Lucrative career prospects and creative opportunities often attract students to enroll in computer science courses, resulting in large class sizes. A common challenge in large classrooms is the lack of engagement between students and both the instructor and the learning material. Recent advances in Large Language Models (LLMs) present an opportunity to leverage Conversational Artificial Intelligence (CAI) to address this challenge. To examine the potential of CAI to support engagement with learning material, we designed an in-class activity in a large Software Engineering course in which students interacted with a CAI tool. Using a quasi-experimental, within-subject study design in a real classroom at a large public United States University. We compared student engagement during an activity that involved the use of CAI for discussion with an activity that did not (peer discussion). For our analysis, we collected data on students’ interactions with the CAI tool and their self-reported responses on multiple engagement scales. Results show higher student engagement with the learning material in the activity involving CAI discussion as opposed to without (peer discussion). Higher self-reported learning was also observed in the activity with CAI as compared to without. Students also reported significantly more usefulness for learning in the CAI condition. Overall, our results indicate that CAI (ChatGPT) has the potential to support engagement with learning content during in-class activities in large class sizes.
Students’ academic achievement is determined by various factors, including teaching style and the use of e-learning resources. This study aims to analyze the effect of teaching style and e-learning on the academic achievement of students enrolled in the Catholic Religious Education Program at the Catholic University of Timorese (UCT) of Saint John Paul II. A quantitative correlational research approach was adopted, involving 77 respondents randomly selected from a population of 177 students. Data were collected through a self-administered questionnaire and analyzed using multiple linear regression with the support of JASP and SPSS software. The findings indicated that teaching style had a positive and significant effect on students’ academic achievement (p-value = 0.001 < 0.05), while e-learning’s independent contribution was positive but statistically non-significant (p-value = 0.053 > 0.05). However, when combined, both variables have a positive and significant impact (p-value = 0.000 < 0.05), explaining 41.3% of the variance in students’ academic achievement. These findings highlight e-learning’s pedagogical value, which emerges when integrated with appropriate teaching styles, serving as a complement rather than an independent determinant of academic achievement. The study provides theoretical and practical insights for optimizing the use of digital technology in higher education to improve teaching quality and students’ academic achievement.
Artificial Intelligence (AI) chatbots are increasingly reshaping learning practices in higher education. This study extends the Technology Acceptance Model (TAM) by incorporating critical thinking as a key cognitive antecedent of behavioral intention to use AI chatbots for learning. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data collected from Vietnamese university students (n = 100) were analyzed to examine the proposed relationships. The findings indicate that critical thinking is predictively associated with students’ self-reported behavioral intention to use AI chatbots and shows a larger standardized association than traditional TAM factors, including perceived usefulness, perceived ease of use, and attitude. Attitude toward AI chatbot use also directly predicts behavioral intention and is positively associated with critical thinking, suggesting a potential mediation mechanism. The extended model demonstrates substantial explanatory power in explaining variance in students’ self-reported behavioral intention. Overall, the results highlight critical thinking as an important cognitive factor associated with more reflective and responsible use of AI chatbots in university education. Given the cross-sectional and exploratory design, the findings should be interpreted as predictive rather than causal.
Autonomy and self-regulation among university students constitute key factors for the development of metacognitive skills and the strengthening of meaningful learning. In this context, the present study analyzes the relationship between Artificial Intelligence (AI)–based methodological strategies and the development of autonomous learning in higher education students. The objective was to predict and explain the variance of the constructs AI methodological strategies, autonomous learning, and academic development. A quantitative approach was adopted and implemented in two phases: an exhaustive theoretical review and the administration of an online survey to a sample of 383 university students from different academic disciplines. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS v4 software, yielding acceptable reliability levels (α ≥ 0.70). The results revealed significant and positive relationships between AI strategies, metacognitive processes, self-regulation, and affective motivation with academic development. Specifically, the use of virtual assistants was found to be strongly associated with AI methodological strategies (β = 0.744) and with academic development (β = 0.212), as virtual assistants enhance motivation by providing adaptive and personalized resources that stimulate autonomous learning. Furthermore, the mediating role of autonomous learning in the relationship between AI and academic performance was confirmed. These findings provide empirical evidence of the pedagogical use of AI as a facilitator of autonomous learning in university contexts. It is concluded that the integration of AI strengthens student autonomy, improves learning personalization, and optimizes academic performance; however, ethical and technological accessibility challenges persist and must be addressed to ensure equitable implementation in higher education.
Serious games provide an effective approach to cybersecurity education by situating learners in interactive environments that support skill acquisition and knowledge retention. This study introduces SecureCyberQuest (SCQ), a theory-driven serious game designed to enhance cybersecurity awareness and learner engagement. The game design integrates principles from Cognitive Load Theory to structure-focused mini games targeting specific cybersecurity concepts, and Flow Theory to guide the progression of challenges and game mechanics. An experimental evaluation with 44 undergraduate students demonstrated improvements in cybersecurity awareness following gameplay. Additionally, perceived learning effectiveness, usability, and overall gameplay experience were positively associated with user engagement. These findings empirically support Flow Theory, indicating well-balanced challenges and game elements foster learners’ competence and autonomy. The results further confirm the effective application of Cognitive Load Theory, as progressively increasing difficulty contributed to positive learning experiences and outcomes.
This study aimed to investigate the degree of faculty members’ acceptance of digital knowledge management through Artificial Intelligence (AI)-based Learning Management Systems (LMS) in Jordanian universities, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The researchers adopted a descriptive-analytical approach to assess the effectiveness of AI-based LMS as an independent variable on the dependent variable of digital knowledge management. A questionnaire was used as the primary data collection instrument and was distributed randomly through Google applications to a sample of 320 faculty members from universities in northern Jordan. The study results indicated that faculty members exhibited a high level of acceptance of digital knowledge management through AI-based e-learning systems. Furthermore, the findings revealed that key UTAUT constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions—had a significant positive impact on faculty members’ intention to use AI-based LMS for digital knowledge management. The researchers recommend encouraging faculty members to integrate AI applications into educational management, particularly in digital knowledge management, while considering theoretical models such as UTAUT to enhance adoption and implementation.
Public speaking anxiety among Gen Z students is currently experiencing a significant increase, making it difficult for them to speak in front of an audience. This has been proven to impact their performance and social relationships. Virtual Reality (VR) technology in the Metaverse is an effective training tool for managing public speaking anxiety through various environmental customizations. However, scientific research is needed to better understand which features of the simulated environment are most important for increasing or decreasing participants’ discomfort. This study examined the role of visual audience and presentation room characteristics, including realism and unrealism, on perceived anxiety, presence, and realism in an interactive VR public speaking scenario. 85 participants participated in four public speaking sessions and measured heart rate changes throughout the presentation. The results showed that audience and presentation room realism played a significant role in perceived anxiety during virtual public speaking. Specifically, higher levels of realism, as a stimulus, increased anxiety levels among Gen Z students. Conversely, lower levels of acoustic realism were associated with lower anxiety among presenters during public speaking. Furthermore, a relationship between realism and anxiety was found, and a relationship between the duration of practice and reduced anxiety. Overall, our results demonstrate that virtual environmental features play a key role in creating a realistic public speaking experience. They can be used as a social skills training strategy to help reduce academic difficulties and improve public speaking anxiety among Gen Z students.
An Artificial Intelligence (AI) cultural learning dashboard was designed and validated for measuring and assessing Balinese vocational students’ competencies in Samskara, Cesta, and Krtya. Using the Borg and Gall R&D model, the study was divided into three steps: (1) Information Research and Gathering, where relevant and contextual theories, including a needs analysis, were developed; (2) Preparation, where cultural constructs were operationalized into measurable AI-assessment metrics; and (3) First Production Cycle, where the initial User Interface (UI) prototypes were developed. A mixed-methods empirical study was conducted with 12 teachers and 45 students from three Tourism Vocational High Schools in Bali. The validation study comprised a usability study (System Usability Scale = 82.4), a cultural relevance study (positive response = 87%), and a content expert pedagogical alignment review. Although the dashboard operationalizes Balinese philosophical constructs, the AI-driven behavioral analytics are still in the early stages, as evidenced by the statistically significant AI assessment/teacher assessment correlation (r = 0.76, p < 0.01). Culturally responsive e-assessment design is a contribution vocational education scholars can derive from the study, as it is based on a theory and an empirical validation.
This mixed-methods study investigates the multidimensional role of AI literacy in English Language Teaching (ELT) among 50 faculty members in Saudi preparatory year programs, of whom 47 provided complete survey responses for quantitative analysis. Employing an explanatory sequential design grounded in the Technological Pedagogical Content Knowledge (TPACK) and Technology-Organization-Environment (TOE) frameworks, the research compares standardized AI literacy scores derived from a 17-item AI Awareness and Understanding (AIAU) scale with a single self-reported AI literacy rating and explores the structural determinants of adoption. Quantitative analysis showed a non-significant trend toward a perception-competence misalignment between self-reported and AIAU scale-based AI literacy scores F(2,44) = 2.83, p = 0.070, alongside stark demographic disparities: male faculty demonstrated significantly higher AI literacy than female colleagues (d = 1.11, p < 0.001), and faculty with more than 10 years of teaching experience outperformed those with 6–10 years F(1,45) = 11.31, p = 0.002, trends attributed to systemic access barriers rather than individual capability. Qualitative findings expose an “ethical competence paradox,” where faculty with high ethical concerns regarding data privacy (68.1%) and cultural misalignment (38.3%) demonstrate more sophisticated adoption reasoning. The study argues that institutional readiness—defined by equitable access, policy clarity, and tiered professional development—is more determinative of successful integration than technological sophistication. Recommendations include gender-intentional support structures and policy-grounded capacity building to align AI adoption with Vision 2030 goals.
This study examines undergraduate Information and Communications Technology (ICT) students’ needs, expectations, and concerns regarding Artificial Intelligence (AI)-supported adaptive learning for programming education in Kazakhstan. Using a mixed-methods descriptive design, data were collected from 134 students at two universities via a multilingual online questionnaire that combined Likert-scale items with open-ended questions. Quantitative results indicate high awareness of AI and strong readiness to use AI-supported learning tools, with the highest-rated needs focusing on adaptive content, automated practice/task generation, progress tracking, and mobile access. At the same time, students reported moderate concerns about the accuracy of AI-generated materials and the privacy of learning data, and they preferred models where teachers remain actively involved in interpreting and guiding AI feedback. Qualitative findings reinforced these patterns, identifying technical reliability, language/localization, transparency, and integration with existing Learning Management System (LMS) as key barriers, while emphasizing stability, explainable feedback, multilingual support, and teacher-in-the-loop functionality as priorities for improvement. This study should be interpreted as a pre-implementation needs assessment based on students’ responses to a hypothetical AI-supported learning scenario and does not provide evidence of actual adoption or instructional effectiveness. The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requires further research.
This study investigates how technology-enhanced tools and Artificial Intelligence (AI) can be used to personalize fraction learning in primary education through cognitive profiling. Fractions are a foundational area of mathematics, yet many students struggle to understand their abstract concepts. While research has shown that cognitive skills such as working memory, attention, and visual perception influence mathematics performance, there is limited evidence on how these factors specifically shape students’ Conceptual Understanding (CU) and Procedural Knowledge (PK) of fractions. To address this gap, senior primary school students in Greece were assessed using the RODI cognitive profiling application, the Fraction Lab learning tool, and additional validated measures of CU and PK. AI-based analyses were applied to examine relationships between cognitive skills, fraction performance, and learning outcomes, and to map students into distinct cognitive profiles. Results demonstrated strong links between visual perception, executive function, and fraction performance. Distinct profiles revealed different patterns of strength and challenge, which were then used to design adaptive learning pathways. The findings highlight how digital tools can provide teachers with actionable insights for tailoring instruction, enhancing engagement, and improving outcomes in fraction learning.
Although Virtual Reality (VR) is widely regarded as an effective tool for enhancing experiential learning, its role in fostering spatial presence in English for Specific Purposes (ESP), particularly in a hotel English course, remains underexplored. This study examines whether Immersive VR (IVR) elicits stronger spatial presence than Desktop VR (DVR) and how these differences are reflected in learners’ experiences of self-location and possible actions. To systematically investigate these relationships, a dual-mode VR hotel English training system was developed, with identical content, tasks, and instructional design across conditions, differing only in interaction modality. An explanatory sequential mixed-methods design was employed (QUANTITATIVE → qualitative = explanation). In the quantitative phase, 144 undergraduate students were assigned to IVR or DVR conditions in a quasi-experimental study. In the qualitative phase, 8 participants were interviewed to further explore their experiences. Quantitative results showed that students in the IVR condition reported significantly higher spatial presence than those in the DVR condition. Qualitative findings indicated that IVR enhanced learners’ sense of self-location and possible actions through greater immersion and interactivity, although some participants reported discomfort and occasional difficulty concentrating. DVR, while less immersive, was perceived as more comfortable and easier to use. By integrating spatial presence theory with ESP pedagogy, this study provides empirical evidence for selecting appropriate VR modalities for hotel English instruction and offers a more explanatory framework to narrow the gap between students’ conversational competence and the communicative demands of the hospitality industry.
Developing critical thinking skills is a cornerstone of modern education, particularly for pre-service teachers who will foster these competencies in future generations. This study addresses the common failure of traditional pedagogical methods by evaluating an integrated learning model that combines Project-Based Learning (PjBL), interactive e-modules, and gamification to enhance the critical thinking skills of pre-service physics teachers. A mixed-methods sequential explanatory design was employed with 45 participants. The results demonstrate that the model significantly improved critical thinking skills (p < 0.001), with the highest gain observed in interpretation (N-gain = 0.71) and explanation (N-gain = 0.69), with the lowest gain in evaluation (N-gain = 0.48). A key finding emerged from project assessments: gamified projects stimulated the highest in-process student engagement (M = 4.33), whereas more structured projects, such as website development, yielded superior quality final products (M = 4.49). In conclusion, integrating PjBL, e-modules, and gamification is a practical approach for enhancing critical thinking skills, particularly in fostering interpretation and explanation. The findings highlight a crucial trade-off between process engagement and final product quality, suggesting that educators should implement a strategic, hybrid PjBL model for optimal outcomes. This model would leverage the motivational benefits of gamification in early-stage activities and employ structured projects for final capstone tasks, thus balancing engagement with academic rigor.
In numerous Global South contexts, including Ecuador, wherein English proficiency remains low while schools encounter persistent infrastructural constraints, the promise of digital learning frequently collides with everyday challenges. This mixed-methods study examines how educational technology is experienced in a public secondary school and utilises the resulting insights to inform the design of a user-centred Virtual Learning Environment (VLE) for English Language Teaching (ELT). Data were collected from 155 participants—specifically, 133 students, 18 teachers, and 4 school leaders—using the SELFIE survey, semi-structured interviews, and classroom observations. The findings reveal a pronounced gap between stakeholders’ willingness to innovate and the persistent barriers that they face, with technology use remaining largely basic and teacher-centred. Triangulation of the three data sources highlights recurring constraints related to connectivity, device availability, wayfinding, and workload, alongside clear indications of motivation and openness to improving practice. On this basis, we derive design requirements that prioritise low-bandwidth delivery, device-agnostic access, minimal-click navigation, and concise feedback loops suitable for classroom orchestration. These requirements are grounded in converging evidence and presented as actionable guidance for comparable low-resource settings. This study concludes that effective solutions must be genuinely user-centred, supporting the co-design of a VLE for ELT that responds to on-the-ground realities and fosters more equitable learning pathways.
Recent academic work has highlighted the need for a valid and trustworthy tool to assess Chinese undergraduates’ self-efficacy in English argumentative writing. To tackle the challenge posed by the shortage of relevant tools, this study evaluated the psychometric properties of a newly adapted questionnaire, called Foreign Language Learner Self-Efficacy Questionnaire for English Argumentative Writing (FLLSEQEAW), using the Rasch model. The research included 563 undergraduate students from a key provincial university in northern China. The data were analyzed to check the questionnaire’s psychometric quality. The findings confirm that the FLLSEQEAW is a unidimensional and psychometrically sound instrument. The study also acknowledges limitations and proposes future studies to gather further evidence of its validity.
A descriptive research method was utilized in this study to examine the challenges and opportunities associated with AI in gifted education. The study included 582 teachers from King Abdullah II Schools for Excellence, which indicated that artificial intelligence is on average used in Jordanian gifted schools. Using factor analysis, three important dimensions of the challenges associated with utilizing artificial intelligence techniques in gifted schools in Jordan were identified. The challenges identified include: the obstacles and personal challenges faced by teachers of gifted students in adopting and employing Artificial Intelligence (AI) in the educational process for gifted students, the lack of support and training in applying artificial intelligence in the absence of resources and technology, and the obstacles and challenges related to using artificial intelligence. This study is significant as it highlights critical issues and provides insights into AI utilization in the context of gifted education in Jordan.
This study developed and validated a web-based Mixed Reality (MR) system to enhance chemistry learning in schools with limited laboratory resources. Developed using WebXR and the Three.js JavaScript Library, the system delivers immersive, browser-based simulations of distillation and filtration laboratory experiments compatible with the Meta Quest 3 headset. Grade 10 students and six chemistry teachers from an integrated high school in the Philippines evaluated the system’s usability, engagement, and instructional effectiveness through surveys and pre- and post-test comparisons. Results indicated a significant increase in students’ conceptual understanding, with post-test scores rising from 89.33 to 94.67 (t(44) = 2.737, p = 0.008919). Students reported high engagement (mean = 3.71 for interaction enjoyment; 3.56 for learning excitement), while teachers rated usability and engagement at 3.83 and 4.00, respectively. These outcomes confirm that the Mixed Reality (MR) system fosters active and experiential learning while reducing reliance on physical laboratories, underscoring MR’s potential to make chemistry education more accessible and pedagogically effective in resource-constrained settings.
Mobile game-based learning is increasingly recognized for its potential to enhance academic outcomes, yet its application in addressing specific learning difficulties in Science—particularly Biology—remains limited. This developmental research aimed to develop and evaluate a mobile learning application targeting the least-learned competencies in Grade 7 Living Things and Their Environment (Biology). The Analyze, State the objectives, Select, Modify or Design Materials, Utilize materials, Require learners’ response, and Evaluation (ASSURE) instructional design model guided the development process with technology and media integration. Purposively selected learners from public secondary schools; experts in information technology, Biology, and language served as participants of the study. The identified least-learned competencies were: differentiating plant and animal cells; focusing specimens using a compound microscope; predicting the effect of population changes on ecosystems; predicting the impact of abiotic factor changes, and distinguishing sexual and asexual reproduction. In response, four mobile games were developed: MicroSim (simulation), PuzzCell (puzzle), ReproDefenders (adventure/role-playing), and PredicTerms (word puzzle). Evaluation results showed high acceptability (M = 3.79), with favorable ratings for game usability (3.83), mobility (3.62), gameplay (3.85), and learning content (3.88). Findings suggest that the developed mobile games are suitable for blended learning environments, enabling learners to engage with them. Unlike existing educational mobile games that address general science concepts, this study is novel in its targeted, evidence-based approach which embeds identified least-learned competencies in Biology into purpose-built, curriculum-aligned mobile games developed using the ASSURE model.
In the context of accelerated digital transformation in education, Artificial Intelligence (AI) is increasingly recognized as a strategic enabler for personalized, efficient, and data-driven teaching practices. This study proposes and empirically validates a multidimensional competency model for AI integration in arts education. Grounded in the integration of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Technological Pedagogical Content Knowledge (TPACK) frameworks, the model encompasses six latent constructs: foundational AI knowledge, AI-integrated pedagogical skills, ethical awareness, pedagogical efficacy, reflective thinking, and practical AI use behavior. A quantitative design was applied, with survey data collected from 202 arts teachers in Vietnamese K–12 schools. Structural Equation Modeling (SEM) was employed to assess the model’s fit and predictive power. Findings confirm the structural coherence of the model and highlight the interplay between cognitive, ethical, and reflective dimensions in shaping AI adoption in artistic instruction. This research advances theoretical understanding of technology acceptance in creative domains and provides actionable insights for curriculum designers, policymakers, and teacher educators aiming to foster responsible and pedagogically meaningful AI usage in the digital age.
This study aims to design a physics website based on cognitive conflict, Augmented Reality (AR), and Science, Technology, Engineering, and Mathematics (STEM) that is valid to support students’ critical thinking skills. The study employed an Educational Design Research (EDR) approach consisting of three stages: analysis and exploration, design and construction, and evaluation and reflection. The participants included 404 randomly selected upper secondary students in West Sumatra, Indonesia, and six experts for validation. Data were collected through a needs analysis questionnaire and expert validation sheets. The needs analysis was examined using descriptive statistics, t-tests, and ANOVA, while product validity was assessed with Aiken’s V index. The findings indicate that students across different grade levels, both male and female, share similar perceptions regarding socio-emotional aspects, efficiency, actual behavior, and preferences, underscoring the need for integrating a physics website into the learning process. This study produced a validated physics website (V > 0.8) that integrates AR, STEM, and the cognitive conflict-based learning model to support students’ critical thinking skills. This design was found to address student needs and was validated as being valid. This study was limited to the design stage; therefore, the developed website is recommended for further testing to evaluate its effectiveness. It has the potential to enhance digital learning by fostering engagement and improving students’ critical thinking skills.
