Optimization Analysis of AI Intelligent English Teaching Strategies Based on Computer Virtual Reality Technology

Abstract

The Internet of Things and other technology breakthroughs have a big impact on how English is taught (IoTV). The investigation of the actual English teaching process and the identification of student features come first. In order to use the interpolation technique with the student image-based feature detection method, we investigate the IoTV reform. As a result, we find a clever algorithm for IoTV that recognises student features more effectively. Artificial intelligence (AI) is the design technique used to create the intelligence algorithm for pupil feature recognition. A comprehensive multifunctional human-computer interaction system utilizes a variety of input and output streams. In addition to the standard computer keyboard, cursor clicking, and screen touching, the most recent speech and facial recognition technology can be employed for data input. Students learned to orally interact with the robot and act as a guide to various destinations. The Multimodal Interaction System for English Education (MMIEE) is an investigation into the use of network and artificial intelligence (AI) in teaching. Recurrent neural network (Rein RNN)-based Reinforced learning is utilized for the perpectual evaluation model to categorize the questions posed by teachers in terms of their content and kind, and to conduct experimental investigation. The results show that the proposed Rein_RNN model achieves 98.6% of accuracy in 4.3sec of mean evaluation time.