Online AI-Guided Video Extraction for Distance Education with Applications

Abstract

The pervasive applications of artificial intelligence (AI) have made an impact on the human knowledge sharing and education. In practice, a lifelong learning process is becoming an inevitable trend in the future. Although distance education has been developed for a long period, there are still problems such as low learning enthusiasm, low autonomy, and unsatisfactory learning support services. The application of AI to distance education will fundamentally affect the instruction and guidance methods, as well as the instructing content of distance education. Evolutions in distance education have brought significant changes in the instructing mode, learning strategy and support service, evaluation mode, and instructing platform of distance education. Distance education can construct an intelligent education platform to provide learners with personalized learning support. It can also offer flexible, dynamic, and convenient evaluation and on-demand learning resources with guaranteed quality. By leveraging the state-of-the-art data mining technique, we in this work design a set of simple yet effective distance education system, in the context of video conferencing. Our technique depends on the existing video surveillance network and equipment. Our technique can be applied to achieve an effective face-to-face online interaction. Meanwhile, the video surveillance network switch is optimized to enhance the network transmission and ensure the reliability of network operation. Comprehensive experimental results on the two instructing platforms based on embedded system learning and hospital nurse learning have shown the advantage of our method.