Aim/Purpose: To address the lack of a clear pedagogical framing of prompt engineering in secondary education and to analyze how it is currently conceptualized in educational research. Background: While secondary school students increasingly use generative AI tools, prompt engineering is often treated as an implicit technical skill rather than as an explicit educational practice linked to metacognition and AI literacy. Methodology: This study adopts a systematic literature review following PRISMA guidelines. Twenty-one peer-reviewed studies published between 2021 and 2025 were selected from Scopus, Web of Science, IEEE Xplore, and ACM Digital Library. Contribution: The paper provides a structured conceptual mapping of how prompt engineering is addressed in secondary education and identifies gaps between research practices, pedagogical frameworks, and AI literacy policies. Findings: The review shows that prompt engineering is rarely framed as an explicit learning objective, that empirical evidence on cognitive and metacognitive effects is fragmented, and that ethical and reflective dimensions are inconsistently addressed. Recommendations for Practitioners: Teachers should explicitly scaffold students’ prompt design practices, integrate reflective activities on AI use, and align classroom practices with emerging AI literacy frameworks. Recommendation for Researchers: Future studies should operationalize prompt engineering as a learning objective, develop validated assessment tools, and conduct longitudinal research in secondary education contexts. Impact on Society: The findings support the development of responsible AI use in education by highlighting the need for pedagogical guidance that fosters students’ agency, critical thinking, and ethical awareness. Future Research: Future research should investigate instructional models for teaching prompt engineering across disciplines and examine its long-term effects on students’ learning strategies and epistemic beliefs.
Aim/Purpose: This study systematically reviews machine learning techniques for predicting undergraduate student dropout in higher education, identifies related risk factors, explores methodological gaps in Machine Learning (ML)-based dropout prediction, and outlines directions for future research and institutional implementation. Background: Student dropout remains a persistent global challenge with significant academic and socioeconomic implications. Although many studies report high predictive accuracy for dropout models, there is still limited understanding of how these models operationalize educational theory, incorporate psychosocial and equity-related factors, and translate predictions into empirically validated interventions in real institutional settings. Methodology: The review followed Kitchenham’s methodology and PRISMA 2020 guidelines, using a PICO-C framework focused on undergraduate higher education. Searches from 2019 to 2024 across eight databases yielded 301 records. After screening using predefined inclusion/exclusion criteria, double review with Cohen’s kappa, and a 16-criterion weighted quality assessment (including automated keyword checks), 75 studies met all quality and relevance thresholds. Contribution: This systematic review synthesizes 75 quality‑assessed studies on ML‑based undergraduate dropout prediction, clarifying which algorithms currently dominate the field, which risk factors and theoretical frameworks are most frequently used, and where critical gaps remain in generalizability, equity, explainability, temporal modeling, and intervention validation. It is distinctive for its explicit quality‑weighting process, its focus on the actionability gap, and its mapping of emerging trends and outstanding needs in the field. Findings: The systematic review shows rapid growth in dropout prediction research from 2019 to 2024, reflecting increased institutional awareness. Academic performance and engagement indicators are the most used risk factors in prediction models, whereas psychosocial factors (including self-efficacy, sense of belonging, motivation, and resilience) remain significantly underrepresented. Ensemble methods (particularly Random Forest and XGBoost) dominate the algorithm landscape, consistently achieving accuracies of around 86–88%. However, most studies rely on single-institution datasets, do not account for temporal changes, and rarely evaluate the impact or fairness of interventions triggered by predictions. These limitations reduce the generalizability, equity, and practical utility of ML-based predictive models in higher education. Recommendations for Practitioners: Machine learning models should be viewed as decision-support tools, not standalone solutions. By combining predictive risk assessments with academic, financial, and engagement data, institutions can design multi-dimensional retention strategies, implement early-stage monitoring protocols, and establish clear intervention procedures that are evaluated for effectiveness and fairness. Recommendation for Researchers: Researchers should move beyond single-institution, static models toward longitudinal, cross-institutional designs that incorporate temporal dynamics, transfer learning, and domain adaptation. Future studies should integrate psychosocial, motivational, and equity-oriented variables; systematically apply fairness-aware machine learning and explainability (XAI) techniques; and empirically evaluate whether predictive systems improve retention through controlled intervention studies. Impact on Society: By clarifying how machine learning can reliably identify students at risk of dropout and exposing current limitations in fairness and implementation, this review supports the design of more equitable and effective retention policies in higher education. Improved early-warning systems and evidence-based interventions can reduce economic losses, mitigate social inequality, and enhance graduation rates, particularly in regions with historically high attrition. Future Research: Future research should focus on cross-institutional benchmark datasets, longitudinal cohort studies, and models that adapt to diverse institutional and regional contexts. There is a pressing need for studies that integrate psychosocial and structural factors, evaluate fairness across vulnerable groups, apply explainability frameworks as standard practice, and link predictive models to experimentally tested intervention strategies, moving the field from predictive accuracy toward demonstrable educational impact.
Aim/Purpose Despite the persistent challenge of low learner engagement in Massive Open Online Courses (MOOCs), particularly in Global South contexts, this study examines how artificial intelligence (AI)-enabled learning experiences shape multidimensional learner engagement in higher education MOOCs in Vietnam, based on an extended UTAUT2 framework. Specifically, it investigates the mediating role of behavioral intention in linking AI-enabled experiences to cognitive, emotional, and behavioral engagement. Background Although MOOCs expand access to higher education, sustaining meaningful learner engagement remains a persistent challenge. Recent advances in artificial intelligence enable adaptive feedback, intelligent support, and personalized learning pathways; however, limited empirical research explains how these AI-enabled experiences translate into cognitive, emotional, and behavioral engagement, particularly in Global South settings such as Vietnam. Methodology A sequential explanatory mixed-methods design was employed. Survey data from 652 MOOC learners in Vietnam (undergraduate students) were analyzed using structural equation modeling to test an integrated motivational-experiential framework. Semi-structured interviews with seven participants provided qualitative insights that explained the structural relationships and captured learners' lived experiences with AI-supported learning. Contribution This study develops an integrated framework to explain learner engagement in AI-enhanced MOOCs by extending the UTAUT2 model with AI-specific constructs, including trust in AI and perceived personalization. It reconceptualizes technology acceptance factors as functional learning motivations and socio-affective drivers in the context of AI-supported learning. Furthermore, it identifies behavioral intention as a central mediating mechanism linking AI-enabled experiences to multidimensional learner engagement. Findings AI-enabled learning experiences, together with functional learning motivations and socio-affective drivers, were significantly associated with learners' behavioral intention, which functioned as the central mediating mechanism leading to behavioral, emotional, and cognitive engagement. Among the antecedents, trust in AI, perceived personalization, and hedonic motivation emerged as the strongest predictors of behavioral intention. Qualitative findings further revealed that learners experienced AI as an epistemic and affective partner that fostered metacognitive reflection, structured learning routines, and sustained motivation. However, the effectiveness of AI-enhanced MOOCs was conditioned by infrastructural limitations, uneven digital literacy, and socio-cultural learning contexts. Recommendations for Practitioners Recommendations for Researchers MOOC providers should design transparent, trustworthy, and personalized AI features that support self-regulated learning while ensuring low-bandwidth accessibility and contextual relevance for diverse learner populations. Future studies should adopt longitudinal and cross-cultural designs and integrate learning analytics with self-reported engagement measures to capture the dynamic nature of learner-AI interaction. Impact on Society By identifying mechanisms that foster sustained engagement in large-scale online learning, this study contributes to more inclusive and equitable access to higher education in developing countries. Future Research Further research may examine experiential AI-supported learning interventions, participatory AI design with learners, and comparative analyses across Global South contexts.
Aim/Purpose: This paper examines how learning media function as social artefacts that influence academic self-efficacy and perceived applied physics literacy, and addresses the need to understand the role of aesthetic design and cultural relevance in vocational STEM education. Background: While interactive and visually appealing media are widely used, their combined impact with culturally relevant content on students’ confidence and students’ perceived disciplinary literacy remains underexplored, particularly in Indonesian Mining Engineering education. Methodology: A cross-sectional survey was conducted among 355 undergraduate students in Mining and Geological Engineering programs. Data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test direct and mediating relationships among aesthetic design, cultural relevance, media interactivity, academic self-efficacy, and perceived applied physics literacy. Contribution: This study integrates aesthetic, cultural, and functional features of learning media into a unified framework, offering empirical evidence of how these dimensions are associated with motivational and cognitive outcomes in applied STEM learning. Findings: Aesthetic design and cultural relevance were significantly associated with higher self-efficacy, which showed small but statistically significant indirect associations with perceived applied physics literacy. Media interactivity and usability were positively associated with students’ perceived applied physics literacy but were not significantly associated with self-efficacy. Students’ perceived applied physics literacy is therefore associated with both social (cultural and aesthetic) and functional (interactive) media features. Recommendations for Practitioners: Educators and instructional designers should incorporate culturally aligned, aesthetically coherent, and interactive media to support students’ self-efficacy and perceived disciplinary competence. Recommendation for Researchers: Future studies should employ larger, diverse samples and mixed-method designs to explore how culturally and aesthetically informed media may support students’ confidence, perceived disciplinary competence, and engagement with vocational STEM learning. Impact on Society: By supporting students’ confidence, perceived disciplinary competence, and engagement in vocational STEM learning, culturally and aesthetically informed media may enhance students’ learning experiences in vocational STEM education. Future Research: Investigate adaptive and AI-driven learning media that integrate cultural and aesthetic considerations to sustain engagement, identity formation, and self-efficacy across diverse educational contexts.
Aim/Purpose The study proposes and evaluates a theory-driven analytics methodology for inferring learners' cognitive load and performance from Moodle log data in online learning environments. Background Although Moodle captures rich learner interaction data, existing analytics largely focus on descriptive engagement metrics and provide limited insight into learners' cognitive processes. This gap restricts the ability of learning analytics to support cognitively informed instructional design and adaptive learning. Methodology An experimental design was employed, with 324 undergraduate students from three faculties in a public University, randomly assigned to treatment and control groups. The treatment group received cognitive load-informed interface scaffolds, while the control group accessed the same content without interventions. Moodle log data collected over four weeks were analyzed using statistical tests, clustering, classification, and association rule mining within a nine-step analytics workflow implemented in Python. Contribution The study introduces a reproducible analytics methodology that explicitly embeds extraneous, intrinsic, and germane cognitive load constructs into Moodle log analysis, extending existing LMS analytics frameworks from descriptive engagement monitoring to theory-grounded explanatory and predictive analysis. Findings Results indicate that treatment learners demonstrated significantly higher en-gagement and performance than control learners, with large effects for course activity completion (d = 0.77) and module views (d = 0.65), and moderate ef-fects for quiz grades (d = 0.38). Machine learning analyses further revealed more differentiated behavioral patterns and stable associations between cogni-tive load-related behaviors and learning outcomes among treatment learners. Recommendations for Practitioners Recommendations for Researchers Educators and instructional designers can apply the proposed methodology to unobtrusively monitor learners' cognitive load and implement targeted interface scaffolds that enhance engagement and performance without altering core in-structional content. Researchers are encouraged to adopt and extend the proposed methodology to examine cognitive load dynamics across disciplines, platforms, and instructional designs, and use it as a foundation for theory-driven learning analytics studies that move beyond purely predictive models. Impact on Society By enabling cognitively informed learning analytics at scale, this methodology supports more effective and equitable online education, particularly in resource-constrained contexts. Future Research Future work should validate log-based cognitive load indicators using multi-modal data, explore real-time adaptive learning systems responsive to inferred cognitive load, and assess the long-term effects of cognitive load-informed in-terventions on learner retention and achievement.
Aim/Purpose This study examines limitations in existing AI adoption models in education, which often treat data quality, interpretability, trust, and organizational factors as independent elements. This separation may lead to incomplete explanations of AI implementation outcomes in educational information systems. Background This study introduces and empirically examines the Data-Knowledge Alignment Theory for Educational Information Systems (DKAT-EIS). The framework draws on insights from Information Systems, Knowledge Management, and Explainable Artificial Intelligence to explore how data quality, knowledge interpretability, and trust relate to AI adoption in universities. DKAT-EIS is presented as a context-specific analytical framework that offers initial insights into these relationships in higher education. Methodology A quantitative survey was conducted with 1,150 respondents (students, faculty, and staff) at Jazan University in Saudi Arabia. Cross-sectional data were analyzed using Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) to test the proposed relationships. Contribution The study proposes and examines the DKAT-EIS framework to better understand how data and knowledge processes influence AI adoption in higher education. The findings highlight the role of data quality in supporting knowledge interpretability and indicate that the relationships between interpretability, trust, and adoption may be more complex than suggested in traditional technology adoption models. Given the single-institution and cross-sectional design, the findings should be interpreted as context-specific evidence that encourages further validation in other settings. Findings Data quality significantly predicts knowledge interpretability. However, interpretability does not significantly influence ethical trust, and trust does not directly predict AI adoption success. This pattern suggests a "trust-interpretability paradox." In addition, organizational readiness does not significantly moderate the relationships in the model. Recommendations for Practitioners Recommendations for Researchers Educational institutions should prioritize data governance and data quality when implementing AI systems. Improving system transparency alone may not build trust; organizations should also address ethical, organizational, and cultural factors that strengthen user confidence. Future studies should examine additional factors, such as fairness perceptions, digital culture, and institutional context, that may explain the gap between interpretability and trust. Further testing of the DKAT-EIS framework across different universities and regions is recommended. Impact on Society The findings suggest that responsible AI adoption in higher education requires a balanced socio-technical approach that combines strong data foundations with attention to ethical and organizational factors that foster trust. Future Research Future research should include longitudinal studies, cross-institutional validation, and qualitative investigations to better understand how contextual factors influence AI adoption, trust, and interpretability.
Contribution This study contributes empirical evidence on the adoption of GenAI in a South Asian tertiary education context, enriching the body of knowledge on technology acceptance, digital pedagogy, and GenAI in education policy. By revealing the pictures of relevant variables of Generative Artificial Intelligence in Education (GenAIEd) in a unique context, such as Bangladesh, the findings have implications for similar situations. They can inform others about possible challenges and the usefulness of GenAIEd. Findings Teachers and students are both moderately familiar with GenAI. The teachers primarily use it to prepare courses and materials, while students sporadically engage with GenAI, mainly for academic problem-solving, and they emphasize its role in personalized, learner-centered learning. GenAI familiarity is found to be a strong predictor of usage frequency. However, teachers express concerns about the reliability of GenAI, ethical implications, and the potential for deskilling. While the benefits and usefulness dominate, possible challenges and threats are marginally associated with the future adoption and use of GenAI. This finding is unique because, despite the overpowering 'ease of use' of the TAM model, 'benefits or usefulness' of the TTF model, challenges, and threats have been found as catalysts for GenAI adoption. Recommendations for Practitioners Recommendations for Researchers Practitioners are to utilize GenAI to support, rather than replace, their teaching expertise. They should also encourage students to strike a balance between GenAI-assisted learning, critical thinking, and independent work. Furthermore, the institutions should introduce guidelines to ensure the ethical use of GenAI and academic integrity. Researchers should explore the longitudinal effects of GenAI adoption on learning outcomes and skill development. They can also conduct comparative studies across different universities and disciplines. Investigating the role of GenAI in inclusive education and support for learners from disadvantaged backgrounds also demands research focus. Impact on Society The findings highlight how GenAI can transform higher education in Bangladesh and similar contexts. It shows the importance of addressing the risks of overreliance and the unethical use of GenAI for effective learning. A balanced adoption could strengthen human-technology collaboration in education. On the other hand, it has revealed the aspects of GenAI, preferred by educators, that AI developers should consider. Future Research Further studies should examine hybrid learning models that integrate GenAI with human expertise. Cross-cultural perspectives on GenAI in education remain another area of study. Furthermore, studies should be carried out to develop frameworks for maintaining academic authenticity while GenAI is being used in education.
Aim/Purpose This study aims to identify and explain the key factors influencing the artificial intelligence (AI) readiness of in-service secondary Vietnamese teachers, addressing the lack of empirical evidence on how teachers progress from initial acceptance to active instructional integration of AI. Background While AI is increasingly promoted in education policy and practice, teachers' readiness to adopt and integrate AI remains uneven and underexplored in developing countries. Existing studies often conceptualize AI readiness as a single construct, overlooking its developmental nature. Methodology The study employed a quantitative approach and collected survey data from 1,145 in-service secondary school teachers in Vietnam. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study developed and validated a theoretical model incorporating organizational factors (policy and organization support, professional development, collegial support), individual factors (confidence, perceived relevance), and social factors (subjective norms) as predictors of readiness stages (Accepting, Introducing, Teaching). Contribution This research contributes to the literature by conceptualizing teachers' AI readiness as a progressive, multi-stage construct and empirically validating a comprehensive model that integrates organizational, professional, social, and individual factors in a developing-country context. Findings The model demonstrated strong explanatory power and predictive relevance, with thirteen of fourteen hypotheses supported. Results indicate that AI confidence and perceived AI relevance significantly predict all stages of AI readiness. Subjective norms influence early stages of readiness but do not directly affect advanced teaching integration. Acceptance strongly predicts introduction, and introduction, in turn, is a powerful predictor of AI integration in teaching. Collegial support emerged as the strongest organizational predictor of subjective norms. Multi-group analysis revealed gender and regional differences, with professional development effects stronger for female teachers and perceived relevance influencing male teachers' integration intentions more substantially. Recommendations for Practitioners Recommendations for Researchers Educational leaders should design staged professional development programs that build teachers' confidence, highlight the pedagogical relevance of AI, foster supportive collegial cultures, and provide clear institutional policies. Future studies should adopt longitudinal designs to examine changes in AI readiness over time and extend the model to other subject areas and educational levels to enhance generalizability. Impact on Society By clarifying how teachers develop readiness to integrate AI into teaching, this study supports more effective implementation of AI-driven educational reforms, contributing to improved teaching quality and equitable digital transformation in education. Future Research Further research should explore causal mechanisms through mixed-methods approaches and investigate how student outcomes and ethical considerations interact with teachers' AI readiness across diverse educational contexts.
Aim/Purpose The purpose of this study is to identify teachers' and school administrators' readiness for artificial intelligence (AI) integration by conducting a comprehensive needs analysis, with a particular focus on the role of prompt literacy as an emerging dimension of AI competence. Background While AI-based tools are more widely accessible in educational settings, many educators struggle to use them pedagogically and strategically. Existing research has primarily emphasized AI adoption rather than educators' readiness, leaving a gap in understanding the competence-related and institutional factors shaping meaningful AI integration. Methodology The study adopted an exploratory mixed-methods design. Quantitative and qualitative data were collected via a researcher-developed online needs analysis survey distributed to teachers and school administrators in public schools in T & uuml;rkiye (N = 434). Quantitative data were analyzed using descriptive statistics and non-parametric relational analyses, while open-ended responses were examined through thematic analysis. Contribution This paper contributes to the literature by systematically positioning prompt literacy as a foundational component of educators' AI readiness and by integrating quantitative and qualitative evidence within a needs-based framework. It extends AI-in-education research beyond tool adoption toward pedagogical and institutional readiness. Findings Findings indicate that although most participants reported using AI tools, the majority perceived their competence as beginner-level. Prompt literacy was strongly associated with AI competence. Qualitative findings revealed pedagogical uncertainty, ethical and data security concerns, workload pressures, and a lack of institutional guidance as key barriers to effective AI integration. Recommendations for Practitioners Recommendations for Researchers Professional development initiatives should adopt differentiated and staged designs, prioritizing prompt literacy as a foundational skill and emphasizing pedagogical integration, ethical awareness, and context-specific classroom applications. Future research should conceptualize and operationalize prompt literacy, employ validated measurement instruments, and examine the effects of targeted AI professional development interventions using experimental or design-based research approaches. Impact on Society By supporting educators' pedagogically and ethically informed use of AI, the findings contribute to more responsible, equitable, and sustainable AI integration in education, helping to mitigate risks related to misuse, data privacy, and superficial adoption. Future Research Subsequent studies may explore longitudinal changes in educators' AI readiness, conduct comparative analyses across institutional or national contexts, and investigate how leadership practices and policy frameworks influence sustainable AI adoption in schools.
Aim/Purpose The successful integration of immersive virtual reality (imVR) in education depends on various factors, including educators' views and intentions. Pre-service teachers represent an important demographic as they can play a pivotal role in shaping future educational practices. Therefore, understanding the factors that shape their intentions to use this technology is important. Background The Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) model is widely used in educational technology research to examine behavioral intentions across various groups regarding technology use. However, the model is not without its limitations. One of the most significant shortcomings is that it considers its constructs as exogenous factors influencing behavioral intention, without delving into their interrelationships. To address this limitation, the present study proposed and examined an extensively modified version of the UTAUT-2 model. Additionally, self-efficacy is incorporated as an important factor in this framework. Methodology A total of 202 senior students studying at a Department of Education participated in the study, enrolled in a course designed to familiarize them with the design and use of educational imVR applications. Data were collected at the end of the semester, using a questionnaire designed to examine the factors included in the modified UTAUT-2 model. Contribution By demonstrating strong in-sample and out-of-sample predictive power, the proposed model offers a strong theoretical and practical framework for understanding pre-service teachers' intentions to use imVR and for promoting imVR adoption in teacher preparation contexts. Findings Hedonic motivation, habit, and performance expectancy impacted behavioral intention. Self-efficacy emerged as a central determinant, shaping participants' perceptions of effort expectancy, performance expectancy, and hedonic motivation. Facilitating conditions significantly enhanced self-efficacy, effort expectancy, and habit. Age, sex, and prior experience showed limited or no impact. The structural model demonstrated strong inand out-of-sample predictive/explanatory power, while the Importance-Performance Map Analysis identified habit and hedonic motivation as key areas requiring improvement. Recommendations for Practitioners Recommendations for Researchers Education stakeholders should focus on building pre-service teachers' confidence and competence in using imVR through structured, hands-on training and consistent access to well-supported technological environments. Efforts should prioritize cultivating habitual use of imVR by embedding it into regular teaching activities, lesson planning, and semester-long coursework, supported by readily available technical assistance. Finally, enhancing the enjoyment and engagement of imVR experiences can boost teachers' intrinsic motivation, further strengthening their intention to integrate this technology into their future instructional practices. The study illustrates the need to integrate constructs such as self-efficacy, which are not fully addressed in traditional behavioral intention frameworks. Experimental designs that pay attention to sample selection are strongly advised to avoid random or invalid responses when evaluating users' perceptions and intentions related to advanced or emerging technologies. Finally, the study demonstrates the necessity of suggesting and examining models that capture the complex and multifaceted dynamics of imVR adoption. Future Research Researchers should further validate the modified UTAUT-2 across diverse educational contexts and participant demographics. Longitudinal and mixed-meth-ods designs are also advised. Expanding the model to include additional contextual variables can provide a more comprehensive understanding of barriers and facilitators influencing teachers' intention to adopt imVR.
Aim/Purpose To address the research gap in terms of evidence on students' integration of STEM concepts in challenge-based learning (CBL) in the context of senior high school (SHS) robotics. It also evaluates the extent to which robotics can support students in achieving integrated STEM subject areas through the Situated STEM Learning lens. Background Research shows that educational robotics and CBL are effective but overlook the cognitive processes students use to integrate STEM concepts, and there is a lack of qualitative research on their perceptions of STEM integration in robotics. Relying primarily on quantitative data misses crucial insights into student experiences. Methodology A descriptive mixed-methods design was employed across two SHS cohorts (N = 50). Data were collected using a validated STEM integration assessment rubric, student engineering notebooks, and open-ended feedback questionnaires. Contribution Identifies a critical disciplinary imbalance in CBL robotics projects, where technology and engineering often overshadow abstract scientific and mathematical principles. It emphasizes that meaningful integration requires explicit pedagogical scaffolding, not an inherent byproduct of construction. Findings Students found CBL highly effective for bridging theory and practice and fostering computational thinking and creative problem-solving. However, project outputs revealed a notable deficiency in the use of scientific reasoning and mathematical calculations to support design decisions, suggesting that these disciplines do not "naturally occur" in robotics without deliberate instructional intervention. Recommendations for Practitioners Recommendations for Researchers Pedagogical design should intentionally scaffold Science and Mathematics integration, incorporating scientific justification and mathematical modeling into project milestones. Introductory workshops and project showcases are recommended to address the "beginner's gap." Researchers should use a mixed-methods approach to gain a holistic understanding of STEM integration, capturing student perceptions and experiences to provide vital context to quantitative findings. Impact on Society This research provides a replicable, evidence-based model for implementing CBL in K-12 robotics to develop critical 21st-century skills. More importantly, it offers a crucial diagnostic insight for STEM educators: the common pitfall of unbalanced disciplinary integration. Future Research Use experimental designs, diverse samples, and quantitative studies to assess CBL's effects on STEM integration. Longitudinal studies are also recommended to track students' skill development.
Aim/Purpose To examine how chemistry teachers' content, pedagogical, and technological knowledge (CK, PK, TK) and their intersection domains, pedagogical content knowledge (PCK), technological content knowledge (TCK), technological pedagogical knowledge (TPK) interact within the Technological Pedagogical and Content Knowledge (TPACK) framework when integrating augmented reality (AR), with a particular focus on the mediating roles of PCK, TCK, and TPK in predicting overall TPACK. Background AR is increasingly used to visualise abstract and sub-microscopic chemistry concepts, yet little is known about how in-service teachers' TPACK is structured in this context. Prior TPACK studies have largely treated TPACK as a global construct or have replicated the original model with conventional technologies, offering limited insight into how intersectional domains function as pathways to TPACK in AR-enhanced chemistry teaching. Methodology A cross-sectional survey was administered to 337 in-service chemistry teachers who had prior experience using AR in their lessons. A validated TPACK questionnaire was contextualised to AR and reviewed by experts. Data were analysed using structural equation modelling to test a seven-factor measurement model and to estimate direct and indirect effects among CK, PK, TK, PCK, TCK, TPK, and TPACK. Contribution The study refines the TPACK framework for AR-enhanced chemistry teaching by modelling mediating relationships among the intersection domains. It moves beyond confirmatory use of TPACK and shows which knowledge components actually function as leverage points for strengthening teachers' TPACK in an immersive-technology context. Findings The seven-factor TPACK structure demonstrated acceptable reliability and validity. CK and PK strongly predicted PCK; CK and TK predicted TCK; and PK predicted TPK. PCK, TCK, and TPK all had significant positive effects on TPACK, with TPK emerging as the strongest direct predictor. Mediation analyses showed that PCK and TPK are the main pathways through which CK and PK influence TPACK, whereas TCK selectively mediated the effect of CK. TK did not exhibit strong direct or indirect effects on TPACK. Recommendations for Practitioners Recommendations for Researchers Professional development should prioritise the design of AR-supported chemistry lessons that explicitly target PCK and TPK-topic-specific pedagogy and pedagogy-technology integration, rather than focusing primarily on tool operation. Teachers need support in orchestrating AR activities, aligning them with curricular goals, and managing cognitive load and classroom interaction. Researchers should continue to model TPACK as a network of interacting components, paying particular attention to intersection domains and mediation effects. Instruments should be further refined for AR-specific contexts and complemented with classroom observations and artefact analyses. Impact on Society By clarifying how teachers' knowledge supports meaningful AR integration in chemistry, the study can inform professional development programmes and policy initiatives that seek to improve students' understanding of complex scientific concepts, especially in systems where resources for laboratory work are limited. Future Research Future studies should employ longitudinal or intervention designs to track changes in PCK, TCK, TPK, and TPACK during AR-focused professional development; extend the model to additional variables such as teacher beliefs and institutional support; and compare patterns across subjects, educational systems, and different types of AR applications.
Aim/Purpose This study investigates how users' behavioral intention to adopt AI-powered learning platforms in Thailand is influenced by both traditional acceptance factors and organizational strategic capabilities, addressing the limited understanding of adoption determinants in rapidly evolving educational AI ecosystems. Background While substantial research has examined behavioral factors affecting technology adoption, less attention has been given to how users' perceptions of organizational strategic capabilities and trust in AI affect their adoption decisions. This study addresses this gap by integrating the UTAUT2 framework, dynamic capabilities theory, and trust perspectives into a unified model specifically contextualized to Thai educational settings. Methodology Data were collected from 1,368 Thai users (63.9% students, 14.0% teachers/faculty) through online and offline questionnaires. PLS-SEM was employed to test the proposed structural model, with multi-group analysis examining differences across user experience levels, occupations, and usage frequencies. Findings All hypothesized relationships were supported, with the integrated model explaining 63.4% of the variance in behavioral intention. Strategic capabilities significantly influenced both traditional adoption factors and direct adoption intention. Trust in AI demonstrated dual pathways of influence: direct effects and enhanced perceptions of platform adaptability. Contribution This study extends existing adoption theories by demonstrating how strategic capabilities (sensing, seizing, reconfiguring) influence both direct adoption intention and traditional acceptance factors. It establishes a novel dual pathway of trust influence and develops a comprehensive framework that bridges individual-level behavior with organizational-level strategic readiness. Recommendations for Practitioners Educational institutions should prioritize user-centered design (emphasizing ease of use and social integration) and demonstrate strategic adaptability through transparent roadmaps and responsive feature development. Building trust through ethical governance frameworks and clear data practices is essential for sustainable adoption. Impact on Society Understanding the complex interplay can help institutions implement AI technologies more effectively. This potentially addresses educational inequities, supports linguistic diversity, and scales quality learning experiences across diverse contexts. Future Research Future research should explore cultural and institutional moderators of strategic capability influence, examine longitudinal adoption patterns as AI technologies evolve, and investigate how strategic capability perceptions form across different user demographics and educational contexts. Cross-cultural validation of the integrated model across countries with varying philosophies would enhance generalizability.
Aim/Purpose This study employs the Stimulus-Organism-Response (SOR) framework to investigate the psychological drivers of learners' continuance intention toward AI tutors. The study explores how five core service quality dimensions (aesthetics, control, personalization, responsiveness, and reliability) shape learners' internal states, conceptualized as a dual system consisting of rational attitude and experiential engagement. The study further examines how these internal states foster satisfaction and subsequently influence continuance intention, while also analyzing the asymmetrical moderating role of perceived risk on these psychological pathways. Background AI tutors are increasingly adopted in language education due to their capacity to deliver scalable, adaptive, and personalized instruction, an especially relevant development in settings such as Vietnam, where language proficiency gaps persist. Despite their rapid integration into everyday learning, the mechanisms that sustain learners' long-term use remain insufficiently understood. Existing research largely relies on broad technology acceptance models that conceptualize AI systems as uniform delivery tools, overlooking their highly interactive and adaptive nature. Consequently, limited attention has been given to which specific service quality dimensions most strongly shape learners' evaluations and sustained engagement. Moreover, prior studies often assume a direct relationship between system features and continuance intention, neglecting the cognitive and experi ntial states that emerge as learners interact with AI tutors. These gaps are particularly salient in Vietnam's fast-growing digital learning environment, where high demand for effective language education converges with increasing public interest in AI-based solutions. Yet, empirical evidence on how learners assess the quality of AI tutor services remains scarce. This underscores the need for a more fine-grained examination of how distinct service characteristics influence learners' internal evaluations and their intention to continue using AI tutors, providing both theoretical precision and practical guidance for enhancing AI-supported language learning. Methodology This study utilized a quantitative, cross-sectional design via a structured online questionnaire. Data were collected through purposive sampling from 771 experienced users of AI language tutors (including high school students, university students, and working professionals) in Vietnam. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3.0 software to test the complex research model, including the proposed moderation effects. Contribution This study advances technology acceptance theory by applying a nuanced SOR framework to the AI tutor domain, deconstructing service quality into five specific, actionable dimensions. Its primary theoretical contribution is the validation of a "dual-engine" retention model (distinguishing between a rational attitude path and an experiential engagement path) and the introduction of the resilience of flow phenomenon, demonstrating that the engagement-based pathway is uniquely resilient to the negative impacts of perceived risk. The study also provides crucial empirical evidence from the under-researched context of Vietnam's emerging market. Findings The results reveal that all five service quality dimensions (aesthetics, control, personalization, responsiveness, and reliability) significantly and positively influence both learner attitude and engagement. These two internal states, in turn, cultivate satisfaction and drive continuance intention. Crucially, perceived risk demonstrates an asymmetrical moderating effect: it significantly weakens the influence of the rational pathway (attitude-* continuance intention) and the summative judgment (satisfaction-* continuance intention), but it does not weaken the influence of the "hot" experiential pathway (engagement-* continuance intention). Recommendations for Practitioners Recommendations for Researchers Developers and educators should adopt a dual defensive-offensive strategy. Defensively, they must mitigate perceived risk by ensuring pedagogical accuracy (reliability) and data transparency. Offensively, they should prioritize investments in features that drive deep, resilient engagement, as this pathway is robust to risk. Enhancing aesthetics, learner control, personalization, and responsiveness are all critical levers for building both positive attitudes and strong engagement. Researchers should validate this model across specific linguistic cohorts to test for variance. Studies should also test the model's generalizability in other cultural or educational contexts beyond the specific emerging market of Vietnam. Impact on Society By providing a strategic blueprint for designing more effective and engaging AI tutors, this study helps educational technology providers create tools that foster long-term learner commitment. This can help close persistent language proficiency gaps in emerging economies, support economic globalization, and enable transformative, scalable language acquisition for learners at all stages. Future Research Future research should employ longitudinal designs to track the evolution of engagement and satisfaction beyond the initial novelty period. Subsequent studies must also connect these service quality factors and psychological constructs to objective, tangible learning outcomes, such as measured proficiency gains, to ascertain the AI tutor's true pedagogical efficacy.
Aim/Purpose To examine how ethical awareness, cognitive appraisal (trust and perceived usefulness), digital competence, academic performance, and gender influence university students' ethical use of ChatGPT and academic integrity. Background This study explores how university students' use of ChatGPT influences academic integrity in higher education. It responds to emerging integrity challenges posed by generative AI by empirically testing a model that links transparency, plagiarism avoidance, bias awareness, and responsible use to academic integrity outcomes across universities in the Gulf region. Methodology PLS-SEM analysis of survey data from 318 students across five Gulf-region universities; tests direct effects of four ethical variables, mediation by trust in AI and perceived usefulness, and moderation by digital literacy and CGPA. Contribution This study provides empirical evidence that core ethical-use dimensions significantly enhance academic integrity; clarifies the mediating roles of trust/usefulness and the moderating roles of digital literacy/CGPA; and documents gender and discipline differences in usage. Findings All four ethical variables positively and significantly predict academic integrity: Trust in AI and perceived usefulness act as partial mediators. Digital literacy and CGPA significantly moderate several relationships. High-performing and senior students report more frequent and effective use of ChatGPT; gender and discipline differences are evident. Recommendations for Practitioners Recommendations for Researchers Embed digital literacy and ethical-AI training (transparency, anti-plagiarism, bias awareness, responsible use) into curricula; implement inclusive AI policies; and guide trust calibration and verification workflows to support responsible use. Extend the model across countries and disciplines; examine longitudinal effects; test additional mediators (e.g., AI anxiety, institutional policy clarity) and mod-erators (e.g., year of study, assessment type); compare alternative SEM and causal designs. Impact on Society Promotes responsible AI adoption that safeguards academic ethics, supports equitable student outcomes, and informs policy for trustworthy AI use in higher education ecosystems. Future Research Conduct multi-institutional replications, experimental interventions on eth-ics/digital literacy training, and studies of assessment design that balance AI use with integrity (e.g., oral/ authentic assessments).
Aim/Purpose This study examines the role of peer networks in promoting social learning to address Veo3 (a web-based artificial intelligence (AI) video generation tool) rather than common top-down formal learning interventions. Background In a top-down formal learning intervention where the instructor is only present in a workshop, the teaching methods only outline how to write prompts. The instructor has little idea about the product or culture. This limitation hinders micro-entrepreneurs' ability to create contextually appropriate advertising content while experimenting with or implementing Veo3 prompts. Methodology A mixed-methods approach was utilized, integrating post-workshop retrospective interviews with SNA facilitated by Neo4j. The research included halal food craftsmen, participants from Kuningan, Indonesia, who participated in the Veo3 advertising workshop. Coding reliability was delivered through inter-coder agreement of alpha = 0.928. We used SNA metrics, such as betweenness centrality, eigenvector centrality, and Louvain community detection, to create a quantitative map of the network structure before and after the formal top-down workshop. Contribution The primary contribution of this study lies in social learning theory, which provides empirical evidence that individuals acquire influence and knowledge through participation and collaboration in a peer-driven, participatory learning ecology rather than through a top-down pre-workshop learning ecology. Findings The post-workshop peer-driven learning ecology encountered significant trans-formation in comparison to the pre-workshop learning ecology. The results support the following main hypothesis: (1) influence shifted from hierarchical figures (high betweenness) to active collaborators (high eigenvector centrality); (2) a core participatory sub-community emerged, while non-active participants were peripheral; and (3) this restructured network directly enabled sophisticated, iterative, and culturally grounded AI creation workflows among artisans. Recommendations for Practitioners Recommendations for Researchers Learning designers must prioritize making "community share abilities" partici-patory design that requires peer interaction before central instruction. Practi-tioners should design collaborative tasks that generate practice-based network edges, connect learners directly to institutional resources, and monitor network health using centrality metrics to identify structural vulnerabilities. Researchers need to examine the long-term impacts of these network structures on the resilience and cultural preservation of individual learners, as well as how this peer-driven learning ecology may enhance the workflows of advanced cul-turally grounded AI creation. Impact on Society When micro-entrepreneurs use Veo3 as an AI tool to preserve their culture and promote products relevant to their audience, this approach encourages individu-als' computational empowerment. Future Research In the future, researchers should (1) use longitudinal SNA to determine out how long peer-driven learning ecologies last and how they affect business re-sults and cultural preservation by examining how incremental individuals' com-putational empowerment affects them, (2) create more detailed SNA edge defi-nitions to tell the difference between types of interaction (such as "help-seek-ing" and "co-creation"), and (3) set up standard procedures for turning qualita-tive data into network parameters so that studies can be compared.
Aim/Purpose This study aims to determine whether a task-based virtual reality (VR) experience can significantly enhance users' factual understanding and cultural appreciation of a lesser-known archaeological site. The purpose is to evaluate how an interactive reconstruction task impacts perceived educational value and emotional engagement in a cultural heritage context. Background Many virtual reality experiences in cultural heritage are limited to passive tours, failing to leverage the technology's full potential for interactive learning. Grounded in embodied cognition theory, this study posits that active, hands-on participation is crucial for deeper learning. We explore this using the Neolithic statues of 'Ain Ghazal as a case study, addressing the need for more engaging educational models for lesser-known heritage. Methodology A task-based VR application simulating the statue-building process was developed. A mixed-methods approach was used, with 272 participants completing pre-tests and post-tests and a survey, and a subset of 41 participants being interviewed. Quantitative analysis included Wilcoxon tests and correlations, while qualitative analysis identified key user experience themes. Contribution This study's key contribution is a novel, task-based approach to immersive learning in cultural heritage. It highlights the educational effectiveness of VR in both cognitive (factual learning) and affective (cultural appreciation) domains, offering evidence-based design considerations for future heritage-focused VR applications. Findings The task-based VR experience led to a significant improvement in factual knowledge (Z = -12.498, p < .001). A strong, positive correlation was found between cultural appreciation and learning outcomes (r = 0.582, p < .001). Per-ceived authenticity was also a strong predictor of the experience's educational effectiveness (r = 0.549, p < .001). Key interview themes included "Learning by Doing" and "Cultural Appreciation." Recommendations for Practitioners Museum professionals and educators should design interactive, task-based VR experiences that allow users to "learn by doing." To maximize educational im-pact, practitioners must carefully balance archaeological authenticity with user-centered design, as this balance directly fosters both factual understanding and cultural appreciation. Recommendations for Researchers Further research should investigate how specific VR affordances, such as inter-activity, embodiment, and sensory fidelity, impact presence, knowledge reten-tion, and cultural empathy across different heritage contexts. Impact on Society By making underexplored heritage accessible and engaging, task-based VR can strengthen cultural identity, support preservation efforts, and create new, mean-ingful opportunities for museums and tourism. Future Research Future studies should examine long-term knowledge retention from task-based VR, explore the role of creative freedom within these experiences, and assess their impact on diverse global audiences.
Aim/Purpose This study examined the factors influencing students' continued intention to use LLM-based AI chat systems in higher education, addressing the limited research on sustainable AI adoption in learning contexts. The model integrates the Information Systems Success Model (ISSM) within the Stimulus-Organism-Response (S-O-R) framework, with Information Quality, Service Quality, System Quality, and Time Saving as stimuli, Satisfaction and Trust as organism variables, and Intention to Reuse as the response. Background Artificial Intelligence (AI) has become an integral part of higher education in the era of the Fourth Industrial Revolution, primarily through Large Language Model (LLM)-based chat tools such as ChatGPT, Gemini, and Microsoft Copilot. These tools can transform the way students learn. Indonesia ranks sixth among global users of ChatGPT, indicating a strong interest in AI-based learning technologies. However, despite this rapid adoption, maintaining students' continued engagement and trust in AI chat systems remains a significant challenge. Existing studies have primarily focused on initial adoption, leaving a limited empirical understanding of the psychological and system-related factors that sustain continued usage in developing country contexts. Methodology This study adopted a quantitative approach using an online survey distributed to university students across three Indonesian cities: Surabaya, Makassar, and Semarang. A total of 432 valid responses were analyzed after data screening for outliers using Z-scores. Validity and reliability were tested through confirmatory factor analysis, Cronbach's alpha, and composite reliability in SPSS. Structural Equation Modeling (SEM) using AMOS was then applied to examine causal relationships among constructs and assess model fit. Contribution This study extends the application of the Stimulus-Organism-Response (S-O-R) framework to the educational domain, specifically in the context of repeated use of LLM-based AI chat systems. The novelty of this research lies in the inclusion of System Quality and Time Saving as stimulus variables. The mediating role of Satisfaction and its influence on Trust and Intention to Reuse further supports and strengthens findings from previous studies. Findings The findings revealed that all four stimuli, Information Quality, Service Quality, System Quality, and Time Saving, affected Satisfaction, which subsequently enhanced Trust and strengthened students' intention to continue using LLMbased AI chat systems. Among the observed pathways, the effect of Trust on continued use was the strongest. These results underscore that both technical quality and the psychological dimensions of student satisfaction and trust served as critical foundations for sustaining the integration of LLM-based AI chat technologies in academic settings. Recommendations for Practitioners Recommendations for Researchers Developers of LLM-based AI chat systems should ensure the provision of accurate, relevant, and easily comprehensible information that supports critical thinking skills. System quality must be enhanced through fast response times, user-friendly interfaces, and reliable access. Services should be personalized to align with students' profiles and include time-saving features such as content summarization. Researchers examining the application of LLM-based AI chat systems are encouraged to explore a broader range of variables across the stimulus, organism, and response dimensions. Incorporating alternative theoretical frameworks and potential moderating factors could further enrich the analysis, offering a more comprehensive understanding of the determinants of sustained usage intentions toward LLM-based AI chat platforms. Impact on Society The utilization of LLM-based AI chat systems can enhance learning effectiveness, accelerate information access, and foster greater student independence. These benefits contribute to strengthening the quality of higher education and improving readiness for the demands of the digital era. Future Research Future research is recommended to include a larger proportion of postgraduate students (Master's and Doctoral levels) to enhance academic diversity, and to expand the study area using a longitudinal design to capture long-term trends. Additionally, exploring participants from professional certification programs may offer valuable insights for further investigation into the sustained intention to use LLM-based AI chat systems.
Aim/Purpose This paper examines the effect of using Quizizz on students' learning motivation and test anxiety in an EFL classroom in Indonesia, employing Keller's ARCS model of motivation as the theoretical framework. Background Engaging students in the learning process can be challenging, particularly in the current environment where many students rely heavily on digital devices outside the classroom. Therefore, the education sector faces a new challenge and needs to adapt to new demands, modifying programs to better meet the needs of learners. Recently, interest has increased in researching learning approaches that can motivate students to learn, such as gamification. Methodology This study employed a quantitative approach, utilizing numerical values obtained from the survey to explain and clarify a particular phenomenon being observed-62 EFL students who completed four learning sections incorporated with Quizizz as a gaming tool. After the final session, a questionnaire was distributed to all students to gauge their perspective on using Quizizz in the classroom. The data were analyzed using SEM-PLS with SmartPLS4 software. Contribution This study contributes to several aspects in the educational field, such as the theory testing of the ARCS model by Keller in EFL, the effect of gamified assessment by Quizizz on students' motivation and test anxiety, providing an insight into gamified assessment usage for language learning, as well as recom-mendations for stakeholders to integrate gamification with their teaching and learning activities. Findings Two research questions and eight hypotheses were proposed to guide the direc-tion of this study. The analysis showed that five out of eight hypotheses are supported. Confidence, relevance, and satisfaction were found to have a posi-tive effect on students' motivation, while test anxiety was influenced by stu-dents' attention and confidence levels. Several factors may explain these find-ings, including students' digital literacy and specific features of Quizizz, as well as the application's competitive elements. Recommendations for Practitioners Recommendations for Researchers The findings suggest that teachers can effectively incorporate Quizizz as an as-sessment tool in their classrooms, as it enhances student motivation. While gamification can enhance motivation, it may not always reduce anxiety, particularly if competitive elements heighten stress. Therefore, educators need to de-sign gamification strategies carefully to maximize motivation while minimizing test anxiety. Researchers in this area can focus on the qualitative approach to understanding external factors that may exist and affect their motivation and test anxiety. In-struments such as interviews can gain an in-depth understanding of students' perceptions that numerical data alone cannot explain. Impact on Society The study's outcomes suggest that through gamification, students can benefit from enhanced engagement and motivation, leading to improved academic performance. This study offers empirical, model-based evidence that can help poli-cymakers, curriculum designers, and institutional leaders understand how to implement gamified digital tools, such as Quizizz, to enhance learning experiences. Future Research Future research should explore additional factors, such as self-regulation skills and coping mechanisms, and investigate familiarity to understand better how gamification interacts with students' emotional experiences in learning settings.
Aim/Purpose The study investigates the factors influencing the acceptance and utilisation of large language models (LLMs) (predictor variables of LLM usage), such as ChatGPT, in Learning design by instructional designers and university-teaching academics from various countries. Background Large language models (LLMs) have exploded onto the scene, transforming the landscape of learning design. Instructional designers and university teaching academics have been overburdened with content creation for their teaching programmes, and the arrival of LLM models will help in this regard by developing more interactive content that drives student engagement and, in turn, contributes to student success. Since LLMs are a relatively new phenomenon, little is known about the factors influencing their acceptance in learning design; therefore, this research is needed, as learning design principles are the bedrock of student engagement and success. Methodology A cross-sectional correlational quantitative study was employed. Data was collected using an online questionnaire posted on social media, including LinkedIn, from 203 instructional designers and university teaching academics. Purposive and snowball sampling methods were used to target instructional designers and university teaching academics at colleges and universities worldwide. Participants were asked to share the survey link with fellow instructional designers and university-teaching academics in their communities. The factor structure of the data was determined using exploratory factor analysis. Nonetheless, the factor structure derived from the LLMs did not entirely reflect the original configuration of the Unified Theory of Acceptance and Use of Technology (UTAUT3), as certain predictors appeared to coalesce, indicating LLMs' unique nature in learning design. Confirmatory factor analysis was used to verify the fit of the data on the measurement model. First-order and second-order structural modelling were used to identify the structural relationships among the variables. Contribution The study determines significant factors for the acceptance of LLMs by instructional designers and academic teaching staff in learning design, enabling possible opportunities for best practices in the field through interventions to optimize LLM usage. The study applies the technology acceptance model to the emerging LLM technology and extends the technology acceptance model by adding the trust construct as a predictor variable. Findings The structural analysis results indicated that the ingrained LLM practices, LLM peer-driven expectations, innovative propensity towards LLM adoption, reliability and provider trust in LLMs, and ease of use and support influenced perceived LLM benefits and usage, but community standards and infrastructure had no influence. The second-order structural equation modelling indicated that perceived LLM benefits and usage and ingrained LLM habits contributed most to the learning design. Recommendations for Practitioners Recommendations for Researchers Teaching academics and instructional designers must use LLMs in designing content, assessments, and interactive learning activities, and attend LLM training workshops on prompting and best practices in integrating LLMs into learning and teaching to see their benefits; hence, regular use of LLMs will then lead to trust and innovation in LLMs usage, enhancing learning design and improving student learning outcomes. Researchers must use mixed methods approaches to have a deeper understanding of the factors influencing LLMs. Since habit and perceived LLM benefits and usage contributed the most variance to learning design, researchers must investigate strategies that optimise these factors in learning design, such as effective intervention strategies that can help form positive LLM habits. In addition, the findings provide researchers with a starting point for future research. Further researchers must investigate interventions that optimise the influence of personal innovativeness and trust that contributed the least variance to learning design, hence unlocking the potential of LLMs in learning design through innovation, responsible, and ethical use. Impact on Society The use of LLMs in learning design has a high possibility of transforming education, specifically the learning design landscape. Using LLMs will free up more time for teaching academics and instructional designers so that they spend more time on higher-order thinking skill demands. Consequently, the students will be exposed to more engaging and interactive content, resulting in improved learning outcomes. Future Research Future research must include context-derived external variables in technology acceptance models, such as levels of prompting competencies, to provide a deeper understanding of LLMs. In addition, future research must be based on the application and impact of LLMs on student engagement and success, and their attainment of 21st-century skills.
