The Edu-Metaverse, a vast ensemble of different technologies, has initiated a great and unprecedented change in the field of education. This change has been effected through the following Edu-Metaverse characteristics: embodied and multimodal interaction; immersive teaching scenarios, which can accelerate learning and skill acquisition; and the emergence of AI-enabled agents. In comparison to traditional classroom teaching models, smart education is a collaborative and visual model that adopts the latest AI technologies to reach a learning outcome. However, a problem that should be considered is how a smart education model, enabled by the Edu-Metaverse, can be developed to enhance better learning outcomes for students. Such a model should highlight smart pedagogy in the context of the Edu-Metaverse, together with a smart teaching environment, multimodal teaching resources, and AI-enabled assessment. In this study, we focused on the teaching of college English to 60 students from Zhejiang Open University. We investigated the effectiveness of a smart education model, which was empowered by the Edu-Metaverse, in enhancing better learning outcomes for the students, using a combination of qualitative and quantitative research. After the one-semester-long experiment, questionnaires were sent out to complement the interview findings. It was found that the students who engaged in the smart education model in the Edu-Metaverse yielded higher scores in oral English, vocabulary and grammar, reading comprehension, English-to-Chinese translation, and writing than those who engaged in traditional instruction. Therefore, this study suggests that a smart education model enabled by the Edu-Metaverse, which is characterized by a highly immersive experience, multimodal interaction, and a high degree of freedom for resource sharing and creation can help learners to realize deep learning, develop their capacity for high-order thinking, and help them to become intelligent individuals in an online learning space. In order to facilitate this smart learning, we make the following suggestions for educational institutions: (1) teachers should improve the design of teaching scenarios, (2) teachers should focus on learning assessment that is based on core literacy, and (3) teachers’ knowledge of the architecture of the Edu-Metaverse should be enhanced.

This study explores and analyzes the specific performance of Large Language Models (LLMs) in instructional design, aiming to unveil their potential strengths and possible weaknesses. Recently, the influence of LLMs has gradually increased in multiple fields, yet exploratory research on their application in education remains relatively scarce. In response to this situation, our research, grounded in Pedagogical Content Knowledge (PCK) theory, initially formulated an instructional design framework based on mathematical problem chains and corresponding prompt instructions. Subsequently, a comprehensive tool for assessing LLM's instructional design capabilities was developed. Utilizing GPT-4, a high school mathematics teaching plan dataset was generated. Finally, the performance of LLMs in instructional design was evaluated. The evaluation results revealed that the teaching plans generated by LLMs excel in setting instructional objectives, identifying teaching priorities, organizing problem chains and teaching activities, articulating subject content, and selecting methods and strategies. Particularly commendable performance was noted in the modules of statistics and functions. However, there is room for improvement in aspects related to mathematical culture and interdisciplinary assessment, as well as in the geometry and algebra modules. Lastly, this study proposes initiatives such as LLM Prompt-based teacher training and the integration of mathematics-focused LLMs. These suggestions aim to advance personalized instructional design and professional development of teachers, offering educators new insights into the in-depth application of LLMs.