Joint Optimization of 6G Slicing and AR/VR for Teaching Quality Enhancement in Virtual Applications

Abstract

6G has promoted the connection of education, while VR/AR has visualized the education view. Now, with the advent of the educational informational 2.0 era, it is imperative for educators to embrace these novel opportunities brought by 6G and AR/VR. This is crucial to advance the level of educational informatization and enhance teaching outcomes. In this work, we propose a joint strategy based on 6G slicing and VR/AR technologies to enhance the teaching quality in virtual applications. In particular, by using the machine learning-based 6G slicing, we build the real-time and personalized AR/VR environment, in which the policy-driven AR/VR laboratory architecture aligns national strategies with academic transformation. Besides, we also introduce adaptive features to enable the dynamic AR/VR adjustment with intelligent feedback. Via comprehensive experiments and analysis, the paper shows that the transformative impact of 6G and VR/AR technology on virtual experiment teaching in higher education achieves higher performance than the state-of-the-art methods. It also suggests innovative teaching practices and directions for multi-node teaching models. Furthermore, we also enhance the personalization and adaptability of these teaching models, in which we analyze student learning patterns, identify areas of strength and weakness, and recommend tailored learning paths. The results indicate that a high teaching score is achieved with a study experience of more than 70% improved on average.