ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING AND LEARNING

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

This dissertation explores the emerging role of artificial intelligence (AI) technologies in English language teaching and learning. The dissertation comprises two complementary studies. The first study is a systematic review, utilizing the PRISMA model, that examines empirical research studies on the use of intelligent personal assistants (IPAs) tools in an English as a foreign language (EFL) context. It focuses on the types of IPAs implemented, the language skills the studies target, IPA effectiveness for language learners, and the challenges students encountered. The findings revealed that the most commonly utilized IPAs in the EFL context were Google Assistant and Alexa. It also highlights that IPA use helped EFL learners improve their oral, listening, and pronunciation skills in several studies. The analysis found that speaking and listening skills were the most frequently targeted in the included studies, with positive effects, as well as students’ overall positive perceptions. However, the systematic review shed light on some limitations of IPA use, including errors in detecting pronunciation, students’ accents, and other technological issues. The second study is an exploratory case study that examines three English language (EL) teachers’ usage and experiences with an automated feedback tool to support reflective teaching (RT). It investigates whether those experiences led to changes in their teaching practices and what changes were made. Data were collected from background surveys, self-reflection questions, semi-structured interviews, and automated feedback tool reports. The findings indicated that participants had a positive perception of using automated feedback to support RT, and they primarily used the automated feedback to increase their awareness of classroom interactions. The data revealed a measurable change in reducing teacher talk time and increasing student talk time for two of the teachers, while other instructional strategies showed mixed results. However, EL teachers expressed concerns regarding the accuracy of automated feedback in detecting nuanced interactions. In sum, while AI integration in these two studies showed some positive outcomes, the reported AI limitations may hinder its use due to limitations such as AI detection accuracy for diverse language classrooms. However, the two studies holistically provide insights into AI integration in English language teaching and learning, and they contribute to the growing body of knowledge on AI in language education.

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