Current Issue

ADOPTING AI TOOLS AND MOBILE TECHNOLOGY TO ASSIST COLLEGE STUDENTS IN ENGLISH LEARNING

Ching-Ying Lin

Department of Applied English, National Pingtung University, Pingtung, Taiwan

Jo-Ting Chu

Gueilai Elementary School, Pingtung, Taiwan

*(Corresponding Author): This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This study investigates the pedagogical impact of integrating Artificial Intelligence (AI) tools and Mobile-Assisted Language Learning (MALL) technologies into English-Medium Instruction (EMI) courses that focus on English for Academic Purposes (EAP) at the tertiary level. Despite sustained national and institutional efforts to improve academic English proficiency, many university students continue to struggle with essential competencies such as vocabulary development, academic reading, grammatical precision, and active classroom participation. These challenges are magnified in EMI contexts, where learners must concurrently process complex academic content and discipline-specific linguistic conventions in a non-native language. Traditional EMI instruction—often characterized by static curricula, teacher-centered delivery, and insufficient scaffolding—frequently fails to address the diverse linguistic needs, cognitive profiles, and motivational dispositions of today’s students. In contrast, AI-enhanced and mobile-supported platforms offer adaptive learning environments, real-time feedback, gamified activities, and multimodal input, aligning with principles of learner-centered pedagogy and differentiated instruction. These affordances hold promise for promoting learner autonomy, enhancing engagement, and fostering metacognitive growth in EMI classrooms. While the theoretical benefits of these technologies are well documented, there remains a paucity of rigorous empirical research assessing their actual impact on students’ language learning outcomes and classroom experiences. To address this gap, this mixed-methods study will compare the linguistic performance, in-class academic behaviors, and learner perceptions of students in AI- and mobile-supported EMI courses with those in traditional non-EMI instruction. The findings aim to inform evidence-based EMI curriculum design, digital pedagogical innovation, and language policy development in higher education.

Keywords: AI, EMI, MALL, EAP, higher education