Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

August 5, 2024


Pages


DOI

Article

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

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Authors

Yanhua Wang Affiliation:
General Education School, Jinhua Polytechnic, Jinhua, Zhejiang, 321017, China.
and Cui Cui Affiliation:
College of Foreign Languages and Literature, Fudan University, Shanghai, 200433, China.


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.


Keywords

Artificial intelligence, Natural language processing, Bayesian algorithm, English teaching, 97P20


Citation

Wang, Y. & Cui, C. (2024). Practices and challenges of artificial intelligence-assisted teaching in vocational undergraduate public english classrooms. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1873

Published by: Engineering Journals

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