<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The advent of informatization and intelligent systems in education holds substantial practical importance. This study focuses on evaluating the application effects of an AI tutoring model in extralinguistic learning, using a middle school as a case study. Two parallel classes were selected to design teaching experiments aimed at assessing the AI tutoring model’s efficacy by comparing students’ language performance before and after its implementation. Furthermore, factors influencing the willingness to use the AI tutoring model were identified, leading to the construction of a structural equation model and the formulation and testing of related hypotheses. Results indicate that the AI tutoring model significantly enhances students’ language learning outcomes, with a performance increase of 12.02 points post-instruction, which is 10.19 points higher than that observed in the control group. Variables such as performance expectation, hedonic motivation, willingness to communicate, and interaction with AI demonstrated a positive and significant impact on attitudes (p &lt; 0.05). They indirectly fostered a sustained desire to engage with the AI tutoring model. This research offers valuable insights for the future effective integration of AI into extralinguistic educational contexts.</p>
</abstract>

The advent of informatization and intelligent systems in education holds substantial practical importance. This study focuses on evaluating the application effects of an AI tutoring model in extralinguistic learning, using a middle school as a case study. Two parallel classes were selected to design teaching experiments aimed at assessing the AI tutoring model’s efficacy by comparing students’ language performance before and after its implementation. Furthermore, factors influencing the willingness to use the AI tutoring model were identified, leading to the construction of a structural equation model and the formulation and testing of related hypotheses. Results indicate that the AI tutoring model significantly enhances students’ language learning outcomes, with a performance increase of 12.02 points post-instruction, which is 10.19 points higher than that observed in the control group. Variables such as performance expectation, hedonic motivation, willingness to communicate, and interaction with AI demonstrated a positive and significant impact on attitudes (p < 0.05). They indirectly fostered a sustained desire to engage with the AI tutoring model. This research offers valuable insights for the future effective integration of AI into extralinguistic educational contexts.

Effectiveness Analysis and Optimization Path of AI Tutoring Models in Foreign Language Learning Applications

Applied Mathematics and Nonlinear Sciences

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

The advent of informatization and intelligent systems in education holds substantial practical importance. This study focuses on evaluating the application...

Effectiveness Analysis and Optimization Path of AI Tutoring Models in Foreign Language Learning Applications

Publié en ligne: 03 juin 2024

Pages: -

Reçu: 09 févr. 2024

Accepté: 23 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1341

Mots clés
Teaching experiments, Effectiveness analysis, Structural equation modeling, Influencing factors, AI tutoring models

© 2024 Jing Yan, published by Sciendo

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

Effectiveness Analysis and Optimization Path of AI Tutoring Models in Foreign Language Learning Applications

Publié en ligne: 03 juin 2024

Pages: -

Reçu: 09 févr. 2024

Accepté: 23 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1341

Mots clésTeaching experiments, Effectiveness analysis, Structural equation modeling, Influencing factors, AI tutoring models

© 2024 Jing Yan, published by Sciendo

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

Mots clés
Teaching experiments, Effectiveness analysis, Structural equation modeling, Influencing factors, AI tutoring models