AI-driven autonomous interactive English learning language tutoring system
Abstract
Recently, intelligent tutoring systems (ITSs) aid in assisting instructional procedures and help students traverse online learning resources adaptively. It has laid a strong foundation and has grown rapidly in the last several years. Web-based learning platforms make it easier for students from diverse backgrounds with varying requirements, interests, and traits to learn remotely and provide instant access to any subject area and learning method. An ITS is a computer-based system that imitates human tutors and its goal is to give students quick, personalized teaching or feedback usually without the need for human teacher intervention. The purpose of this study is to analyze the artificial intelligence (AI)-driven autonomous interactive English learning language tutoring system. The AI-powered approach utilized by Duolingo facilitates real-time feedback and adjusts to the rate of the student, including exercises that are specifically designed to enhance the English learning language. Through the system’s use, users gradually remember grammar and vocabulary by going over topics at suitable times. A total of 125 students participated in this study. This questionnaire was created to investigate important areas of students’ learning of the English language. Therefore, their feedback offers a more accurate depiction of their opinions toward learning English. Spell and grammar check in the essay are taken into account by a thorough scoring algorithm that is used to evaluate the user-centric system. The data was analyzed using SPSS software. The results indicate that by offering a scalable, user-centric system that adjusts to the demands of specific learners, Duolingo’s AI-based technology greatly improves the English language learning process. The AI-driven language tutoring systems are highlighted in this study, along with possible advancements in conversational AI and the integration of innovative learning technologies to improve the quality of learning.
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Article first published online: October 30, 2024
Issue published: March 2025
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