ABSTRACT Background Providing discussion summaries and examples has long been recognised as an effective strategy to enhance students' engagement in online learning environments. While prior research has primarily focused on human‐written summaries and their influence on observable learning behaviours, recent advances in large language models and artificial intelligence (AI) have enabled both the generation of high‐quality AI‐produced discussion summaries and example posts and increased scholarly attention to this AI‐generated support. However, existing research has largely examined the technical performance, and little is known about the educational impact of such AI‐generated learning support, particularly in relation to students' peer interactions and the development of social capital in online forums. Objectives This study aimed to examine the impacts of an AI‐generated summary‐driven learning design (AI‐SLD) on students' viewing engagement and bridging social capital in online discussion forums. Methods A design‐based research approach was conducted with 128 university students over an eight‐week intervention. Forum log data was analysed using social network analysis to investigate changes in students' viewing activities and opportunities for bridging social capital. Thematic analysis of student interviews was undertaken to explain the behavioural and relational patterns. Results and Conclusions The findings indicate that AI‐SLD broadened students' exposure to peer contributions, strengthened overall network connectedness, and increased the opportunities for developing bridging social capital. AI‐generated summaries functioned as navigational scaffolds, helping students locate diverse perspectives more efficiently. However, AI‐SLD did not prevent the decrease in viewing activities in later weeks, suggesting that external academic workload moderated engagement.
ABSTRACT Background With advances in technology, innovative teaching methods are being used to teach nasogastric catheterization. Aim This study was conducted to evaluate the effects of training in nasogastric catheterization, delivered via traditional and virtual reality methods, on the knowledge, skills, self‐confidence, anxiety and motivation of final‐year nursing students. Methods This randomized controlled experimental design study was conducted with 78 fourth‐year nursing students (39 in the intervention group and 39 in the control group) between March and May 2024. Written permission was obtained from the ethics committee, the institution and the students. Data were collected via the ‘Personal Information Form’, ‘Nasogastric Catheterization Application Knowledge Test’, ‘Nasogastric Catheter Application Checklist’, ‘Self‐Confidence Visual Analog Scale’, ‘Anxiety Visual Analog Scale’ and ‘Instructional Material Motivation Scale for Single‐use’. All the students were trained according to the course content. After the training, the students in the intervention group used the virtual‐reality application, and the students in the control group used it with the traditional method. After the application, the students' skills were evaluated and post‐tests were applied. The data were evaluated using chi‐square analysis, independent‐samples t ‐tests and two‐way analysis of variance with repeated measures. Results There was no significant difference in knowledge and motivation scores between the intervention and control groups ( p > 0.05). The students in the intervention group had significantly greater nasogastric catheterization skills, critical procedure step scores and self‐confidence scores and lower anxiety scores than did those in the control group ( p < 0.05). Conclusion Virtual reality was more effective than traditional methods at improving students' nasogastric catheterization skills, increasing their self‐confidence and reducing their anxiety. Therefore, the use of virtual reality to teach psychomotor skills, such as nasogastric catheterization, should be expanded. Trial Registration ClinicalTrials.gov registration number was received as NCT05815979
ABSTRACT Background The initiation of K‐12 artificial intelligence education has been led by numerous governments and institutions. However, most existing meta‐analyses have focused on the effectiveness of AI tools for learning support (AI in education; AIED), rather than AI education, and research on comprehensive evidence on how AI education improves learners' AI literacy remains limited. Objectives This meta‐analysis aims to provide an additional understanding of the future direction of K‐12 AI education by synthesising and analysing existing interventions in AI education classes to enhance learners' AI literacy and holistically discussing their effectiveness. Methods A total of 16 source articles were searched and analysed from 8 databases, considering our inclusion and exclusion criteria, yielding 57 effect sizes. These effect sizes were used to examine the effectiveness of various K‐12 AI education classes in enhancing AI literacy. Results and Conclusion The analysis showed a significant overall mean effect of AI education classes ( g = 0.892, CI: [0.548, 1.236], p < 0.001) on enhancing AI literacy. Moreover, moderator analyses by publication type, year of publication, and school level of the participants did not reveal any significant patterns, indicating that K‐12 AI education classes exhibit consistently positive effects across different contexts. The findings underscore the importance of continued research on K‐12 AI education and offer a strong rationale for its ongoing integration by policymakers across various governments. To support discussion on this topic, future studies can reference this research and investigate the topic with more published articles.
ABSTRACT Background Although researchers have conducted research on the issues of e‐learning, the current body of literature lacks a universally accepted framework. The existing body of research in this field exhibits a fragmented state of knowledge, as shown by the abundance of scattered and disconnected studies within the literature. These studies neither make substantial progress in theoretical development nor provide meaningful contributions to practical applications. Objectives The overall objective of this research is to provide an integrated view of the body of knowledge of e‐learning challenges in higher education. Based on this objective, it is aimed to: (1) investigate the descriptive aspects of the existing research on e‐learning challenges, (2) clarify important themes pertaining to challenges and identify knowledge gaps in the examined literature of e‐learning and (3) propose a conceptual framework that would set the direction for future studies. Methods A systematic literature review (SLR) is conducted to perform a comprehensive evaluation, compile the findings from prior research on e‐learning challenges in higher education, and synthesise them. TIPEC model served as the basis of the content analysis conducted in the present study. Results and Conclusion The challenges resulting from the analysis of 48 publications are categorised into the TIPEC model, which is refined and evolved into a matrix format considering the stakeholders of e‐learning. This matrix framework, formed based on categories and sub‐categories, allows the definition of each sub‐category to be made more broadly and clearly in terms of learner, instructor, institution, country, and generic. In this respect, a more inclusive and detailed theoretical framework of e‐learning challenges is developed and posed. Findings unveil 72 sub‐elements, 28 sub‐categories, and four categories of challenges, namely pertaining to ‘Technology’, ‘Individual’, ‘Pedagogy’, and ‘Enabling Conditions’. ‘Interactivity ( f = 59)’, ‘technological infrastructure ( f = 56)’, and ‘system design/development ( f = 48)’ as well as ‘motivation ( f = 48)’ are the sub‐categories of challenges that are mostly experienced.
ABSTRACT Background Computational thinking (CT) is increasingly critical in K‐12 education for fostering essential digital‐age problem‐solving abilities. Scratch, a widely adopted block‐based programming environment, has emerged as a promising platform for developing CT skills, yet a clear synthesis of instructional use remains limited. Objectives This paper presents a systematic literature review on instructional methods for teaching CT skills through Scratch in K‐12 settings. The review aims to: (1) identify the contexts and subject domains in which Scratch is used to teach CT; (2) explore the instructional strategies employed to integrate CT teaching through Scratch; (3) examine factors that influence CT learning outcomes; and (4) investigate challenges in implementing Scratch‐based CT instruction. Method Following PRISMA guidelines, a comprehensive search strategy was used to identify relevant literature, resulting in 46 included empirical studies. Studies were thematically coded and analysed to synthesise key findings. Results and Conclusions Scratch‐based CT instruction is common in primary and middle school settings. Most studies position Scratch as a standalone tool for CT teaching, followed by integration with science, mathematics, and STEM, with very few studies in non‐STEM subjects. Three categories of teaching approaches were identified, including task‐oriented, social‐oriented, and technology‐based approaches. Instructional effectiveness is influenced by student age, gender, and task difficulty. Key challenges include the unstructured Scratch interface, difficulties in teaching abstract CT concepts, and reliance on trial‐and‐error methods. The review highlights the need for scaffolded, developmentally appropriate teaching approaches and suggests expanding CT education into non‐STEM domains and diverse school levels.
ABSTRACT Background Information and communication technology (ICT) offers distinct advantages for addressing challenges in traditional experiments, such as costly sensors, insufficient equipment, and safety risks, by leveraging its immersive, interactive, and imaginative features. Driven by rapid advances in hardware and software, a growing number of ICT, including Augmented Reality (AR), Virtual Reality (VR), and various computer‐based simulation platforms, are being adopted to conduct science experiments. However, existing reviews have largely focused on identifying common features within a single type of ICT‐assisted experiment (e.g., virtual experiments), with limited comparative analysis of technical features and implementation impacts across various ICT‐assisted experiments. Objectives This review aims to compare the technical differences between various ICT‐assisted experiments and evaluate their effectiveness, and then provide a reference for selecting suitable technologies based on available resources, instructional requirements, and the intended learning outcomes of experimental teaching. Methods This review selected and analyzed 118 relevant studies from 2010 to 2024 with a systematic literature review approach, thoroughly examining the research context, technical features, and impacts on students. An additional cross‐analysis was conducted to analyze the relationship between the technology function and its effects on students. Results and Conclusions The review summarized that ICT can be classified as environmental tools (organizing experiments) and experimental tools (observation, operation, and data collection) depending on the purpose of their implementation. Additional cross‐analysis revealed that ICT serving different functions affects students differently: observational ICT mainly improves students' cognitive and affective outcomes, while operational ICT primarily enhances skill outcomes.
ABSTRACT Contributions in This Special Issue The contributions to this special issue collectively demonstrate the conceptual breadth, methodological diversity, and global relevance of IDLE as an innovative pedagogy. Across diverse educational and sociocultural contexts, the included studies extend current understandings of IDLE by examining its intersections with learner psychology, digital equity, pedagogy, identity, and emerging forms of AI‐mediated language learning beyond formal classrooms. Rather than reviewing individual articles in detail, this editorial synthesizes the shared intellectual directions that emerge across the collection, highlighting IDLE as a dynamic and context‐sensitive form of learning that both complements and challenges traditional instructional paradigms. Positioning IDLE Within the Sustainable Development Agenda Building on the contributions, this editorial further positions IDLE within the broader framework of the United Nations Sustainable Development Goals (SDGs). Specifically, it conceptualizes IDLE as a micro‐level, learner‐driven practice with potential macro‐level implications for sustainable development. Through its capacity to foster linguistic competence, digital literacy, and learner agency, IDLE may contribute to advancing key SDG priorities, including equitable access to quality education, social inclusion, and lifelong learning. In this sense, the special issue not only advances scholarship on IDLE but also foregrounds its relevance as a pedagogical and societal resource in addressing global challenges in an increasingly digitalized world.
ABSTRACT Background Digital game‐enhanced vocabulary learning (DGEVL), referring to the use of commercial off‐the‐shelf games for vocabulary learning, has attracted growing scholarly interest. Although existing studies predominantly report positive effects, previous reviews have conflated different game types, restricted their scope or overlooked the processes that mediate learning outcomes. As a result, there is limited understanding of how and under what conditions DGEVL works, leaving gaps in theory and research practice. Objectives This study aimed to examine the effectiveness of digital game‐enhanced vocabulary learning and identify the factors that facilitate or hinder its impact. Methods A meta‐analysis of 12 studies (14 samples, 765 participants) was conducted using a Bayesian random‐effects model to assess the overall impact of DGEVL. Subgroup analyses explored the influence of study, participant, intervention and assessment characteristics. To contextualise these findings, a systematic review of 25 studies, comprising 12 quantitative studies included in the meta‐analysis and an additional 13 qualitative studies, was carried out, and a thematic analysis was conducted to construct a conceptual model of the learning process. Results and Conclusions DGEVL demonstrates a strong positive effect on vocabulary learning (posterior median g within = 1.11, 95% credible interval (CI) [0.62, 1.61]; g between = 1.40, 95% CI [0.58, 2.25]), with substantial between‐study heterogeneity ( τ within = 0.56, 95% CI [0.26, 1.00]; τ between = 0.80, 95% CI [0.26, 1.68]). These effects vary depending on factors such as the length of the intervention, learner proficiency, and the type of game played. The cyclical model conceptualises vocabulary learning as a cyclical process shaped by gameplay, language interactions and metacognitive regulation. The study highlights the need for balanced assessment techniques, prolonged interventions, micro‐longitudinal data collection, comparative designs and rigorous trials.
ABSTRACT Background While existing reviews have mapped the efficacy of pre‐GenAI tools or, more recently, explored GenAI research trends and user perceptions, a comprehensive synthesis focused specifically on the pedagogical strategies and instructional designs employed in empirical studies across both eras is needed. Objectives This systematic review analyzes AI‐supported L2 writing trends and examines the integration of technology, content, and pedagogy within instructional designs, guided by the TPACK framework from 2014 to 2024. Methods 61 empirical studies were analysed. We employed a dual‐coding framework, with one lens identifying general research trends and the second using the TPACK framework to examine specific instructional design elements like theoretical grounding and learning activities. Results A dramatic surge in publications occurred post‐2022. The research landscape is overwhelmingly concentrated in formal higher education settings, with a narrow instructional focus on academic writing genres. A significant “theory‐practice” pattern was identified: while Sociocultural Theory was the most cited framework, the dominant instructional format was individual activity, suggesting a reinterpretation of the AI as a “social partner” or “More Knowledgeable Other.” Over half of the studies lacked an explicit theoretical framework. AI is used primarily for language optimization and explanation, targeting the revision and editing stages, while assessment remains focused on the final writing product. Conclusions The data reveal a disconnect between technological potential and instructional practice, evidenced by the research's narrow scope (limited to formal settings and generative capabilities), often atheoretical basis, and its focus on AI as a remedial tool.
ABSTRACT Background The growing prominence of online learning in educational contexts has underscored the critical role of cognitive engagement in shaping academic performance. Objectives To synthesise current research trajectories, this study conducted a systematic literature review spanning two decades on cognitive engagement within online learning contexts. Methods A structured framework encompassing research backgrounds, theoretical foundations, analytical methods, and existing challenges was employed to code and analyse the 98 retrieved literature. Results and Conclusions The findings indicated that the majority of studies concentrated on examining the impact of educational technology tools and a range of associated influencing factors on cognitive engagement. A notable absence of research examining cognitive engagement in online learning among primary and secondary education students was observed. The majority of existing studies focused on college students. Moreover, cognitive engagement was a complex academic concept researchers attempted to interpret from different perspectives, including motivation and self‐regulation, psychological engagement, learning strategies, Taxonomy of educational objectives, knowledge construction, and overt behaviours. Self‐determination theory, ICAP framework, and community of inquiry were the most frequently adopted in research on cognitive engagement. The self‐report scale was the most frequently employed method for data collection. The multimodal data illustrated significant potential and promise in predicting learners' cognitive engagement in online learning environments. This would enable a comprehensive analysis of students' cognitive engagement patterns and evolutionary characteristics.
ABSTRACT Background The rapid advancement of artificial intelligence (AI) is reshaping language education, with AI‐assisted language learning (AILL) offering new opportunities for personalised support and interactive engagement. However, it remains underexplored how English as a Foreign Language (EFL) learners perceive and engage with AI tools in specific learning contexts, particularly in translation tasks. Objectives This study explores Chinese EFL learners' perceptions and practices in AI‐assisted translation tasks, with a particular focus on the interplay between perceptions and practices, as well as the factors shaping this engagement. Methods The study was conducted at a provincial “Double First‐Class” university in China and involved third‐year English majors enroled in the compulsory course Advanced English , where students completed authentic Chinese‐English and English‐Chinese translation tasks using AI tools, including ChatGPT, DeepL and Wenxin Yiyan. Data were collcted from six participants through learning journals completed over one academic semester and post‐course semi‐structured interviews. Results and Conclusion The findings suggest that participants in this study tended to express generally positive orientations toward AI tools, particularly in terms of enhancing translation efficiency and providing access to diverse linguistic resources, while also identifying limitations related to cultural contextualisation, fluency and the accuracy of specialised terminology. Learners appeared to follow an emergent three‐stage pattern of engagement consisting of draft generation, multi‐tool cross‐validation and manual optimisation, reflecting a critical and strategic rather than uncritical use of AI. This process was mediated by factors including language proficiency, task complexity and the technological features of the employed AI tools. The study contributes to current understandings of AILL by offering qualitative insights into how learner agency is reconfigured in AI‐mediated translation tasks and by highlighting translation as a productive context for examining both the opportunities and constraints of AI integration. The findings also provide pedagogical implications for supporting more critical, reflective and context‐sensitive uses of AI in language learning environments.
ABSTRACT Background Teachers often lack the time and structured guidance needed to address the pedagogical and ethical challenges involved in the integration of artificial intelligence in education (AIED). Although prior research identifies these challenges, empirically grounded frameworks that help teachers integrate AIED in responsible and pedagogically meaningful ways remain limited. Addressing this gap is essential to ensure AIED enhances student engagement and higher‐order thinking skills (HOTS) rather than undermining them. Objectives This study examines how teachers' understanding and adoption of AI tools change after participating in a collaborative professional development course on ethical and constructive AI use, and how students perceive and engage with AI‐supported learning. Drawing on study findings and the ACAD meta‐theoretical framework, the study proposes design dimensions for planning AI‐supported learning. Methods The study involved 45 teachers and 266 students from three countries. Data sources included pre‐ and post‐course teacher surveys on ethical and constructive AI use; student surveys on engagement and perceived AI affordances; and qualitative analysis of 12 teacher‐designed lesson plans examining student engagement levels (informed by the ICAP framework), development of HOTS, and AI roles in planned activities. Results and Conclusions Results show that effective AI adoption is not intuitive. Teachers grounded in critical thinking pedagogy and collaborative practice communities were better able to recognise and adapt AIED's instructional value. Students reported greater engagement when AI functioned as a tutor, study companion, or Socratic opponent rather than merely a design aid. AI affordances were perceived as either challenging or encouraging depending on activity design. Based on these insights, the study proposes design dimensions for teachers to support responsible and pedagogically aligned AI integration into learning tasks.
ABSTRACT Background The integration of Generative AI (GenAI) into collaborative learning in secondary education has created new possibilities for this pedagogical approach. It has also raised concerns about its impact on team communication, particularly, for adolescent learners whose collaborative competencies are still developing. Current research offers limited understanding of how GenAI shapes team communication in educational settings. Objectives This study explores how GenAI influences team communication during group tasks and identifies key factors shaping these dynamics in secondary education. Methods A mixed‐methods design was employed with 24 secondary students (Grades 9–12) from two Hong Kong secondary schools. Data collection included written reflections from all participants and semistructured interviews with 10 students. Thematic analysis addressed GenAI's impacts on communication frequency, timeliness and quality (RQ1), while activity theory guided analysis of influencing factors (RQ2). Results and Conclusions Findings underscore GenAI's dual role as both a facilitator and a potential disruptor of team interaction in secondary education. First, the introduction of GenAI tools improves communication quality by fostering deeper understanding, generating new ideas and resolving disagreements. However, generative AI tools also significantly reduce communication frequency among team members and may introduce delays in team exchanges. Moreover, this study identifies six factors influencing the use of GenAI in team communication as perceived by students, including the perceived value of discussions, task interdependence, team familiarity, attitudes towards AI, guidelines for AI use and role clarity.
ABSTRACT Background This study investigates the overall effect of Digital Game‐Based Learning (DGBL) on mathematics achievement, with specific attention given to identifying potential moderating factors. Objectives To determine the impact of DGBL on mathematics achievement in primary education and to explore the moderating roles of game characteristics (game type, year group, duration, dimensions) and study characteristics (research design, level of integration, measurement type, and mathematics domain) on this effect. Methods A comprehensive literature search was conducted across Scopus and Web of Science databases, identifying 30 eligible studies published between 2015 and 2025. Data extraction and analysis were performed using R‐studio, with Standardized Mean Differences (SMD) calculated and meta‐regression analysis employed to assess the effect of DGBL. Results The findings indicate a significant positive effect of DGBL on mathematics achievement at the primary level. Furthermore, game type, year group, intervention duration, and game dimensions were found to significantly moderate this effect. Simulation games demonstrated the greatest effectiveness. Years 1, 2, 4, and 6 exhibited more substantial improvements. Shorter (≤ 1 day) and longer (> 7 days) intervention durations proved to be more effective. Game designs incorporating combinations of social and fiction elements yielded the largest effect sizes, while more complex combinations did not significantly impact the effectiveness of DGBL. Conclusions Educators should prioritize the use of engaging game types such as simulations, tailor DGBL interventions to specific year groups, carefully consider intervention duration, and select game designs that incorporate social and fictional elements without introducing cognitive overload. Furthermore, the level of curriculum integration acts as a significant moderator, suggesting that digital games yield optimal outcomes when embedded within regular instruction rather than used as a supplement.
ABSTRACT Background Prior studies in computer‐based feedback predominantly concentrated on feedback assigned by the program to learners, in which case learners only received feedback passively, without consideration for their preferences or autonomy. It remains unclear whether allowing learners to choose feedback types of varying complexity can improve learning. Objectives The current study examined the effects of self‐selected feedback on emotions, motivation, and learning performance. Methods We implemented a single‐factor between‐subjects design, involving 310 middle school students tasked with completing 10 multiple‐choice items on the topic of quadratic radicals. Learners either passively received program‐provided feedback with different complexities (Knowledge of Correct Response [KCR], Cue Feedback, and Explanation Feedback) or chose between any of these three feedback types after responses. Results and Conclusions The results showed that: (a) learners in self‐selected feedback condition reported higher decisional autonomy than those in the program‐provided feedback conditions; (b) learners in self‐selected feedback condition experienced more positive emotions than those in the program‐provided KCR or cue feedback conditions, and reported stronger motivation than those in the program‐provided KCR; (c) learners in self‐selected feedback condition also outperformed those in the program‐provided KCR condition; (d) in the program‐provided feedback condition, more detailed information resulted in better learning performance (explanation > cue > KCR). Implications These findings underscored the significance of allowing learners to choose feedback type to improve learning in computer‐based learning environments and provided empirical evidence to support more learner‐centered approaches to feedback design.
ABSTRACT Background Compared to paper textbooks, digital textbooks (DTs) offer greater accessibility, multimodal representations, and interactivity, which might boost personalized and social learning and greater motivation. However, past DT studies showed mixed results: more learning versus non‐significant results versus less learning. Objectives Hence, this meta‐analysis (a) determines the overall effect of DTs (vs. paper textbooks) on student motivation, and (b) identifies moderators that account for different effect sizes across the original studies. Methods We conducted a random‐effects meta‐analysis of 42 effect sizes from 27 experimental/quasi‐experimental studies of 3590 participants. Results and Conclusions Compared to paper textbooks, DTs boosted students' intrinsic motivation ( g = 0.536) but not their extrinsic motivation. DT effects were larger for (a) collectivist cultures than individualist cultures; and (b) students at higher educational levels. Implications Educators and policymakers can consider how to help students use DTs to improve their motivation, especially starting with intrinsic motivation in collectivist cultures among older students.
ABSTRACT Background The BOPPPS instructional model (Bridge‐in, Objective, Pre‐assessment, Participatory Learning, Post‐assessment, Summary) is widely used in higher education to promote structured and student‐centred learning. With the rapid advancement of artificial intelligence (AI), educators are increasingly exploring how AI technologies can support teaching and learning within this framework. However, existing research on AI‐integrated BOPPPS instruction remains scattered, and a systematic synthesis of current evidence is still lacking. Objectives This study aims to systematically review research on the integration of AI technologies into the BOPPPS instructional model in higher education and examine their effects on teaching and learning outcomes. Methods A systematic review was conducted following the PRISMA 2020 guidelines. Multiple academic databases were searched for empirical studies published between 2015 and 2025, and studies were screened using predefined inclusion criteria. Forty‐six eligible studies were included and analysed to identify the types of AI technologies used, their application across different BOPPPS stages, and their reported educational impacts. Results and Conclusions The review shows a growing body of research, particularly after 2018, integrating technologies such as intelligent tutoring systems, machine learning, virtual and augmented reality, and generative AI into BOPPPS‐based teaching. These technologies were reported to support personalised learning, increase student engagement, and facilitate data‐informed instruction, leading to improved academic performance, enhanced higher‐order thinking skills, and stronger learning motivation. Nevertheless, many studies remain small‐scale and quasi‐experimental, and challenges related to data privacy, teacher readiness, and equitable access persist. Future research should prioritise large‐scale and theory‐driven studies to strengthen the evidence base and support effective implementation.
ABSTRACT Background Artificial intelligence (AI) literacy is increasingly essential in higher education for developing digital competencies, but its impact on IT skill acquisition remains underexplored in Arab contexts. Objectives To evaluate the effectiveness of the University of Florida (UF) AI Literacy Model in enhancing IT competencies among Saudi university students, aligning with Vision 2030's digital transformation goals, and to examine factors influencing skill retention and practical application. Methods In a randomized control‐group design, 60 students aged 18–22 were assigned to either the experimental group, which completed a 10‐session AI literacy intervention, or the control group, which followed traditional IT instruction. Pre‐ and post‐intervention achievement tests measured proficiency in navigation, file management, system customization, and troubleshooting. The mixed‐methods approach also incorporated surveys, interviews, and machine learning classifiers to analyse outcomes. Results The experimental group exhibited significantly greater improvements in IT skills, with large effect sizes. Machine learning analyses identified attendance and regular tool usage as key predictors of success. High‐engagement learners showed strong retention, with performance gains aligning with DigComp 2.2 standards. However, students' ethical awareness remained limited, with only 30% spontaneously raising privacy concerns. Conclusions This study provides the first empirical validation of the UF AI Literacy Model in an Arab higher‐education context. It offers evidence of both effectiveness and skill retention conditions, providing implementation insights for Gulf state educational contexts. Curricula should include regular AI tool practice and explicitly assess ethics (privacy, bias, and transparency). Institutions should implement graded ethical checkpoints to foster digital fluency and workforce readiness.
ABSTRACT Background Unintentional drowning is a leading cause of injury and death among children worldwide, and inadequate water safety education is a significant contributing factor. Innovative technologies such as virtual reality [VR] have the potential to teach children about safety and reduce the risk of accidental injury. However, there are limited studies on VR's effectiveness to improve water safety knowledge among children, highlighting the need for research in this area. Objective This study aimed to evaluate the effectiveness of 360° VR panoramas plus traditional teaching materials compared to traditional teaching materials alone in increasing and retaining water safety knowledge, and also assessed which approach was more interesting and enjoyable for children. Methods Participants ( n = 156) in school year levels 3 to 6 were assigned to either an intervention group, trained using workbook and audio files (traditional materials) supplemented with 360° VR panoramas, or a control group, trained using only traditional materials. Knowledge and retention were measured at baseline, post‐test (4 weeks later), and at follow‐up (3 months later). Interest and enjoyment, and presence were assessed at post‐test. Results and Conclusions Both groups demonstrated significant knowledge improvements over time; however, the difference between the groups at post‐test was not statistically significant. The intervention group had significantly higher knowledge retention and interest and enjoyment compared to the control group. These findings suggest that 360° VR panoramas are an effective teaching aid to increase and retain water safety knowledge and foster interest among children. Further research is required to examine whether improvements in knowledge translate to behaviour change.
