An Exploration of Artificial Intelligence Assisted Strategies in English Reading Teaching

In this study, the knowledge tracking model and dynamic cognitive diagnostic method are used to assist English reading teaching so as to achieve the purpose of personalized learning for students. The article carries out a research design after proposing English reading teaching strategies, and then designs an IRT-based knowledge tracking model by combining IRT and DKVMN models. It also combines CF-DKD with internal cognitive laws such as learning and forgetting with the key-value memory network, and proposes a dynamic cognitive diagnosis method based on learning and forgetting factors through two gate mechanisms to diminish knowledge memory and enhance repetitive knowledge memory. By applying the method of this paper to analyze the attribute mastery characteristics of different groups (Level A, B and C), it is found that the variance of attribute mastery probability of Level B group is larger than that of Level A and C groups. The probability of mastering each attribute in the A-level group is above 0.8, and the probability of mastering the five attributes is more balanced and has the least variation. The method of this paper is applied to a school’s English teaching experiment, selecting two parallel classes with comparable reading levels to be divided into experimental and control groups, and the results of the experiment show that the experimental group is more obvious than the traditional group in terms of the overall English reading level, and most of the scores of the traditional group are lower than 4.0, which can effectively improve the reading level of the students.