International Journal of Technology Enhanced Learning (28 papers in press)

Regular Issues

• The influence of educational virtual reality on the visual perception skills of architecture students  
  by Flora Fakourian, Mehdi Khakzand, Esra Kurul, Mehdi Hamzenejad 
  Abstract: This study explores the educational potential of virtual reality (VR) as a transformative tool in architectural pedagogy, focusing on its impact on the visual perception skills of architecture students in Iran. Despite the increasing global interest in immersive technologies, their application in design education remains limited and largely experimental. Using a quasi-experimental pre-testpost-test design, 90 undergraduate architecture students were assigned to experimental and control groups. The experimental group participated in VR-based learning sessions integrated into a basic design studio, while the control group received conventional instruction. A validated visual perception assessment was employed to measure outcomes across domains such as spatial cognition, construction drawing interpretation, and three-dimensional visualisation. Statistical analyses using SPSS and AMOS 26 revealed a significant improvement in the visual perception scores of the experimental group. The findings emphasise the effectiveness of VR in strengthening cognitive skills critical to architectural design, suggesting a shift toward more immersive and perception-based learning environments. This study contributes to the evolving discourse on digital innovation in architectural education, presenting empirical evidence that supports the structured integration of VR into design curricula. Future cross-regional studies are recommended to better understand the broader educational implications of this emerging technology.
  Keywords: educational virtual reality; visual perception; architecture students; architectural design education; experiential learning.
  DOI: 10.1504/IJTEL.2025.10074560
   
  
• A digital intervention to promote phonological awareness in Arabic-speaking beginning readers: a case study  
  by Naila Tallas-Mahajna, Najla Khateeb 
  Abstract: This study investigates the impact of interactive digital tools on enhancing phonological awareness and decoding skills among Arabic-speaking first-grade students identified with early reading difficulties. Given Arabics morphophonemic complexity, diglossic nature, and visually confusable consonants, beginning readers often face unique linguistic challenges that hinder decoding and fluency. A quasi-experimental design was employed with 30 students divided into two groups: an experimental group receiving a seven-session digital intervention and a control group receiving traditional phonological instruction. The digital tools incorporated multimodal features visual, auditory, and kinaesthetic designed to reinforce phoneme-grapheme mapping, sound discrimination, and syllable segmentation. Pre- and post-tests were administered using a standardised diagnostic tool developed by Israels National Authority for Measurement and Evaluation in Education (RAMA). Statistical analyses revealed significant improvements in the experimental group compared to the control group across both phonological awareness and consonant-sound correspondence (p < 0.01). Findings demonstrate the pedagogical value of culturally responsive digital tools in supporting Arabic literacy acquisition. The study highlights the potential of educational technology to scaffold early reading development in linguistically diverse contexts and calls for its thoughtful integration into primary curricula alongside teacher-led support.
  Keywords: phonological awareness; digital tools; Arabic literacy; interactive learning; beginning readers; educational technology.
  DOI: 10.1504/IJTEL.2025.10075146
   
  
• A study on the design and development of an educational tablet: mPAD4  
  by Sacip Toker, Mehtap Tufan 
  Abstract: This study describes the design, development, and integration of an educational tablet, mPAD4, led by the Basic English Department of a private universitys Prep School. The project aimed to introduce an innovative learning model using digital, interactive, and mobile elements such as information systems, e-books, and educational applications. The initiative stemmed from inequitable access to printed textbooks, which hindered student learning. Existing tablets did not fully address educational needs, prompting the development of mPAD4 with enhanced specifications and lower cost. A pilot study compared students using mPAD4 with e-textbooks against those using printed materials. While midterm, quiz, and writing scores showed no significant differences, students using mPAD4 achieved higher homework results. Following the pilot, incoming Prep School students adopted the device for classroom use. The project highlights the potential of custom-designed tablets to improve equity, support mobile learning, and strengthen language education, with recommendations for future research.
  Keywords: mobile learning; educational tablet; mPAD4; e-textbooks; design-based research; quasi-experimental research.
  DOI: 10.1504/IJTEL.2025.10075147
   
  
• Modeling the effects of digital competence, technology accessibility, and technology use on learning motivation and physics achievement  
  by Muhammad Aizri Fadillah, Usmeldi Usmeldi, Desnita Desnita, Asrizal Asrizal 
  Abstract: This study investigates the relationships among digital competence, technology accessibility, technology use in learning, learning motivation, and students academic achievement in physics. Data were collected from 545 students and analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM). The findings reveal that digital competence has a positive effect on academic achievement but shows no significant influence on learning motivation. Technology accessibility enhances learning motivation, yet its direct effect on academic achievement and digital competence is negligible. In contrast, technology use in learning positively contributes to both learning motivation and academic achievement while also strengthening digital competence. However, learning motivation neither significantly predicts academic achievement nor mediates the effects of the digital and technological factors on it. These results provide valuable insights for advancing technology-based physics education by identifying the digital and motivational dimensions that most effectively support students academic success.
  Keywords: digital competence; technology accessibility; technology use in learning; learning motivation; physics academic achievement.
  DOI: 10.1504/IJTEL.2026.10075639
   
  
• Measuring the elusive student attention: an innovative approach by V7 Labs  
  by Leila Rawashdeh, Adiy Tweissi, Shafig Al-Haddad, Baker Alserhan 
  Abstract: Although student attention is key to improved learning, measuring this attention manually has proved problematic in terms of scale and accuracy, which limits the measurements usability as a planning tool to enhance understanding. Automating the measurement process and measuring attention through visible behavioural cues increases accuracy and enables broader-scale attention analysis, possibly measuring attention across an entire college or school campus, including multiple classes, periods, courses, and dates. This study aims to test the ability of AI-assisted tools to identify attention and non-attention cases using V7 Labs. A total of 90,000 frames from one class were annotated and analysed. The results showed an attention percentage of 68.5% out of the total number of instances (frames) identified. The researchers aimed to demonstrate that, given high-quality videotapes, students attention could be measured satisfactorily using V7 Labs. Validating the results through more classes is recommended. Once validation is complete, live measurement and feedback could enable a more adaptable learning environment.
  Keywords: student attention; V7 Labs; AI-assisted measurement; classroom engagement; AI-assisted learning; classroom analytics; behavioural cues; video frame analysis; educational technology; real-time feedback.
  DOI: 10.1504/IJTEL.2026.10075640
   
  
• The AI-teacher dichotomy: an active or passive substitute?  
  by Elnaz Rashtizadeh, Maryam Ganjkhani, Sepideh Norouzi, Maryam Hojjati Siyamakani 
  Abstract: This study examined the effectiveness of three teaching approaches, traditional, laboratory, and artificial intelligence (AI), on the quality of learning and academic motivation of students in chemistry concepts. This quasi-experimental study was conducted by conducting a pre-test/post-test. The statistical population was eighth-grade students and a random sample consisted of three classes of 20 eighth-grade students. The findings showed the laboratory method was more effective, especially for concrete concepts, due to interaction and practical participation. In contrast, for abstract topics such as atomic structure, multimedia based on AI was more effective than laboratory and traditional approaches. Academic motivation was assessed using the Harter academic motivation questionnaire. The AI generated the most motivation. The reason for this is probably the novelty and attractiveness of the AI educational videos. Overall, integrating AI technologies with interactive and hands-on methods can provide a highly effective and motivating learning experience for students.
  Keywords: artificial intelligence; laboratory instruction; motivation; learning strategies; digital videos; multimedia.
  DOI: 10.1504/IJTEL.2026.10076053
   
  
• Learning analytics in higher education: insights from a structural equation modelling analysis of UTAUT-2  
  by V.P. Joshith, S. Asha 
  Abstract: Technology integration in education has generated vast datasets, leading to the rise of learning analytics (LA) as a crucial tool for understanding learners and their environments. This study, focusing on higher education, assesses and validates LA technology using the UTAUT-2 model. The model is extended with a learning insights component to align with the student-facing perspective. Survey data from a predominantly female Gen Z cohort (N = 540; Female = 392, Male = 148) in Indian universities and colleges were visualised using structural equation modelling (SEM), resulting in a well-fitted model with nine variables and thirty-two indicators. The findings highlight the need for enhanced digital literacy, with educational institutions encouraged to integrate LA-related tools into their curricula and to provide professional development for instructors. The study concludes that while LA adoption is still nascent, targeted interventions both at the curriculum and faculty levels could significantly boost its uptake. Future research should explore gender-specific patterns in LA adoption, refine the UTAUT-2 model to better capture the complexity of students decision-making, and further investigate the role of learning insights in shaping technology use in education.
  Keywords: unified theory of acceptance and use of technology; UTAUT2; learning analytics; LA; technology integration; technology adoption survey; higher education; structural equation modeling; SEM; learning insights; LI.
  DOI: 10.1504/IJTEL.2026.10076325
   
  
• Action research on the use of generative AI in quiz preparation: insights from an optics laboratory course  
  by Seyma Yurusoy, Riza Salar, Yaren Gungor 
  Abstract: This study investigates the role of generative artificial intelligence (AI) in supporting quiz preparation among pre-service science teachers enrolled in an optics laboratory course. Using an action research design, the study was conducted over ten weeks with 52 participants working in self-selected groups. Students used a generative AI tool to generate potential quiz questions prior to class and compared them with instructor-designed questions. Data were collected through weekly reflections and semi-structured interviews and analysed using thematic analysis. Findings indicate that AI use enhanced accessibility, information retrieval, and student engagement, particularly in generating theoretically accurate and formula-based questions. However, AI tools showed limitations in handling course-specific visuals, mathematical reasoning accuracy, and question complexity. The results emphasise the importance of AI literacy and pedagogical guidance for effective integration. Overall, generative AI can serve as a complementary study resource when supported by instructional oversight.
  Keywords: generative artificial intelligence; quiz preparation; AI-assisted learning; pre-service science teachers.
  DOI: 10.1504/IJTEL.2026.10076498
   
  
• Blended learning in engineering education: enhancing student performance through hybrid models  
  by Julio Pérez-Sánchez, Héctor R. Díaz-Ojeda 
  Abstract: Higher education has undergone significant changes due to digital technologies and blended learning, although engineering degrees still rely heavily on traditional lecture-based teaching. However, industry increasingly demands practical skills and software proficiency. This study compares three instructional approaches in a naval design course: traditional lectures (LM), short video-based learning (SVM), and hybrid learning (HLM). Results show that the hybrid model, combining lectures with short video reinforcements, achieves the highest average performance (7.43/10) and the most consistent outcomes in both theoretical and practical assessments. While SVM supports autonomous learning, it leads to greater variability and weaker practical results. LM demonstrates moderate effectiveness, with consistent but lower overall performance. Additionally, 95% of students reported improved understanding with HLM, and many noted enhanced self-directed learning. Overall, hybrid learning appears especially effective for technical subjects requiring both conceptual knowledge and practical skills.
  Keywords: blended learning; engineering education; computer-aided design; learning models; academic performance.
  DOI: 10.1504/IJTEL.2026.10077315
   
  
• Adopting artificial intelligence in Ethiopian higher education: the role of digital readiness, risk and trust  
  by Merih Welay Welesilassie, Berhane Gerenchael 
  Abstract: Understanding educators readiness to integrate artificial intelligence (AI) tools into their teaching necessitates an exploration of the factors influencing this adoption. Given AIs capacity to enhance instructional effectiveness, this study evaluated factors affecting teachers adoption of AI tools by extending the Technology Acceptance Model (TAM) to include digital readiness support, perceived risk, and trust. A cross-sectional quantitative survey involving 128 teachers unveiled significant determinants of AI adoption. Key influences included digital readiness support, perceived ease of use, perceived usefulness, perceived risk, and trust. It was found that digital readiness support positively impacted ease of use, usefulness and trust while mitigating safety and privacy concerns. While perceived ease of use increased perceived usefulness, it adversely affected trust. Conversely, perceived usefulness reinforced trust. Ultimately, Safety and privacy risks reduced trust, limiting educators willingness to adopt AI tools. These findings offer valuable guidance for educators and policymakers.
  Keywords: digital readiness support; AI acceptance; perceived risk; AI trust; technology acceptance model; TAM.
  DOI: 10.1504/IJTEL.2026.10077407
   
  
• Shedding light on the question of “who should be the mentor in flipped classrooms to maximise individual contribution ”: The critical role of transactive memory  
  by Ömer Demir, Tayfun Akin, Sinan Keskin 
  Abstract: This study was designed to examine how intrinsic goal orientation affects individual contribution/outcome and to identify the mediating role of transactive memory in this causal relationship. Moreover, the moderating role of teacher and peer mentors in the relationship between transactive memory and individual contribution was examined. Some 175 university students were randomly assigned to the experimental group. The results of the study showed the partial mediation of transactive memory in the relationship between intrinsic goal orientation and individual contribution. Besides, teacher mentoring was found to create a stronger causal effect between transactive memory and individual contribution. The reasons for and implications of these findings were discussed.
  Keywords: classroom mentor; individual contribution and outcome; ICO; intrinsic goal orientation; IGO; motivation; transactive memory.
  DOI: 10.1504/IJTEL.2026.10077479
   
  
• Factors influencing continuance intention towards eLearning: a TAM and ECM approach in higher education  
  by Rasha Talib Ahmed Alnaqeeb, Nor Athiyah Abdullah, Nasuha Lee Abdullah 
  Abstract: The study investigates the factors influencing user satisfaction and the desire to continue using e-learning systems in Iraqi higher education by integrating the technology acceptance model (TAM) and expectancy-confirmation model (ECM). Perceived ease of use (PEOU), perceived usefulness (PU), digital literacy, satisfaction, continue intention, and confirmation of expectations are all examines in the study. Partial least squares structural equation modelling (PLS-SEM) was used to anaysed 135 respondent collected from different university students using a quantitative research approach. The results demonstrate that PEOU and digital literacy have a significant and positive impact on satisfaction, emphasising the importance of usability and digital competence in creating e-learning environments. Continuance intention was found to become one major mediator as satisfaction. PU results pointed to a less positive effect on satisfaction, its influence is dominated by the necessity of digital literacy and ease of use. Likewise, confirmation supported PU that expectations and perceived value coincided. These findings advance existing theoretical frameworks, particularly by emphasising the incorporation of contextual aspects such as digital literacy into e-learning systems. The study emphasises the need of institutions focusing on usability improvement, digital competency training, and matching user expectations in order to maintain continued participation in Iraq.
  Keywords: technology acceptance model; TAM; expectation-confirmation model; ECM; E-learning continuance intention; Iraqi higher education and digital literacy.
  DOI: 10.1504/IJTEL.2026.10077774
   
  
• Integrating artificial intelligence in mathematical education: a techno-pedagogical assessment in India  
  by Shruthi Patricia Dsouza, C.H. Padmanabha 
  Abstract: The growing integration of artificial intelligence (AI) in K-12 education presents new opportunities for enhancing mathematics learning. This study explored the relationship between technology self-efficacy (TSE) and perceived usefulness of AI tools (PUAI) with critical cognitive and academic outcomes among school students, specifically attitude for learning (ATL), working memory (WRM), task accuracy (TSA), and mathematical competency (MTC). The research was grounded in the technology acceptance model and cognitive learning theory. Using a quantitative design, data (n = 250) were collected via a structured questionnaire and analysed through structural equation modelling (SEM). The study offers practical implications for educators and policymakers to design interventions that strengthen students technological confidence and AI tool engagement, ultimately promoting deeper learning and academic success in mathematics.
  Keywords: mathematical competency; technology self-efficacy; perceived usefulness; cognitive engagement; learning motivation; India.
  DOI: 10.1504/IJTEL.2026.10077807
   
  
• Motivation in computer science education: insights from Greek Lyceum students  
  by Sofia Kasotaki, Nikolaos Dimokas 
  Abstract: This research investigates the motivational factors that shape Greek Lyceum students decisions to pursue computer science, while focusing on gender differences. Established theoretical frameworks, including self-determination theory (Deci and Ryan, 2000), expectancy-value theory (Eccles and Wigfield, 2002), and self-efficacy theory (Bandura, 1997), offer valuable foundations for understanding and fostering students motivation to learn within the field of computer science education. Drawing on these theories, this research examines how various motivational factors - including personal interest, perceived competence, autonomous regulation, achievement goals, and career aspirations - shape students academic choices. An adapted version of the science motivation questionnaire II (SMQ-II) (Glynn et al., 2011) was administered to 157 students, and statistical analyses were conducted to examine psychometric properties, gender-based differences, and motivational disparities between students who intend to pursue computer science studies and those opting for other fields. The findings indicate significantly higher levels of motivation across all five factors for students opting for computer science as well as minimal gender disparities within this group. The findings point toward a possibly positive shift toward inclusivity in terms of gender within computer science education and highlight the need for targeted and motivation-enhancing secondary educational interventions.
  Keywords: computer science education; student motivation; self-determination theory; SDT; expectancy-value theory; EVT; self-efficacy; gender differences; secondary education.
  DOI: 10.1504/IJTEL.2026.10078207
   
  
• Associations between the use of AI generative writing and reduced levels of stress related to academic tasks and academic self-concept  
  by Dalya Khayat 
  Abstract: This research investigates how generative AI writing tool usage and familiarity influence academic stress and self-concept among students and faculty in Saudi Arabian higher education. Utilising a quantitative design with 220 participants, data were analysed via partial least squares structural equation modelling (PLS-SEM). Findings reveal that AI tool usage and familiarity significantly correlate with reduced academic stress and enhanced academic self-concept. Task engagement was found to mediate these effects, while individual AI competency acted as a significant moderator, further diminishing stress and boosting academic optimism. The study underscores the necessity of integrating generative AI into curricula while prioritising the development of student engagement and technical competency. These results offer a novel perspective on the psychological impacts of AI, providing actionable insights for policymakers to modify strategies and maximise student outcomes in digital learning environments.
  Keywords: generative AI; academic stress; self-concept; task engagement; AI competency.
  DOI: 10.1504/IJTEL.2026.10078347
   
  
• KashEnsem-R: a transformer-enhanced stacked ensemble framework for sentiment classification in Kashmiri  
  by Sheezan Farooq, Rumaan Bashir 
  Abstract: Despite significant progress in multilingual sentiment analysis, the Kashmiri language remains underexplored due to the lack of labelled datasets and pre-trained linguistic resources. This paper proposes KashEnsem-R, a transformer-enhanced stacked ensemble framework for Kashmiri sentiment classification. The model integrates support vector machine (SVM), bidirectional long short-term memory (BiLSTM), and XLM-RoBERTa (XLM-R) as base learners, with logistic regression as the meta-classifier. Experiments were conducted on a manually curated and balanced Kashmiri sentiment dataset. KashEnsem-R achieved an accuracy and macro-F1 score of 0.952, outperforming individual models including SVM (0.942), BiLSTM (0.934) and XLM-RoBERTa (0.937). A real-time usability evaluation based on model size, parameter count, and inference latency (37.01 ms per sample) demonstrated practical deployment feasibility. Qualitative analysis further confirmed the frameworks robustness in capturing lexical and contextual nuances, establishing KashEnsem-R as a strong benchmark for low-resource sentiment analysis.
  Keywords: sentiment analysis; Kashmiri language; stacked ensemble learning; transformer models; bidirectional long short-term memory; BiLSTM; real-time usability.
  DOI: 10.1504/IJTEL.2026.10078348
   
  
• The role of TPACK in reimagining art education: prospective art teachers’ perspectives on digital technologies as catalysts for creative development  
  by Masoumeh Shiri, Aidar Kuzdeubayev, Aidyn Kozhagulov, Zhazira Stambekova, Rakhat Berikbol 
  Abstract: This study investigates the role of technological pedagogical content knowledge (TPACK) in art education by examining prospective art teachers perceptions of digital technologies for creative development. Using a mixed-methods explanatory design, data were collected from 104 undergraduate art teacher candidates from three universities in Kazakhstan through a structured TPACK-based questionnaire. Confirmatory factor analysis and structural equation modelling showed strong relationships among TPK, TCK, and the overall TPACK construct, while lower scores in technological knowledge (TK) and content knowledge (CK) indicated areas needing improvement. To contextualise these findings, participants completed a two-week digital poster project titled A World Without War. Analysis of the artworks revealed limited originality, overreliance on digital tools, and pedagogical gaps, highlighting a mismatch between perceived technological competence and actual creative output. The findings emphasise the need for curriculum approaches that move beyond tool-focused training to foster critical thinking and creativity.
  Keywords: technological pedagogical content knowledge; TPACK; digital technologies; art education; creative development; technological knowledge.
  DOI: 10.1504/IJTEL.2026.10078349
   
  
• Faculty adoption of generative AI in higher education: evidence from ChatGPT use in an emerging market  
  by Jusuf Zeqiri, Sedat Mahmudi, Miranda Sabriu Bexheti 
  Abstract: This study explores how university faculty members utilise ChatGPT in their professional activities. To accomplish this objective, a comprehensive survey was conducted with faculty members within the higher education setting in North Macedonia. A sample of 151 responses was collected to examine ChatGPT usage in their daily life activities from April to July 2024. The data was analysed using partial least squares structural equation modelling (PLS-SEM). Additionally, multiple regression analyses with structural equation modelling (SEM) were used to test independent and dependent variables. Results reveal that of the DoI factors, perceived compatibility, perceived observability, and perceived relative advantage significantly and positively impacted the intention to adopt ChatGPT among faculty members. On the contrary, perceived complexity and perceived trialability did not seem to impact intention to adopt this innovative technology among faculty members.
  Keywords: ChatGPT; educational technology; faculty research; innovation in education; faculty uses of ChatGPT.
  DOI: 10.1504/IJTEL.2026.10078413
   
  
• Predicting student risk in higher education using machine learning and institutional data analytics: a Southeastern European perspective  
  by Burim Ismaili, Adrian Besimi, Zamir Dika 
  Abstract: This research investigates the application of machine learning techniques to identify students at academic risk using institutional data from South East European University (SEEU). The dataset consists of approximately 3,000 student records collected across five academic years, including variables related to academic performance, engagement, and socio-economic background. Several supervised learning models were implemented and compared, such as random forest, LightGBM, decision trees, neural networks, K-nearest neighbours, and logistic regression. The findings indicate that ensemble-based approaches achieved the highest predictive performance, with Random forest reaching an accuracy of 0.90 in detecting at-risk students. Feature importance analysis reveals that attendance rate, GPA, and financial stress are the most influential predictors. The results highlight the potential of combining machine learning with institutional data analytics to support early warning systems and improve decision-making processes in higher education institutions.
  Keywords: at-risk students; higher education; student performance; machine learning; data analytics; predictive models; academic analytics; early warning systems; root cause; educational data mining; EDM; learning analytics; classification models; risk prediction.
  DOI: 10.1504/IJTEL.2026.10079166
   
  
• ChatGPT in primary school within the TPACK framework: analyse transformation in science and mathematics lessons and teacher opinions  
  by Hilal Uğraş, Oğuzhan Toker, Mustafa Uğraş, Georgios Zacharis, Michail Kalogiannakis 
  Abstract: This study investigates primary school teachers perspectives on integrating ChatGPT into science and mathematics education for 3rd- and 4th-grade students. Using a qualitative case study design, 16 teachers were purposively selected to participate in a 12-hour professional development program grounded in the TPACK framework. Data gathered from semi-structured interviews and researcher diaries were analysed through inductive content analysis. The findings reveal predominantly positive attitudes toward ChatGPT, which is perceived as enriching instructional delivery, facilitating individualised learning, and enhancing student engagement. Nevertheless, significant concerns were raised concerning misinformation risks, potential erosion of social skills, and technological dependency. The study concludes that comprehensive AI training for educators and stakeholders is essential to mitigate risks and foster the ethical application of generative AI in primary STEM education.
  Keywords: artificial intelligence; AI; ChatGPT; GenAI; TPACK; science and mathematics instruction; primary education.
  DOI: 10.1504/IJTEL.2026.10079477
   
  
• A trait-aware Markov decision framework for context-sensitive and ethical intervention in academic procrastination  
  by Wafa Louafi, Houda Tadjer, Yacine Lafifi 
  Abstract: Academic procrastination remains a major challenge in technology-enhanced learning environments, where automated interventions often rely solely on behavioural data and overlook underlying psychological factors. This paper proposes an interpretable adaptive intervention framework that integrates eight psychological traits with four dynamic behavioural indicators through a theory-informed weighted fusion mechanism structured by a sparse interaction matrix. The intervention process is modelled as a contextual Markov decision process (MDP) with an adaptive priority parameter that balances academic performance and learner well-being. Simulation experiments on 150 synthetic learners across an academic semester show significant improvements over a static baseline, including reduced task initiation delay (34.9%), improved session continuity (36.2%), lower peak stress (39.7%), and higher task completion (27.3%). The results demonstrate that explicit trait-behaviour integration enables more personalised, psychologically grounded, and interpretable adaptive learning support systems.
  Keywords: academic procrastination; dual-channel state representation; Markov decision process; MDP; explainable AI; behavioural modelling; learning analytics; adaptive intervention.
  DOI: 10.1504/IJTEL.2026.10079674
   
  
• The impact of teacher attitudes and government support on fostering sustainable learning in Saudi higher education through AI implementation  
  by Yahya Nassar, Shahla Abuzahra, Asmaa Mohammed M. Abduldaem, Abeer Siraj Abdullah Akkad, Rana Saud Abdulaziz Shahini 
  Abstract: This study examined the effects of artificial intelligence (AI) implementation and technology use on sustainable learning practices and student learning outcomes in Saudi higher education. Teacher attitudes toward technology were investigated as a mediating variable, while government support was examined as a moderating factor. Using a quantitative research design, data were collected from 250 students, faculty members, and administrators through an online questionnaire. The proposed model was analysed using partial least squares structural equation modelling (PLS-SEM) with SmartPLS. The findings revealed that AI integration and technology use significantly and positively influenced both sustainable learning practices and student learning outcomes. Teacher attitudes toward technology partially mediated these relationships, highlighting the importance of educator readiness for successful AI adoption. Government support significantly strengthened the effects of AI and technology use through training, infrastructure, and policy initiatives. The study contributes to the literature on AI-enabled educational sustainability and provides practical implications for higher education institutions and policymakers seeking to enhance learning outcomes through technology supported innovation.
  Keywords: AI integration; technology adoption; sustainable education practices; student learning outcomes; teacher attitudes toward technology; government support.
  DOI: 10.1504/IJTEL.2026.10079713
   
  
• Empowering the educator: the mediating role of self-efficacy in gamification adoption among academicians in private higher education institutions  
  by Osman Zahir, Huong-An Thi Nguyen 
  Abstract: The adoption of gamification is strategically important for academicians in private higher education institutions, as it enhances student engagement and institutional competitiveness, yet its uptake remains limited. This study examines the effects of role clarity, role innovation, and role congruence on gamification adoption, with self-efficacy as a mediating variable. Drawing on role theory and self-efficacy theory, a quantitative survey was conducted with 299 academicians, and the data were analysed using partial least squares structural equation modelling. The results indicate that self-efficacy plays a central mediating role. Role congruence had a significant direct effect on adoption, whereas role clarity and role innovation did not. However, all three role-related factors significantly influenced self-efficacy, with role innovation emerging as the strongest predictor. Moreover, all indirect effects via self-efficacy were significant. The findings suggest that promoting gamification adoption requires clear role structures and strengthening academicians confidence through targeted training and supportive institutional environments.
  Keywords: gamification adoption; role clarity; RCL; role innovation; RIN; role congruence; RCO; self-efficacy; SE; academicians; private higher education institutions; PHEIs; partial least squares structural equation modelling; PLS-SEM.
  DOI: 10.1504/IJTEL.2026.10080924
   
  
• The impact of influencer credibility on purchase intention moderated by user experience  
  by Shafig Al-Haddad, Chahd El-Qutob, Sondos Al Khazendar, Farah Baker, Mays Al Adawi 
  Abstract: This research examines the impact of influencer credibility, with a focus on three dimensions, attractiveness, trustworthiness and expertise, on consumers purchase intentions. This research employed a quantitative research design, with a total of 163 valid convenience-sampling responses from digitally aware social media users who completed a structured online survey. The research findings clearly showed that all credibility dimensions can significantly predict consumers online purchase intention. The moderating analysis indicated that user experience can enhance the impact of influencer credibility on purchase intention. The research findings confirmed that influencer marketing is more effective when paired with a positive digital user experience. This research contributes to the influencer marketing and digital commerce literature by linking credibility dimensions to user experience factors to anticipate consumer purchasing behaviours. Brands should invest in credible influencers rather than those with viral visibility and reach, while optimising the user experience to improve conversion rates across digital touchpoints.
  Keywords: influencer credibility; attractiveness; trustworthiness; expertise; user experience; UX; purchase intention; social media marketing.
  DOI: 10.1504/IJTEL.2026.10080988
   
  
• How accurate and logical are AI chatbots in answering basic, clinical and research MCQs in dentistry?  
  by Ebtisam Almahdi Aboalgaith, Enas Almabrouk Ragab, Ali Milad Milad 
  Abstract: Technology-enhanced learning increasingly incorporates artificial intelligence (AI), yet evidence regarding the educational performance of AI chatbots in dentistry remains limited. This study evaluated four AI chatbots ChatGPT, DeepSeek, Google Gemini, and Microsoft Copilot using 90 validated board-style multiple-choice questions (MCQs) in operative dentistry and endodontics across basic, clinical, and research domains. Each model was tested in three independent sessions with a standardised prompt. Accuracy required consistent answers across sessions, and explanation quality (02 scale) was rated by two faculty members. Inter-rater reliability was assessed using weighted Cohens kappa, and group differences were analysed at = 0.05. Accuracy ranged from 76.7% to 86.7%, with no statistically significant differences between models. Reasoning quality was high (median = 2.0), and inter-rater agreement was excellent (= 0.8940.926). AI chatbots demonstrated comparable performance, supporting their potential as supplementary tools in dental education.
  Keywords: artificial intelligence chatbots; operative dentistry; endodontics; evidence-based dentistry; multiple-choice questions; MCQs; explanation quality.
  DOI: 10.1504/IJTEL.2026.10081089
   
  
• AI tools in accounting education: adoption drivers and academic effects  
  by Suhaib Bani Kinana, Omar Arabiat, Hashem Alshurafat 
  Abstract: This study examines the factors influencing the intention to use AI tools among accounting students at Jordanian universities and the subsequent effects on students achievement, integrating the characteristics of the diffusion of innovation (DOI) theory. A quantitative method was utilised. A total of 625 responses were collected from accounting students at universities across Jordan. The data was analysed using structural equation modelling (SEM) through PLS. The findings indicate a favourable correlation between the relative advantage, observability, compatibility, and the intention to use AI tools. This highlights their significance in influencing students behavioural intentions. On the other hand, complexity is negatively correlated with the intention to utilise AI tools, although this association is not statistically significant. This indicates that the challenges associated with understanding or using these tools impedes decision-making regarding AI adoption. Based on these findings, the study recommends focusing on reducing complexity by integrating AI into curricula.
  Keywords: diffusion of innovation; DOI; AI tools; student achievement; PLS-SEM; structural equation modelling; SEM.
  DOI: 10.1504/IJTEL.2026.10081133
   
  
• Teachers intention to adopt cybersecurity technologies for e-examinations in UAE higher education  
  by Rasha Talib Ahmed Alnaqeeb, Muthana F. Almasooudi 
  Abstract: Continuous adoption of learning systems in higher education has promoted the use of e-examinations, creating cybersecurity issues whose solutions depend on teachers willingness to embrace cybersecurity technology. Using the technology acceptance model (TAM), this study examines teachers behavioural intentions to adopt cybersecurity technologies for e-examinations in UAE universities. Using PLS-SEM and data from 350 teachers, the findings revealed that perceived usefulness, perceived ease of use, perceived trust, and institutional support influenced attitudes, which predicted cybersecurity adoption intention. However, perceived cyber risk and awareness showed non-significant effects, contradicting fear-driven and awareness-based behavioural theories. Attitude is the full mediator of relationships through institutional support paths. The model explained 64.1% of the variance in attitude and 12.7% of the variance in adoption, with institutional support representing the strongest predictor of teachers attitudes. This research contributes to TAM and offers evidence-based solutions for authorities and policymakers to develop cybersecurity infrastructure and promote teachers participation.
  Keywords: cybersecurity adoption; e-examinations; higher education; institutional support; teachers’ intention; technology acceptance model; TAM; UAE.
  DOI: 10.1504/IJTEL.2026.10081379
   
  
• Robotics and programming in primary education: analysis of the differences between socioeconomic level and gender in seven educational centres  
  by Rolando-Óscar Grimaldo-Santamaría, Sara Redondo-Duarte, Pilar Quicios-García, José-Manuel Sáez-López 
  Abstract: This study analyses the implementation of educational programming and robotics activities in a sample of 704 students from seven primary schools, specifically in the 5th and 6th grades. The intervention includes structured educational robotics activities and visual block programming using the micro:bit board and robotics kits. The approach is based on Seymour Paperts constructionism, which maintains that learning is more effective when students take an active role and construct their own knowledge by creating products or artefacts. The results show positive student attitudes toward these activities, which they perceive as creative, interesting, and innovative. Greater awareness and use are observed in schools with a higher socioeconomic and cultural level. No significant differences are found based on gender. These pedagogical proposals thus appear to be feasible and beneficial for promoting active, creative, and motivating learning.
  Keywords: educational technology; elementary education; gender; programming; robotics.
  DOI: 10.1504/IJTEL.2026.10081702