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<p>With the goal of improving the teaching effect of English in colleges and universities, this paper combines artificial intelligence technology with teaching and constructs an English-intelligent tutoring teaching model. By analyzing the main forms of artificial intelligence in the field of teaching, the advantages of intelligent teaching mode in college English teaching are explored from personalized teaching as well as teaching effect. Combining the RBF neural network to analyze the students’ learning data, the function approximation principle and interpolation method are used to improve the accuracy of the analysis of the students’ data. Using the form of network topology, the information transfer process in English intelligent teaching is explored. To improve the classification of students’ abilities and the prediction of their grades, judgment trees are added to the network. The English Intelligent Assisted Teaching model was applied to teaching to explore its feasibility and effectiveness. The results show that students’ satisfaction with personalized teaching is 0.85, and their satisfaction with personalized evaluation is 0.8. The students’ translation ability under the English Intelligent Assisted Teaching Model has improved more, from 75 to 90.</p>
</abstract>

With the goal of improving the teaching effect of English in colleges and universities, this paper combines artificial intelligence technology with teaching and constructs an English-intelligent tutoring teaching model. By analyzing the main forms of artificial intelligence in the field of teaching, the advantages of intelligent teaching mode in college English teaching are explored from personalized teaching as well as teaching effect. Combining the RBF neural network to analyze the students’ learning data, the function approximation principle and interpolation method are used to improve the accuracy of the analysis of the students’ data. Using the form of network topology, the information transfer process in English intelligent teaching is explored. To improve the classification of students’ abilities and the prediction of their grades, judgment trees are added to the network. The English Intelligent Assisted Teaching model was applied to teaching to explore its feasibility and effectiveness. The results show that students’ satisfaction with personalized teaching is 0.85, and their satisfaction with personalized evaluation is 0.8. The students’ translation ability under the English Intelligent Assisted Teaching Model has improved more, from 75 to 90.

Artificial Intelligence Technology Boosts the Construction of English Intelligent Teaching Mode in Colleges and Universities

Lanzhou Resources & Environment Voc-Tech University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the goal of improving the teaching effect of English in colleges and universities, this paper combines artificial intelligence technology with teaching...

Artificial Intelligence Technology Boosts the Construction of English Intelligent Teaching Mode in Colleges and Universities

Data publikacji: 02 gru 2023

Zakres stron: -

Otrzymano: 31 sty 2023

Przyjęty: 12 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01319

Słowa kluczowe
Artificial Intelligence, RBF Neural Network, Function Approximation Principle, English Intelligent Tutoring, Personalized Teaching

© 2023 Yang Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artificial Intelligence Technology Boosts the Construction of English Intelligent Teaching Mode in Colleges and Universities

Data publikacji: 02 gru 2023

Zakres stron: -

Otrzymano: 31 sty 2023

Przyjęty: 12 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01319

Słowa kluczoweArtificial Intelligence, RBF Neural Network, Function Approximation Principle, English Intelligent Tutoring, Personalized Teaching

© 2023 Yang Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Artificial Intelligence, RBF Neural Network, Function Approximation Principle, English Intelligent Tutoring, Personalized Teaching