Research on the construction and application of intelligent tutoring system for english teaching based on generative pre-training model

In the era of globalization and information technology, English is becoming more and more important as the main language of international communication, but the traditional English teaching model is difficult to meet the diverse needs of learners due to the uneven distribution of resources and the lack of personalized tutoring. In order to meet these challenges, this study uses a generative pre-training model to build an intelligent tutoring system for English teaching, aiming to innovate the English learning experience with the help of artificial intelligence technology and achieve personalized and efficient teaching guidance. The construction solution includes collecting data such as learners' English proficiency test scores, learning history, and self-reported learning preferences to create detailed learner profiles, integrating advanced generative pre-trained models such as GPT-based and fine-tuning with data related to English language teaching, and then automatically generating exercises based on learner profiles and dynamically adjusting the difficulty. The application of the system is reflected in the integration of natural language processing technology and generative models to provide immediate feedback after learners complete the exercises, such as analyzing the grammar, vocabulary use and coherence of English passages and pointing out mistakes, giving suggestions and explanations for corrections, as well as providing intelligent tutoring in the form of dialogues, such as examples, comparisons and related exercises to enhance understanding in response to learners' questions about grammar points. The experimental results show that compared with the traditional teaching mode, the use of this intelligent tutoring system increases the learners' progress in English listening, speaking, reading and writing by an average of 30 %, and the learning satisfaction increases by 40 %, especially in the improvement of oral expression and writing skills.

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Publication Details

Journal
Systems and Soft Computing
Published
2025-04-15
DOI
https://doi.org/10.1016/j.sasc.2025.200232
Citations
7
Primary Topic
Educational Technology and Pedagogy
Type
article
Field-Weighted Citation Impact
17.37
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article

Research on the construction and application of intelligent tutoring system for english teaching based on generative pre-training model

Yi Du, Weiguo Feng, Xiaoling Lai, Xin Zhang et al.
7 citations
Systems and Soft Computing
Educational Technology and Pedagogy
17.37
article

Research on the construction and application of intelligent tutoring system for english teaching based on generative pre-training model

Yi Du, Weiguo Feng, Xiaoling Lai, Xin Zhang, Xiaojing Fan
article en
7 citations

Abstract

In the era of globalization and information technology, English is becoming more and more important as the main language of international communication, but the traditional English teaching model is difficult to meet the diverse needs of learners due to the uneven distribution of resources and the lack of personalized tutoring. In order to meet these challenges, this study uses a generative pre-training model to build an intelligent tutoring system for English teaching, aiming to innovate the English learning experience with the help of artificial intelligence technology and achieve personalized and efficient teaching guidance. The construction solution includes collecting data such as learners' English proficiency test scores, learning history, and self-reported learning preferences to create detailed learner profiles, integrating advanced generative pre-trained models such as GPT-based and fine-tuning with data related to English language teaching, and then automatically generating exercises based on learner profiles and dynamically adjusting the difficulty. The application of the system is reflected in the integration of natural language processing technology and generative models to provide immediate feedback after learners complete the exercises, such as analyzing the grammar, vocabulary use and coherence of English passages and pointing out mistakes, giving suggestions and explanations for corrections, as well as providing intelligent tutoring in the form of dialogues, such as examples, comparisons and related exercises to enhance understanding in response to learners' questions about grammar points. The experimental results show that compared with the traditional teaching mode, the use of this intelligent tutoring system increases the learners' progress in English listening, speaking, reading and writing by an average of 30 %, and the learning satisfaction increases by 40 %, especially in the improvement of oral expression and writing skills.

Systems and Soft ComputingVol. 7
Hebei University of Economics and Business (CN), Hebei University of Science and Technology (CN), Hebei University of Architecture (CN), Hebei GEO University (CN)
Quality Education
Openalex Percentile: Top 1%
Educational Technology and Pedagogy
17.37
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