Modern education faces persistent challenges, including disengagement, inequitable access to learning resources, and the lack of personalized instruction, particularly in virtual environments. In this perspective, we envision a transformative Metaverse classroom model, the Multi-layered Immersive Learning Environment (Meta-MILE) to address these critical issues. The Meta-MILE framework integrates essential components such as immersive infrastructure, personalized interactions, social collaboration, and advanced assessment techniques to enhance student engagement and inclusivity. By leveraging three-dimensional (3D) virtual environments, artificial intelligence (AI)-driven personalization, gamified learning pathways, and scenario-based evaluations, the Meta-MILE model offers tailored learning experiences that traditional virtual classrooms often struggle to achieve. Acknowledging potential challenges such as accessibility, infrastructure demands, and data security, the study proposed practical strategies to ensure equitable access and safe interactions within the Metaverse. Empirical findings from our pilot experiment demonstrated the framework’s effectiveness in improving engagement and skill acquisition, with broader implications for educational policy and competency-based, experiential learning approaches. Looking ahead, we advocate for ongoing research to validate long-term learning outcomes and technological advancements to make immersive learning more accessible and secure. Our perspective underscores the transformative potential of the Metaverse classroom in shaping inclusive, future-ready educational environments capable of meeting the diverse needs of learners worldwide.

Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational Adaptation Multimodal) framework. This modular pipeline decomposes multimodal educational content into semantically tagged, pedagogically annotated units for regeneration into alternative formats while preserving source traceability. STREAM is designed to integrate automatic speech recognition, transformer-based natural language processing, and planned computer vision components to extract instructional elements from teacher explanations, slides, and embedded media. Each unit receives metadata, including time codes, instructional type, cognitive demand, and prerequisite concepts, designed to enable format-specific regeneration with explicit provenance links. For a predefined visual-learner profile, the system generates annotated path diagrams, two-panel instructional guides, and entity pictograms with complete back-link coverage. Ablation studies confirm that individual components contribute measurably to output completeness without compromising traceability. This paper reports results from a tightly scoped feasibility pilot that processes a single five-minute elementary STEM video offline under clean audio–visual conditions. We position the pilot’s limitations as testable hypotheses that require validation across diverse content domains, authentic deployments with ambient noise and bandwidth constraints, multiple learner profiles, including multilingual students and learners with disabilities, and controlled comprehension studies. The contribution is a transparent technical demonstration of feasibility and a methodological scaffold for investigating whether within-lesson content transformation can support personalized learning at scale.

This study tried to investigate pragmatic competence in Iran’s 2013 presidential election candidates. The data were collected from the recorded video of a live TV program at the Fararu news source by sampling the third debate. At first, the researchers employed Austin’s (1962) and Searle’s (1975) theory to extract types of speech act strategies used by the candidates. Then, the study used the Pearson chi-square formula to examine the frequency of speech acts types. Moreover, the present study compared types of locutionary, illocutionery, and perlocutionary speech acts used by candidates with the poll outcomes issued by press media, in order to achieve the assumption that speech acts might have a great effect on election’s outcomes. It found three kinds of locutionary acts: declarative, imperative and interrogative. Also, it found five kinds of illocutionary acts used by some candidates: declarative, representative, directive, expressive, and commissive. Consequently, the candidates were almost equally in using locutionary acts, but they were permanently different in illocutionary acts. Finally, in terms of perlocutionary acts, the candidates were mostly intended to get the hearers know by their assertion, explanation, clarification, argumentation, etc.

This study examined using the Metaverse as an educational tool to bridge academic learning and industry readiness in a computer design course. A paired pretest-posttest design was used to assess changes in familiarity, comfort, motivation, and perceived industry relevance among 28 senior undergraduates following a brief Metaverse-based intervention. Statistical analysis revealed significant gains in students’ confidence and readiness, with moderate to large effect sizes. While technical and accessibility challenges were noted, findings showed that even brief, simulation-based Metaverse experiences can positively impact students’ perceptions of emerging technologies. Mixed views on long-term value emphasize the need for infrastructure, faculty training, and hybrid models. This study contributes to immersive education by demonstrating the practical benefits of short-term Metaverse integration in supporting professional skill development.

The present paper investigates pragmatic competence by considering some corpora of 2013 Iran presidential debates.The Fararu news source was used for sampling third debate. The present study examined two aspects of pragmatic competence for analyzing the materials. First, Brown and Levinson’s (1987) politeness theory was used to examine the positive and/or negative politeness of each candidate’s speech. Then, the study used the Pearson chi-square formula to examine the frequency of politeness strategies used by candidates. Second, Arundale’s (2010) face theory was used to analyze criticism responses exchanges between the candidates. The researchers assumed that, following the theories, pragmatic competence might have a great effect on election’s outcomes and mitigate the threat to candidates’ face. The findings showed a statistically significant difference between the frequencies of politeness strategies used by Iran’s 2013 presidential candidates. Moreover, there was one by one relation between the mitigating of face threatening acts and face constituting strategies used by candidaes. We hope the findings could add to the body of knowledge in both pragmatics and presidential election context.