International Journal of Mobile Learning and Organisation (22 papers in press)

Regular Issues

• Delving into conceptions of using large language models in school settings for teachers with high and low AI literacy: a drawing analysis  
  by Yun-Fang Tu, Xiao-Ge Guo, Yi-Chun Lu 
  Abstract: The rapid advancement of artificial intelligence (AI) and large language models (LLMs) makes it essential to examine teachers’ perceptions of their use in instructional settings. This study employed drawing tasks and content analysis to investigate 73 Information and Communication Technology (ICT) teachers’ views on integrating LLMs and to compare differences between teachers with high and low levels of AI literacy. Teachers produced drawings analyzed with 29 coded elements and classified into six categories, followed by content analysis of their feedback. Findings showed teachers’ conceptions of LLMs were generally consistent across content, activities, and objects. Frequently mentioned responsible use of GenAI, learning environment, and interaction enhancement, improved teaching effectiveness and quality, and innovation in teaching methods. However, teachers with higher and lower AI literacy differed notably in their views on participants involved, locations, and emotional attitudes. The study highlights the instructional potential of LLMs and the importance of teachers’ AI literacy.
  Keywords: AI literacy; large language models; LLMs; LLMs-supported instruction; content analysis; drawing analysis; CLEAR framework.
  DOI: 10.1504/IJMLO.2027.10073952
   
  
• Factors influencing behavioral intention of students to adopt mobile computer supported collaborative learning (mCSCL) in resource-constrained nations: A structural equation modelling approach.  
  by Ugochi Ugwu, Natasha Anne Rappa, Kok Wai Wong 
  Abstract: Many resource-constrained nations suffer regular learning interruptions due to factors like strike, and civil unrest. A potential solution which would have been a switch to remote learning has remained unsuccessful, due to lack of necessary infrastructure. Given the situation, and the popularity of mobile devices, this study examined students’ perceptions towards the adoption of mCSCL, an aspect of mobile learning that focuses on collaboration. Mobility and collaboration (unpacked by its characteristics of positive-interdependence, individual-accountability, promotive-interaction, and use of social skills), that mCSCL focuses on were used as external variables to the Technology Acceptance Model (TAM) model. Structural equation modelling (SEM) was used to assess the connections between constructs. Overall, the findings indicate mCSCL acceptance in resource-constrained nations, particularly, amongst the sect of students who participated in this study. This imply that mCSCL may prove to be an alternative learning method in cases of learning disruptions in the region.
  Keywords: mobile learning; mobile computer supported collaborative learning; collaborative learning characteristics; Structural Equation Modelling; Technology Acceptance Model.
  DOI: 10.1504/IJMLO.2027.10074734
   
  
• Promoting critical thinking and argumentation ability for young students: A GenAI-based self-regulated argumentation approach  
  by Xinli Zhang, Zi Nie, Ruiting Huang, Yuchen Chen 
  Abstract: This study integrated the self-regulated learning strategy into generative artificial intelligence (GenAI)-based argumentation to propose a GenAI-based self-regulated argumentation (GenAI-SRA) approach and explored its effects on young students critical thinking, argumentation ability, self-regulation, and cognitive load. Seventy-two secondary school students were enrolled and randomly assigned to the GenAI-SRA or GenAI-based conventional argumentation (GenAI-CA) groups. Results disclosed that the GenAI-SRA group outperformed the GenAI-CA group in critical thinking and self-regulation. However, the two groups did not vary in cognitive load. For argumentation ability, the GenAI-SRA group showed a more complex argumentation structure. Additionally, the interviews indicated that the GenAI-SRA group better managed the argumentation process and showed a more positive argumentation attitude. This study can offer implications for argumentation learning and GenAI in education.
  Keywords: generative artificial intelligence; GenAI; self-regulated learning; SRL; critical thinking; argumentation ability.
  DOI: 10.1504/IJMLO.2027.10075053
   
  
• Advancements of robot-assisted language learning in the mobile era: a systematic review and bibliometric analysis  
  by Yun-Shan Lee, Gwo-Jen Hwang, Pei-Ying Chen 
  Abstract: This study aims to highlight the development trends and teaching strategies of Robot-Assisted Language Learning (RALL) through systematic review and bibliometric analysis. We collected articles from SSCI and SCI journals published between 2010 and 2024, resulting in 41 articles. The research findings indicated that the development trend of RALL in recent years shows significant potential. The most frequently researched keyword was 'language,' followed by 'students,' 'technology,' and 'motivation.' The instructional strategy primarily involved communication with physical robots, which facilitated a more interactive learning process and provided more realistic communication practice. We conducted a comprehensive analysis and discussion, integrating key information from research related to RALL. The main trends in RALL were visualised and identified. Additionally, this study addresses the existing research gaps in related fields, and proposes new research directions and instructional strategy suggestions in RALL. These insights can serve as valuable references for both researchers and educators.
  Keywords: robot-assisted language learning; RALL; language education; physical robots; systematic review; bibliometric analysis.
  DOI: 10.1504/IJMLO.2027.10075574
   
  
• Enhancing English language acquisition through mobile technologies: a systematic review and meta-analysis of experimental studies  
  by Erfan Hashemi, Mahdi Norouzvand 
  Abstract: Mobile-assisted language learning is increasingly recognised as an effective strategy for enhancing English language skills, yet questions remain about its overall impact and methodological rigor. This systematic review and meta-analysis synthesised evidence from 11 experimental studies published between 2015 and 2025, employing randomised controlled or quasi-experimental designs with both intervention and control groups. Results suggest that MALL interventions may lead to improve English language outcomes compared to controls, with a pooled standardised mean difference of 1.19. Traditional and AI-based MALL tools appeared to be beneficial, though traditional methods tended to yield somewhat larger gains, while AI-based tools showed more consistent outcomes. Subgroup analyses indicated benefits across university and non-university contexts, and minimal publication bias was detected. Despite these positive findings, methodological heterogeneity and limited reporting of implementation fidelity and long-term outcomes should be noted. This study provides an updated synthesis of MALL effectiveness, highlighting persistent research design and reporting challenges.
  Keywords: mobile-assisted language learning; MALL; technology-enhanced learning; AI-based learning tools; second language acquisition; language learning applications.
  DOI: 10.1504/IJMLO.2027.10076362
   
  
• Examining primary school learners’ awareness and readiness to utilise mobile devices for self-online learning  
  by Patrick Duffy Bayuong, Train Tening Bato, Muhammad Firdauz Mohd Zainal, Angela Felix Arip, Alan Gibson Ricky 
  Abstract: This study examined the awareness and readiness of rural and suburban primary school learners in Sarawak, Malaysia, in using mobile devices for self-online learning. A quantitative approach was employed using a closed-ended questionnaire administered to 100 learners (50 rural, 50 suburban). Data were analysed using independent samples t-tests and chi-square tests to compare overall awareness, readiness, and mobile learning behaviours. Findings revealed no statistically significant differences in overall awareness and readiness between the two groups, suggesting a narrowing digital divide in terms of access and basic preparedness. However, item-level analyses indicated that rural learners demonstrated greater resourcefulness and more frequent use of mobile devices for school-related tasks and educational applications. Both groups reported similar difficulties in managing digital distractions. These findings highlight that while access to mobile technology is increasingly equitable, differences in engagement and adaptive learning strategies persist. The study underscores the need for pedagogical interventions that promote digital literacy, self-regulation, and meaningful mobile learning practices across diverse primary education contexts.
  Keywords: awareness; mobile devices; primary school learners; readiness; self-online learning.
  DOI: 10.1504/IJMLO.2027.10076858
   
  
• Integrating mobile games with flipped learning to enhance word- and sentence-level strategies of classical Chinese reading  
  by Kit-Ling Lau, Quan Qian, Morris Jong, Xuan Zang 
  Abstract: This study integrated digital games into the flipped learning (FL) model to examine their effectiveness in enhancing the learning of word- and sentence-level strategies for reading classical Chinese among 114 eighth-grade students in Hong Kong. Three types of online learning activities, including online videos, Kahoot games, and mobile games, were designed for pre-class preparation, in-class activities, and post-class work, respectively, to facilitate students’ learning, application, and revision of the strategies. Reading tests, questionnaires, and interviews were used to evaluate the program’s effectiveness and to solicit students’ comments on the design of FL and digital games. The findings revealed that students performed significantly better in the reading post-test and improved their strategy use and intrinsic motivation. Students also expressed positive views on FL and the game-based activities. The study’s findings support the benefits of integrating FL with mobile games to engage students in a language-learning field traditionally dominated by teacher-centered instruction.
  Keywords: mobile game; flipped learning; reading strategy; technology-enhanced language learning.
  DOI: 10.1504/IJMLO.2027.10077191
   
  
• Learning success in the era of generative AI: a new paradigm and theoretical model for education technology  
  by Fuzheng Zhao, Gwo-Jen Hwang, Chengjiu Yin 
  Abstract: In the era of Artificial Intelligence (AI), traditional learning-support theories can no longer sufficiently meet the demands of modern learning environments, as they primarily rely on passive feedback mechanisms. To align educational practices with AI’s transformative capabilities, we propose a new theoretical framework called ‘learning success’. This framework advocates actively identifying students’ learning challenges and providing timely solutions to enhance learning quality and outcomes. The learning success theory is grounded in GenAI technologies and multimodal educational data. To establish this foundation, we first reviewed the developmental stages of educational technologies. The theory categorises goals and methods into two primary dimensions: current and future learning states. This study outlines directions for the continued development and application of the Learning Success theory, aiming to support more sustainable, adaptive, and intelligent learning environments in the AI era.
  Keywords: theory of learning success; generative artificial intelligence; GenAI; proactive intervention; educational big data; education technology.
  DOI: 10.1504/IJMLO.2027.10077554
   
  
• Mobile learning adoption in Nigerian universities among computer science students: the role of effort expectancy and social influence  
  by Olayinka Babayemi Makinde, Sunday Segun Akinbowale 
  Abstract: This research investigated mobile learning (M-learning) adoption in Nigeria, considering the roles played by the factors of effort expectancy and social influence among computer science undergraduates. A survey research with a quantitative approach was employed. The study was underpinned by the unified theory of acceptance and use of technology, and the theory of technology readiness covering 602 computer science undergraduates in their final year. A total enumeration technique was employed. A validated semi-structured questionnaire was engaged in collecting data. The study though through empirical evidence revealed that both effort expectancy (β = 0.478, t = 14.569) and social influence (β = 0.358, t = 10.897) had a positive influence on the intention to adopt M-learning; nevertheless, indicated that effort expectancy had a stronger influence than social influence. The study advocates that institutions should leverage peer networks, instructor endorsement, and family encouragement in promoting M-learning initiatives.
  Keywords: effort expectancy; mobile learning; intention to adopt; social influence; computer science undergraduates; Nigeria.
  DOI: 10.1504/IJMLO.2027.10077740
   
  
• Engaging EFL learners in effective and enjoyable learning: a gamified annotation, questioning, summarisation, and reflection approach using Duolingo  
  by Sohaib A. Al-Obaidi, Salwa M. Elakeili, Monther M. Elaish, Gwo-Jen Hwang, Mahmood H. Hussein 
  Abstract: This study explored the use of the gamified mobile annotation, questioning, summarisation, reflection (AQSR) method for teaching English vocabulary skills. 60 fourth-grade girls (M ≈ 8.5 years) Libyan students were randomly assigned to either a gamified Duolingo condition (n = 30) or to a non-gamified mobile condition (n = 30). Vocabulary was measured with pre-and post-test; motivation, engagement and collaboration were measured with a questionnaire. The group that received gamified content showed a statistically significant increase in vocabulary score from 11.73 to 25.80; paired samples t-test (29) = 8.53, p < 0.001. Motivation was also significantly higher in the control group (CM-AQSR) compared with the experimental (GM-AQSR) 001 (all p < 0.001). Neither engagement nor collaboration showed any significant differences. ANCOVA showed that group effects were significant with respect to post-test scores (F(1,57) = 33.189, p < 0.001; partial η² = 0.368). The implication is that, although a gamified AQSR approach represents a substantial improvement over rote learning for vocabulary learning, future studies should explore how features that could promote meaningful interaction and cooperation among learners can be achieved.
  Keywords: English language; gamification; learning methods; modelling.
  DOI: 10.1504/IJMLO.2027.10077890
   
  
• Smart healthcare technologies in the mobile era: shaping perspectives and attitudes from campus training to clinical nursing practice applications  
  by Chun-Chun Chang, Pei-Chi Liu 
  Abstract: The present study explored the differences between nursing staff’s and nursing students’ learning attitudes and overall perceptions in their use of smart healthcare technologies. The results indicated that both groups used physiological monitoring and data upload systems to streamline workflows and enhance service quality, and both valued technological teaching tools. Nursing students exhibited more positive learning attitudes, while nursing staff showed higher technology acceptance. Interviews revealed that nursing staff worried about users’ ability to express opinions and obtain sufficient training, emphasising the need for senior staff to have dedicated learning time and for academic institutions to familiarise students with healthcare equipment. Conversely, nursing students prioritised patient privacy and urged early integration of related courses. The study’s recommendations provide valuable insights for designing effective training programs to prepare both current and future healthcare professionals. This analysis highlights key differences and priorities that inform future educational and training initiatives.
  Keywords: smart healthcare technologies; artificial intelligence; nursing education; healthcare service.
  DOI: 10.1504/IJMLO.2027.10078147
   
  
• Human becoming theory-based virtual reality training to promote primiparous women’s positive experience and self-efficacy of vaginal delivery  
  by Chun-Chun Chang, Liang-Shiou Ou, Wan-Lin Pan 
  Abstract: Professional vaginal delivery education can help boost confidence and reduce anxiety about labour. This study utilised virtual reality (VR) training, incorporating the principles of Parse’s human becoming theory, to explore the prenatal experiences of primiparous women. This study was conducted in a regional teaching hospital in northern Taiwan, with a focus on the hospital’s vaginal delivery education course. A phenomenological research method was employed, with 20 primiparous women, who were at least 28 weeks pregnant and had provided informed consent. Data analysis revealed five main themes related to the prenatal experiences of these women: concerns about the health of the mother and baby, active participation in the course, affirming the maternal role, preparation for vaginal delivery, and enhancing self-efficacy. A key contribution of this study was the active involvement of the nurse in the VR-based vaginal delivery education course. This process enhanced the women’s self-efficacy and boosted their confidence in managing various vaginal delivery situations.
  Keywords: virtual reality; childbirth education; primiparous women.
  DOI: 10.1504/IJMLO.2027.10079503
   
  
• Gestalt Informed usability guidelines for mobile learning applications  
  by Bimal Aklesh Kumar, Sailesh Saras Chand 
  Abstract: Mobile learning applications increasingly support flexible learning. However, their effectiveness depends not only on pedagogical design but also on interface usability. Poor visual organisation can undermine learning outcomes, particularly on small screen devices. This study develops and validates a set of Gestalt informed usability guidelines specifically for mobile learning applications. A dataset of usability problems extracted from empirical evaluations of fifty-one mobile learning apps was analysed through thematic coding and card sorting to map recurring usability problems to eight Gestalt principles. Thirty-one actionable guidelines were formulated and evaluated by five usability experts, yielding strong overall validation. The findings suggest that aligning interface design with Gestalt principles can enhance mobile learning experiences. This study bridges perceptual psychology and mobile learning design, providing theoretically grounded and empirically validated guidelines.
  Keywords: usability; usability guidelines; mobile learning; Gestalt theory.
  DOI: 10.1504/IJMLO.2027.10079820
   
  
• An integrated model of motivation in mobile-assisted language learning  
  by Fatih Kurtoglu, Kubra Okumus Dagdeler 
  Abstract: The objective of this study is to examine the indirect influence of mobile assisted language learning (MALL) motivation on the behavioural intentions of English as a foreign language (EFL) learners. Specifically, the study explores the relationships among learner motivation, performance expectancy and behavioural intentions, while also considering the mediating role of learners’ perceptions of MALL (PtMALL). Data were collected through a survey administered to EFL students at a public university in Türkiye. The findings revealed that motivation toward MALL did not have a direct effect on learners’ behavioural intentions. Instead, behavioural intentions were shaped through indirect pathways, mediated by learners’ performance expectancy and their perceptions of MALL. The results indicate that performance expectancy played a more substantial mediating role in influencing behavioural intentions than perceptions of MALL, highlighting the importance of learners’ beliefs about the usefulness and effectiveness of mobile learning technologies in shaping their intention to use them.
  Keywords: mobile learning; motivation; behavioural intention; perception; performance expectancy; language learning.
  DOI: 10.1504/IJMLO.2027.10080158
   
  
• Design and evaluation of a textbook difficulty prediction model based on multidimensional feature fusions  
  by Yu Bai, Fuzheng Zhao, Chengjiu Yin 
  Abstract: Textbook difficulty assessment is crucial for optimising cognitive load and supporting personalised learning in multimodal educational environments. Current approaches remain limited by unidimensional readability metrics that focus solely on linguistic features and overlook the interactive effects of formula diagrams, knowledge depth, and structural organisation. These methods assume linear additivity among difficulty factors and therefore systematically underestimate high-cognitive-load content, particularly in STEM materials where multiple modalities co-occur. To address this gap, this study proposes a multidimensional textbook difficulty model grounded in cognitive load theory, integrating linguistic complexity, formula density, diagram-related complexity, knowledge abstractness, and structural organisation. A hybrid weighting strategy combining expert judgements with PCA-derived data-driven weights was employed, and nonlinear tree-based models were used to capture the interaction effects among features. The results show that nonlinear tree based models outperform linear baselines, with ExtraTreesRegressor achieving the best performance.
  Keywords: textbook difficulty assessment; cognitive load theory; multimodal learning materials; educational data mining; natural language processing; NLP; ensemble learning; feature fusion; nonlinear interaction effects; explainable artificial intelligence; SHAP analysis; readability assessment; STEM education.
  DOI: 10.1504/IJMLO.2028.10080284
   
  
• Measuring self-regulation of smartphone use and self-regulation of learning in university students  
  by Fabiola Saéz-Delgado, Javier Mella-Norembuena, Kendall Hartley, Yaranay López, Carolina Contreras, Andrés Chiappe 
  Abstract: Smartphones can either distract or support student learning, depending on how students manage their use. However, it remains unclear how varying levels of self-regulation influence this balance. This study aimed to: 1) determine the factor structure of the smartphone and learning inventory-Spanish version (SALI-SV) and the self-regulation of learning instruments for university students (SRL-IUS); 2) explore the relationships between their dimensions; 3) examine sex-based differences in self-regulation. An instrumental, cross-sectional correlational design was employed with 428 Chilean university students. Results showed that SALI-SV followed a three-factor model, while SRL-IUS exhibited a bifactorial structure with three specific and one general factor. The ‘mindful phone use’ dimension correlated most strongly with SRL-IUS dimensions. Women scored significantly higher in SRL-IUS, though no sex differences were found in SALI-SV. These findings offer practical insights into supporting higher education students based on their self-regulation and smartphone use behaviours.
  Keywords: self-regulation of smartphone use; SRSU; self-regulation of learning; higher education; psychometric validation.
  DOI: 10.1504/IJMLO.2027.10080326
   
  
• Design and evaluation of AI-Driven personalised learning applications: a systematic review  
  by Jashnil Kumar, Bimal Aklesh Kumar 
  Abstract: Personalised learning has become increasingly important for tailoring educational experiences to learners’ individual needs. With recent advancements, interest in AI-driven personalised learning applications has grown across educational contexts. However, there remains limited understanding of how these applications are designed and evaluated. A systematic review synthesised evidence on the design and evaluation of AI-driven personalised learning applications. N = 115 studies published between 2020 and 2025 were selected from the Scopus database following inclusion and exclusion criteria. Data were extracted on AI models, data sources, personalisation strategies, and evaluation methods. The review followed PRISMA guidelines. AI-driven personalised learning is rapidly expanding, with research concentrated in higher education, particularly computer science. The field has evolved from classical machine learning to deep learning and large language models. Most studies use real-world learner data, while evaluations emphasise learning effectiveness, engagement, and algorithmic performance. The review highlights the need for robust design and evaluation frameworks.
  Keywords: AI; personalised learning; AI-driven personalised learning; AI in education; systematic review.
  DOI: 10.1504/IJMLO.2028.10080498
   
  

Special Issue on: Engaging Students in Mobile and Intelligent Learning Environments

• From data to decisions: leveraging learning analytics for predictive insights in university admissions  
  by Kam Cheong Li, Billy Tak-Ming Wong, Mengjin Liu 
  Abstract: This paper surveys the application of learning analytics to support prediction in university admissions, summarising the patterns and trends in relevant learning analytics practices and highlighting the potential of data-driven approaches to enhance decision-making processes within higher education institutions. It covers 79 publications published from 2005 to 2024 collected from Web of Science and Scopus. The analysis focused on the types of data collected, the target variables measured, the methods used for data analysis, and the identified users of these analytics. The findings indicate that academic performance, educational background, and demographic information were the main criteria for candidate selection for admissions. Most of the studies utilised multiple data types in admissions analytics. Nearly half of the predictive models focused on binary classification of admission outcomes. The study also identifies limitations in the existing literature, particularly regarding insufficient details of data and evaluation methods. More research into the interconnections among data, variables, analytics techniques, and user profiles is recommended to further optimise predictive learning analytics in university admissions.
  Keywords: learning analytics; predictive analytics; machine learning; educational data mining; university admissions; student enrolment.
  DOI: 10.1504/IJMLO.2026.10070131
   
  
• The impact of team formation criteria on learning behaviours, experiences and outcomes in computer-supported collaborative learning: comparative study  
  by Aleksandra Kobicheva, Ekaterina Shostak, Tatiana Baranova 
  Abstract: The current paper analyses the impact of team formation criteria on undergraduate and graduate students’ learning behaviors, experiences, and outcomes in terms of computer-supported collaborative (CSCL) learning and compares the results between different groups of students. The study involved 9 groups of 3rd year undergraduate students (N=217) and 3 groups of 2nd year postgraduate students (N=57) of Saint-Petersburg Peter the Great Polytechnic university (SPbPU). According to the results gained it can be stated that the CSCL is more effective when students with a high level of academic achievements worked in cooperation with students whose level of academic achievements was insufficient. Also, it was revealed that graduate students were less influenced by team formation criteria, that can be connected with their higher level of professional training in comparison to undergraduate students. The findings of the paper contribute to the understanding team formation that can serve an important and valuable tool for forming and building an effective team.
  Keywords: computer-supported collaborative learning; CSCL; team formation criteria; learning behaviours; learning experiences; learning outcomes.
  DOI: 10.1504/IJMLO.2026.10073311
   
  
• Emerging technologies in Architecture, Engineering and Construction education: concerns shifting from curriculum renovation to industry and social impact  
  by Ying Wang, Siu Kei Lam 
  Abstract: The rapid progression of developing technologies and industries has highlighted the necessity of reforming the education system to meet the demands of contemporary society. However, the precise impact and development of emerging technologies on Architecture, Engineering, and Construction (AEC) education remain uncertain, hindering long-term implementation. To address this issue, this study conducts a comprehensive systematic review, integrating a literature review with a machine-learning-based analysis of grey literature from News platforms. The study aims to examine the current state of emerging technology adoption and its effects on AEC education, identify research gaps, and offer recommendations for future directions. The findings reveal a shift in primary focus over the past two decades, transitioning from curriculum updates to addressing broader industry and societal impacts. Additionally, the absence of established multidisciplinary collaboration among academics and different related parties in this field impedes its progress.
  Keywords: emerging technology; AEC education; network analysis; machine learning; ML; AI; grey literature.
  DOI: 10.1504/IJMLO.2026.10075170
   
  
• Acquisition of translation technology skills in blended learning: impact of bMOOC on learning outcomes  
  by Venus Chan 
  Abstract: This study explores the effectiveness of a blended MOOC (bMOOC) approach in translation technology training, combining online and onsite learning. The bMOOC included four MOOCs, face-to-face workshops, live online seminars, and guest talks, focusing on English/Chinese translation technology. Data from 67 participants, including students and working adults in Hong Kong, were collected through pre- and post-questionnaires and translation technology tests. Results showed significant improvements in participants’ self-rated language proficiency, translator competencies, and skills in using tools like Phrase, Trados, and Wordfast. Test performance also confirmed these advancements. Additionally, age and prior experience with translation technology were found to influence knowledge acquisition. The study highlights the potential of bMOOCs to integrate the strengths of MOOCs and face-to-face learning while addressing their limitations. It emphasises the importance of considering student factors, such as prior exposure and individual needs, and implementing adaptive measures for successful blended learning.
  Keywords: blended learning; blended MOOC; bMOOC; computer-aided translation; CAT; learning outcomes; machine translation; massive open online course; MOOC; translation technology competence; translation technology training.
  DOI: 10.1504/IJMLO.2027.10076806
   
  
• Virtual reality campus environment for enhancing the educational experience of preliminary year students  
  by Gege Zhang, Sannia Mareta 
  Abstract: This study explores the potential of a virtual reality (VR)-based campus tour to enhance the educational experience of preliminary year students, focusing on the University of Nottingham Ningbo China (UNNC) virtual campus environment (VCE). The VCE offers an immersive platform for freshmen to explore academic programs and practical skills, serving as an alternative to traditional orientation methods. Participants, primarily first- and second-year undergraduates, engaged in an educational interactive experience based on the VCE and completed pre- and post-VR experience questionnaires. The results revealed improvements in students’ learning experience. Additionally, 70% of students reported increased confidence in their major choice, and 57% felt that their public speaking skills had improved ‘very well’. 70% of students regarded the VCE as more effective than traditional methods, demonstrating high engagement for continued use of the VR application. Virtual reality technologies, including gesture recognition and gamified elements, were recognised as effective tools for enhancing learning outcomes.
  Keywords: virtual reality; virtual campus environment; VCE; student experience; immersive technology; virtual reality in education; digital twin.
  DOI: 10.1504/IJMLO.2027.10078487