Improving Robot-Assisted Virtual Teaching Using Transformers, GANs, and Computer Vision

Introduction

In today's digital age, the field of education is facing significant challenges and opportunities. With the explosion of information and the diversification of learner needs, personalised education has become a key strategy for improving educational outcomes and enhancing learning efficiency (Raja & Nagasubramani, 2018). The optimization of personalized learning paths, which involves planning suitable learning trajectories for individuals based on their unique needs and characteristics, has emerged as a core element for improving educational quality and nurturing students' creative thinking and problem-solving abilities (Peng et al., 2019). Meanwhile, computer vision-supported robot virtual teaching assistants are gradually emerging, providing students with more interactive and personalized learning experiences (Alam, 2022). The focus of this research is to explore how to achieve more personalized learning paths through robot multimodal information fusion and decision-making technology, particularly with the aid of computer vision and multimodal information processing techniques, and apply them to the field of English education. The authors’ study aims to enhance the effectiveness of English education while fostering students' creative thinking, problem-solving abilities, and fluent English communication skills, all of which are closely related to the exploration of robot multimodal information fusion and decision-making technology.

In the realm of education, the optimization of personalized learning paths addresses some of the challenges inherent in traditional educational approaches. Conventional methods of English education often hinge on standardized textbooks and uniform progressions, potentially overlooking the unique needs of individual students. This standardized teaching model may result in variations in students' English learning outcomes, with some students potentially being overlooked and others struggling to keep pace with the curriculum. Therefore, the optimization of personalized learning paths holds significant importance in meeting the diverse English learning needs of students and improving the overall effectiveness of English education. Introducing computer vision-supported robot virtual teaching assistants brings increased interactivity and flexibility to English education. Virtual teaching assistants can engage with students in real-time, offering personalized feedback and guidance, thereby enhancing the appeal and effectiveness of English learning. Furthermore, virtual teaching assistants have the capacity to simulate diverse English learning scenarios, providing students with a more real-world English learning experience. This immersive approach contributes to the development of practical skills and fluent English communication abilities among students.