Journal of Information Technology Education Research

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Using a qualitative content analysis approach, this study reviewed 47 published studies and research on online teaching and learning since 2008, primarily focusing on how theories, practices and assessments apply to the online learning environment. The purpose of this paper is to provide practical suggestions for those who are planning to develop online courses so that they can make informed decisions in the implementation process. Based on the findings, the authors argued that effective online instruction is dependent upon 1) well-designed course content, motivated interaction between the instructor and learners, well-prepared and fully-supported instructors; 2) creation of a sense of online learning community; and 3) rapid advancement of technology. In doing this, it is hoped that this will stimulate an on-going discussion of effective strategies that can enhance universities and faculty success in transitioning to teach online. Under current debates on the cost and quality of higher education, this study could help for the improvement of higher education and student enrollment and retention.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Aim/Purpose: The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during COVID-19 pandemic. Background: The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education “e-learning.” The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology: An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students’ perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution: The study aims to explore the quality of e-learning in higher education from the students’ perspective. The study was analyzed based on the student’s data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings: The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students between gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners: The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendation for Researchers: The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society: The study revealed that the positive results of the students’ perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvement. Future Research: There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Aim/Purpose: The purpose of this study was to explore language teachers’ online engagement during the Covid-19 pandemic in Indonesia. Four questions guided the inquiry in this study: 1) To what extent did teachers engage in online learning during the Covid-19 pandemic? 2) What challenges did teachers encounter while engaging in online learning during the Covid-19 crisis? 3) How has the suspension of face-to-face classroom meetings changed teachers’ practices? and 4) What were their hopes for the future of education in their respective regions?v Background: The sudden learn-from-home mode enacted since 24 March 2020 due to the Covid-19 pandemic has forced all schools to shift into online learning with no or little preparation in terms of internet access, teacher capacity, and student-parent readiness. All in all, the pandemic disruption has shed light on the widening digital divide that has serious implications for the human capital development in Indonesia Methodology: This case study involved 18 teachers from four regions in Indonesia. Data were collected through an online survey, weekly reflections, and interviews with the teachers. A group interview with five students for each of the 18 teachers was used as triangulation. To probe more deeply into a representative sample for a variety of attributes, the researchers then focused on four teachers for a more in-depth analysis. Contribution: Knowledge of the impacting factors on online learning engagement can aid in resolving the issues and providing equal opportunities for all students. This study highlighted that teachers in remote regions would need a more top-down intervention from education authorities and offered two recommendations to the government to overcome the widening digital divide as amplified by the current school suspension. This study presents interesting results pertaining to online learning engagement during the Covid-19 pandemic in Indonesia. Insights gained in this study would contribute to the perspective on the challenges and dilemmas faced by educators and students elsewhere while engaging in online learning. Findings: This study found an interplay of five related factors of online learning processes against five levels of engagement. Those five factors are learners, teachers’ prior exposure to online learning, technological knowledge, pedagogical knowledge, and the support system. Teachers in this study were still struggling to enhance the quality of online learning engagement. Nevertheless, given the rising awareness of the inadequacy of their online learning delivery and a renewed sense of commitment, these teachers had high hopes that they would be able to enhance their competence and improve their professional practices. Recommendations for Practitioners: This study found most teacher participants have, within a short period, enhanced their technological knowledge (TK) regardless of their prior exposure to technology. Teachers can be encouraged to integrate their technological knowledge with pedagogical and content knowledge to develop their technological pedagogical content knowledge (TPACK) by participating in the government program for teacher professional development. Recommendation for Researchers: This study involves only language teachers; therefore, future researchers are invited to involve non-language teachers so that more conclusive findings can be obtained. Impact on Society: Knowledge of the impacting factors on online learning engagement can aid in resolving the issues and providing equal opportunities for all students. More importantly, the lessons learned should enlighten educators that technology integration into sound pedagogy would transform current practices into quality learning. Future Research: Issues related to technology integration in education can still be unearthed especially because the budding insights of online learning will steadily be developing in post-pandemic realm, particularly in Indonesia.

Aim/Purpose: This systematic literature review investigates the underlying factors that influence the gap between the popularity of online learning and its completion rate. The review scope within this paper includes an observation of possible causal aspects within the non-ideal completion rates in online learning environments and an identification of recommended strategies to increase retention rates. Background: While online learning is increasingly popular, and the number of online students is steadily growing, student retention rates are significantly lower than those in the traditional environment. Despite the multitude of studies, many institutions are still searching for solutions for this matter. Methodology: A systematic literature review was conducted on 40 studies published between 2010 and 2018. We established a set of criteria to guide the selection of eligible articles including topic relevance (aligned with the research questions), empirical studies, and publication time frame. Further steps were performed through a major database searching, abstract screening, full-text analysis, and synthesis process. Contribution: This study adds to expanding literature regarding student retention and strategies in online learning environments within the higher education setting. Findings: Revealed factors include institutional support, the level difficulty of the programs, promotion of a sense of belonging, facilitation of learning, course design, student behavioral characteristics, and demographic variables along with other personal variables. The recommended strategies identified for improving student retention are early interventions, at-all-times supports for students, effective communication, support for faculty teaching online classes, high-quality instructional feedback and strategies, guidance to foster positive behavioral characteristics, and collaboration among stakeholders to support online students. Recommendations for Practitioners: Since factors within the open systems of online learning are interrelated, we recommend a collective effort from multiple stakeholders when addressing retention issues in online learning. Recommendation for Researchers: We recommend that fellow scholars consider focusing on each influential factor and recommendation in regard to student retention in online learning environments as synthesized in this study. Findings will further enrich the literature on student retention in online learning environments. Future Research: Future research may investigate various data-mining and analytics techniques pertaining to detection and prediction of at-risk students, the efficacy of student support and faculty support programs, and ways to encourage struggling students to adopt effective strategies that potentially engender positive learning behaviors.

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 recommendations for stakeholders to integrate gamification with their teaching and learning activities. Findings: Two research questions and eight hypotheses were proposed to guide the direction of this study. The analysis showed that five out of eight hypotheses are supported. Confidence, relevance, and satisfaction were found to have a positive effect on students’ motivation, while test anxiety was influenced by students’ attention and confidence levels. Several factors may explain these findings, including students’ digital literacy and specific features of Quizizz, as well as the application’s competitive elements. Recommendations for Practitioners: The findings suggest that teachers can effectively incorporate Quizizz as an assessment 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 design gamification strategies carefully to maximize motivation while minimizing test anxiety. Recommendation for Researchers: Researchers in this area can focus on the qualitative approach to understanding external factors that may exist and affect their motivation and test anxiety. Instruments 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 policymakers, 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: 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. Recommendation for Researchers: 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.

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 LLM-based 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: 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. Recommendation for Researchers: 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 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 α = 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 transformation 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: Learning designers must prioritize making “community share abilities” participatory design that requires peer interaction before central instruction. Practitioners 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. Recommendation for Researchers: 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 culturally 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 individuals’ 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 results and cultural preservation by examining how incremental individuals’ computational empowerment affects them, (2) create more detailed SNA edge definitions to tell the difference between types of interaction (such as “help-seeking” and “co-creation”), and (3) set up standard procedures for turning qualitative 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). Perceived 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 impact, practitioners must carefully balance archaeological authenticity with user-centered design, as this balance directly fosters both factual understanding and cultural appreciation. Recommendation for Researchers: Further research should investigate how specific VR affordances, such as interactivity, embodiment, and sensory fidelity, impact presence, knowledge retention, 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, meaningful 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 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 experiential 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: 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. Recommendation for Researchers: 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: 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. Recommendation for Researchers: Extend the model across countries and disciplines; examine longitudinal effects; test additional mediators (e.g., AI anxiety, institutional policy clarity) and moderators (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 ethics/digital literacy training, and studies of assessment design that balance AI use with integrity (e.g., oral/ authentic assessments).

Aim/Purpose: This study investigates the underexplored interdependence of social presence and well-being in online learning. It aims to clarify their reciprocal relationship and identify technology-enabled strategies that support both constructs, along with the associated challenges and support factors, to help educators design socially engaging and well-being supportive online environments. Background: Despite the recognised importance of social presence and well-being for engagement and collaboration in online learning, existing research largely examines these constructs in isolation, resulting in fragmented understandings of how they jointly influence learning experiences. Moreover, there is a notable lack of systematic reviews that synthesise technology-enabled strategies designed to support both simultaneously. Together, these gaps leave e-learning designers without a unified, evidence-based framework, leading to design decisions guided more by intuition than by evidence. Methodology: A two-phase design was employed: a narrative literature review to develop a preliminary conceptual framework linking social presence and well-being, followed by a systematic literature review (SLR) using hybrid inductive–deductive analysis to synthesise strategies, support factors, and challenges from empirical studies published between 2019 and 2025 across ACM Digital Library, IEEE Xplore, Web of Science, and Scopus. Contribution: This study contributes by (1) proposing a preliminary conceptual framework that advances understanding of social presence, well-being, and their mutual reinforcement, and (2) offering practical insights into technology-enabled strategies and associated challenges for holistic online learning design. Findings: Social presence and well-being are mutually reinforcing constructs underpinning engagement and collaboration in online learning. A key conceptual contribution of this study is the development of a unified framework that maps the reciprocal influence between these constructs, thereby addressing a gap in fragmented prior research. Phase 2 synthesized empirical evidence to identify six technology-enabled strategies, visualization, real-time interaction, social connectivity, personalization, proactive support, and interactive environments, that support both constructs. The study also highlights practical implementation challenges, including technical barriers, learner engagement, pedagogical alignment, ethical considerations, and staff training needs. Recommendations for Practitioners: Practitioners should intentionally integrate strategies that support both social presence and well-being, while anticipating implementation challenges, to foster meaningful connection and engagement in online learning. Recommendation for Researchers: Researchers should examine social presence and well-being as interdependent constructs to capture their dynamic interplay, providing richer insights into learners’ experiences and guiding the development of more effective online learning communities. Impact on Society: By highlighting the interdependence of social presence and well-being, this study informs the design of supportive online learning environments that foster inclusion, emotional stability, and meaningful engagement. Future Research: Future research should empirically test the preliminary conceptual model, investigate cascading effects between social presence and well-being, and evaluate how new technologies can be ethically and effectively leveraged to enhance both constructs.

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: 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.” Recommendation for Researchers: 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: 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 in- and 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: 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. Recommendation for Researchers: 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‑methods 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.