The deep integration of digital technologies like big data, artificial intelligence, and 5G is reshaping education. This study investigated a blended English teaching model within a transformative context at a vocational college. Results demonstrated that the model, by synergizing flexible online resources with interactive offline classes, significantly enhanced students' academic performance. It effectively fosters learner autonomy, supports personalized learning, and enables differentiated instruction. Furthermore, it cultivates self-directed learning skills, laying a solid foundation for lifelong learning. This research validates the model's efficacy and offers valuable insights for educators pioneering teaching innovation.

This study examines how web-enabled instructional environments influence metacognitive strategy development in preschool children. A quasi-experimental design assigned 180 children (aged 4–6) to either digital or traditional instruction groups over a six-month period. Data from observations, assessments, and tests showed that the digital environment significantly enhanced planning, monitoring, and reflection, with monitoring demonstrating the greatest improvement. Age-related differences emerged: children aged 4–5 were most responsive to gains in planning, whereas those aged 5–6 showed the greatest growth in reflection. Specific technological features produced distinct effects. Immediate feedback supported monitoring, simulations promoted planning, and replay functions strengthened reflection. Teacher-guided questioning also served as an important mediator. These findings illustrate how web-based tools can scaffold young learners' cognitive processes and provide practical guidance for designing effective pedagogical systems.

Online education in creative disciplines often misaligns with industry innovation. This study proposes an artificial intelligence (AI)-generated content-driven closed-loop model that synchronizes classroom creativity with industry demands via a unified digital platform. Codeveloped with three universities and 10 creative enterprises, the framework integrates a dual-mentor mechanism and real-time semantic tuning to establish a dynamic industry–academia ecosystem. Results show a 71% reduction in prototype iteration time, enhanced creative translation, and improved collaboration. Key scaling factors include algorithm compatibility, assessment fairness, and resource sustainability. An “open-incremental-collaborative” optimization strategy and a concrete AI governance framework covering transparency, bias mitigation, privacy, and intellectual property is proposed. This provides an empirically validated blueprint for AI-generated content-enhanced data-driven education.

Vocational physical education faces challenges including resource limitations, safety risks, and rigid teaching models. This study explores virtual reality (VR) as a web-based immersive solution to enhance instruction. Empirical results show that VR overcomes spatial constraints by simulating realistic environments—such as skiing and martial arts scenarios—and enables personalized learning through data-driven feedback. It supports a “pre-class, in-class, post-class” hybrid model, improving skill acquisition, focus, and safety. Despite limitations like user discomfort, lack of tactile feedback, and high initial costs, VR significantly boosts engagement and teaching efficiency. The study recommends policy support, teacher training, and iterative scenario design to promote the sustainable integration of VR in vocational physical education.

This research investigated the application and effectiveness of a web-enabled virtual reality (VR) environment in art-sketching courses. The study examined the pedagogical compatibility of VR technology in enhancing observational depth, interactive practice, and resource accessibility. The study designed and implemented a structured teaching framework that integrates VR support across pre-class preparation, in-class creation, and post-class extension phases. Through a controlled experiment and multi-dimensional evaluation, findings indicated that the virtual environment significantly improved students' spatial perspective, proportional accuracy, and overall learning motivation. Nevertheless, challenges related to visual fidelity, lack of tactile feedback, and potential cognitive dependence on technological aids were identified. The results suggested that web-enabled virtual environments serve as a powerful supplement to traditional methods, with their educational value best realized through a blended virtual-real instructional model and ongoing technological optimization.

This study employs the Technology Acceptance Model (TAM) to examine factors influencing graduate students' acceptance and use of ChatGPT in academic writing. Despite ChatGPT's growing presence in higher education, limited evidence exists regarding its actual adoption. A modified TAM-based questionnaire was completed by 254 graduate students from four Saudi universities and analyzed using SPSS and JASP. Structural Equation Modeling assessed both direct and indirect effects within a mediation model. Results indicate that perceived ease of use and perceived usefulness significantly shape students' attitudes and behavioral intentions toward ChatGPT. However, ease of use only impacts attitudes when mediated by usefulness. Positive attitudes were linked to stronger intentions to use the tool, yet high intentions did not always lead to actual use. Barriers such as limited skills or concerns about academic integrity may hinder this transition.

This study focuses on the “disconnection” of competencies between outstanding primary/secondary school teachers and normal students, constructing an inter - embedding framework. Through methods like theoretical analysis, model - building, regional comparison, and case studies, we explore its development logic and promotion strategies. The research reveals that they have respective advantages in teaching, technology, and classroom management. A cooperative model of task co - construction, resource sharing, and collaborative evaluation enables complementary growth. Regions have different practice paths. The Xichang case shows this model can improve teachers' and students' competencies and teaching quality. Finally, optimization strategies are put forward in terms of institutions, platforms, and ecosystems to support teacher education reform and educational quality improvement.

This study proposes an optimized automated English writing assessment model based on big data analysis techniques. The model aims to address the limitations of traditional manual grading and existing automated systems by leveraging multi-source data to enhance the assessment's comprehensiveness and accuracy. By utilizing deep learning algorithms, the model can assess multiple dimensions of writing, including grammar, content, structure, and style. Furthermore, it incorporates dynamic adaptation strategies to provide personalized feedback tailored to individual learner needs, thereby supporting continuous learning progression. Empirical validation of the optimized model demonstrates significant improvements in error correction and content evaluation accuracy across different proficiency levels. This research offers a potential solution to large-scale, efficient, and objective English writing evaluation and provides insights into the future direction of integrating big data and AI into educational technologies.

With advancements in technology, traditional college English vocabulary instruction faces challenges such as limited interactivity and student disengagement. Gamified computer-assisted instruction, which combines educational technology and psychology principles, has emerged as a solution. By incorporating game elements like points, task progression, and rewards, this approach creates an interactive and motivating learning environment. Based on constructivist and self-determination theories, this study tests a gamified framework using a self-developed platform. Results show that gamified tasks reduce resistance to learning, improve autonomous learning, and enhance collaboration. Compared to traditional methods, game mechanics increase language input/output and diversify learning approaches. This research offers practical and theoretical support for reforming English vocabulary instruction in higher education.

This study explored virtual reality (VR) technology to enhance cultural learning in heritage tourism, aligning with a focus on learning systems. Traditional models lack interactivity, but VR digitizes artifacts and historical scenarios. Through surveys (1,200 participants) and data analysis, VR improved student knowledge retention by 45% (F=8.3, p<0.01) and increased visits by 42–75%. While boosting stay time (39%) and consumption (41%), challenges include content stagnation (23% revisit decline) and hardware discomfort (55% dissatisfaction). Proposed solutions are ergonomic headsets (33–50% weight reduction), dynamic content updates, and in-scene augmented-reality commerce (27% online revenue growth in pilots). These innovations position VR as a non-formal learning tool for heritage preservation and digital education.