance of institutional support and the ethical integration of AI. This study contributes to the growing body of research on generative AI in education by providing localized insights from a non-Western English as a Second Language (ESL) context and recommending pedagogically sound strategies for responsible AI adoption.