Innovation in Language Learning

The rapid emergence of Information and Communication Technologies (ICT), especially Artificial Intelligence (AI), has revolutionised the field of language learning and teaching, making personalized, adaptive, and learner-centred learning possible. While the potential of digital technologies for language learning has been noted in previous studies, there remains a lack of empirical evidence on the measurable effects of digital technologies, particularly AI-based adaptive learning pathways, on students' engagement, motivation, and language achievement, especially in higher education. This study addresses this gap by examining the effectiveness of AI-integrated adaptive learning pathways in enhancing English language proficiency, student engagement, motivation, and learning experiences. This research adopts a quantitative, quasi-experimental design grounded in Constructivist Learning Theory and the Technology Acceptance Model (TAM). The study involved 300 undergraduate EFL students, comprising an experimental group (n = 150) and a control group (n = 150) from three public and private universities. Over one semester, the experimental group was instructed via an AI-based personalised adaptive learning platform featuring personalised feedback, adaptive content delivery, and machine-learning-based learning suggestions; the control group was instructed using traditional EFL methods. Data were collected through pre- and post-language proficiency tests, a student engagement and motivation scale, and a technology acceptance questionnaire. Descriptive statistics, paired- and independent-sample t-tests, and regression analysis were used for data analysis. The results showed that language proficiency, engagement, motivation, and self-directed learning behaviours were significantly higher in the group of students who learned through the AI-supported adaptive learning pathway than in the group who learned through traditional instruction. Statistical analysis supported these findings, showing significant differences in pre- and post-test scores and between the experimental and control groups, while regression analysis indicated that perceived usefulness, ease of use, and personalised AI feedback were significant predictors of students' acceptance and ongoing use of AI-based language learning tools. Feedback from participants also suggested that AI systems could aid in enhancing vocabulary and writing skills, providing instant feedback on errors, and tailoring learning paths to individual needs. This study adds an empirical understanding of the pedagogical consequences of adaptive AI pathways, which goes beyond technology adoption and general digital learning environments, to the research concerning the use of AI in language learning. The results indicate that AI tools should be used to support and enhance, but not supplant, the role of English language teachers in EFL classrooms, highlighting the need for ethical use of AI, digital skills training, and a balanced approach to language learning in future educational contexts.

 

Keywords: Artificial Intelligence, ICT for Language Learning, Adaptive Learning, EFL Education, Student Engagement, Personalized Learning

 

REFERENCES

 

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