Frontiers in Education: Digital Learning Innovations

ChatGPT empowers instructors to provide interactive, individualized attention and enhance student engagement. It is used to understand the learners so that the teaching materials and assessments can be contextualized. ChatGPT can enrich the learning experience, motivate the learners, and improve academic performance. No study in Saudi Arabia surveyed law educators on the intention to use ChatGPT. To fill the gap in this area, this research investigated the intention of law educators to use ChatGPT. To achieve the research objective, the researcher used a survey method to collect information from law educators in Saudi Arabia. The research revealed that law educators will use ChatGPT in legal education as the constructs of the performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention are found to be significant. The finding has policy, practical, and theoretical implications. The finding can be used to understand the factors that influence ChatGPT adoption by law educators. Accordingly, teaching and learning policies can be strengthened, and the learning institutions can introduce training for the proper and acceptable use of ChatGPT in legal education. The research also expanded the technology adoption model to understand the intention to use ChatGPT among law educators in a developing country.

Despite the increasing integration of technology in education, digital inequities persist among ESL students, particularly in under-resourced academic settings. This explanatory mixed-methods study examined digital equity among undergraduate ESL students in the English Department of an Afghan public university, drawing on data from 78 questionnaires and six in-depth interviews. Thematic and statistical analyses revealed that while students reported high access to personal technological devices for language learning, they faced significant constraints in accessing computer laboratories and developing digital English learning materials. Additionally, limitations were observed in their engagement with technology-enhanced educational research. The findings indicated no statistically significant correlations between digital equity and gender, academic year, or socioeconomic status. Both students and instructors encountered these challenges, underscoring the urgent need for institutional strategies to mitigate digital disparities in higher education. Ultimately, the study concluded with several pedagogical implications.

IntroductionRemote teaching often feels unnatural and restricted compared to on-site lectures, as traditional teaching aids are reduced to a 2D interface. The increasing adoption of VR expands online teaching platforms by offering new possibilities for educational content and enables teachers to teach more intuitively. While the potential of virtual reality (VR) for learners is well-investigated in the academic literature, VR tools for educators have hardly been explored. In this paper, we introduce the tool VRTeaching, a platform designed for presenters to enable immersive lectures using VR glasses and integrated tools such as an interactive whiteboard that can display slides, built-in chat integration to enable communication, and interactivity features such as polling tools or audience questions.MethodsThe presented study includes an expert evaluation assessing the usability and the potential of the teaching and learning platform and an investigation of the mental demand and psychophysiological responses on teachers and students giving presentations depending on the teaching environment.ResultsThe results indicate a significantly higher mental demand for the VR environment than the online environment, with no significant effects on the psychophysiological measures.DiscussionDespite the increased perceived mental demand, participants recognized the VR lecture room as having strong potential for enhancing teaching and learning experiences. These findings highlight the potential of VR-based platforms for remote education while underlining the importance of considering cognitive load aspects in their design.

IntroductionVirtual learning systems (VLS) have become increasingly significant in agricultural education, especially for enhancing accessibility and flexibility. However, their effectiveness in improving learners’ engagement, satisfaction, retention, and overall outcomes remains uncertain, particularly within the Indian agricultural education context.MethodologyA cross-sectional study was conducted among 400 students from Undergraduate (UG), Postgraduate (PG), and PhD programs across randomly selected agricultural universities. Effectiveness Index was constructed using entropy method. Multiple linear regression analysis was employed to identify key predictors.ResultsThe findings indicate that 50.5% of students perceived a medium level of VLS effectiveness. Postgraduate and PhD students reported higher engagement and satisfaction than UG students. Self-regulation was the most significant predictor of learning effectiveness, followed by learners’ attitudes and e-learning design. Gender differences were also observed, with female students performing better in virtual learning environments.Discussion and conclusionThe study highlights the critical role of self-regulation, positive learners’ attitudes, and well-structured e-learning design in enhancing the effectiveness of virtual learning. These insights can inform the development of strategies aimed at optimizing virtual platforms for agricultural education.

BackgroundDeep learning (DL), a subset of machine learning and artificial intelligence (AI), is transforming engineering by addressing complex problems with innovative solutions. Despite its growing influence, a comprehensive review of current trends, applications, and research gaps in engineering disciplines is essential to understand its full potential, limitations, and potential educational implications.PurposeThis study systematically explores the state, trends, and future directions of deep learning applications in engineering, and potential educational implications. The primary research question is: “What are the current applications, trends, and research gaps in the use of deep learning across engineering disciplines, and how can these insights guide future innovations in engineering practice?”MethodA systematic literature review (SLR) was conducted in three phases: identification, screening, and synthesis. Articles were retrieved using the search term “deep learning + engineering” from databases like IEEE Xplore, Web of Science, and Google Scholar. After removing duplicates from an initial pool of 346 articles, abstracts and full texts were screened based on predefined exclusion criteria, narrowing the selection to 101 relevant studies. The synthesis categorized data into four themes: strategic methodologies, practical implementation, system optimization, and emerging applications.ResultsThe analysis revealed DL's significant impact on engineering disciplines, especially mechanical and electrical engineering, with applications such as predictive maintenance and automated grid management. Key trends include strategic deep learning model development, practical evaluation frameworks, and the optimization of efficiency. However, research gaps remain in scalability, model interpretability, and real-world implementation.ConclusionsThis study underscores DL's transformative potential in engineering while identifying critical research gaps and opportunities. It provides a framework for future research and industry applications, emphasizing the importance of strategic innovation and interdisciplinary collaboration to advance deep learning in engineering.