Practices and Challenges of Artificial Intelligence-Assisted Teaching in Vocational Undergraduate Public English Classrooms

Author:

Wang Yanhua1,Cui Cui23

Affiliation:

1. General Education School , Jinhua Polytechnic , Jinhua, Zhejiang , , China .

2. College of Foreign Languages and Literature , Fudan University , Shanghai , , China .

3. School of Foreign Languages , Wuhan City Polytechnic , Wuhan, Hubei , , China .

Abstract

Abstract This paper first introduces the architecture of the natural language processing system and then analyzes how natural language processing decomposes the probability distribution function by multi-factor form and Bayesian formula to improve the efficiency of function description. Then, NLM is applied to enhance the statistical coefficients of the N-gram model, which solves the problems of data sparsity and dimensionality disaster. By using natural language understanding in English teaching practice and experimenting with the teaching effect, it was found that the overall English achievement of the experimental group increased by 5.4 points, the average Z-score reached more than 3 points, and 96.6% of the students were interested in the teaching method. The new teaching method is demonstrated to have a significant impact on the improvement of English scores.

Publisher

Walter de Gruyter GmbH

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