Future Changes in Teachers’ Professional Roles Under the Impact of Artificial Intelligence: A Study in English as a Foreign Language Education

IF 7.4 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2025-10-20 DOI:10.1109/TLT.2025.3624050

Lihang Guan;Yue Zhang;Mingyue Michelle Gu

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Abstract

Generative artificial intelligence (GenAI) has demonstrated its benefits for students learning English as a foreign language (EFL). However, this does not mean that teachers are obsolete when artificial intelligence in education (AIED) is employed. Grounded in self-determination theory and the holistic integration approach, this study explored the significance of human agency in AIED to measure three learning outcomes: students’ intrinsic motivation, classroom anxiety, and willingness to communicate in EFL classes. Its findings suggest that students who received human-centered teacher–student–GenAI collaboration developed better in all three areas than those who only had student–GenAI interactions. Moreover, the holistic integration approach promoted teacher immediacy and teacher–student rapport that supported students’ development. In a climate where an instrumentalist view of education is prevalent and digital devices are commonly banned, this study interpreted the changing roles of EFL teachers under AIED systems, suggesting ways that students can benefit from both technological advancements and human agency in education.

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人工智能影响下教师专业角色的未来变化——以对外英语教育为例

生成式人工智能（GenAI）已经证明了它对学生学习英语作为外语（EFL）的好处。然而，这并不意味着当人工智能应用于教育（AIED）时，教师就过时了。本研究以自我决定理论和整体整合方法为基础，探讨人的能动性在辅助教学中的重要性，以衡量学生在英语课堂上的内在动机、课堂焦虑和交流意愿。研究结果表明，接受以人为本的师生- genai合作的学生在这三个领域的发展都比那些只有学生- genai互动的学生更好。此外，整体整合方法促进了教师的即时性和师生关系，支持了学生的发展。在工具主义教育观盛行、数字设备普遍被禁止的环境下，本研究解释了AIED系统下英语教师角色的变化，提出了学生可以从技术进步和教育中的人类机构中受益的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.