Innovation in Language Learning
Adaptive Learning Pathways in EFL Classrooms: Artificial Intelligence to Improve Student Engagement and Language Learning Outcomes in Higher Education
Sehrish Aslam, School of Social Sciences and Humanities, NUST (Pakistan)
Abstract
The rapid development of Information and Communication Technologies (ICT) and especially Artificial Intelligence (AI) is transforming language learning and teaching by providing opportunities for personalized, adaptive, and learner-centred learning. While studies have also demonstrated the potential of digital technologies in enhancing language learning, there is a lack of empirical evidence on the impact of AI-based adaptive learning pathways on the engagement and achievement of learners of English as a Foreign Language (EFL), especially in higher education settings. This study seeks to fill this gap with a study on the effectiveness of AI-integrated learning pathways in improving EFL students' language level, motivation and learning experiences. The study is based on the constructs of Constructivist Learning Theory and Technology Acceptance Model (TAM) and explores the role of personalized AI feedback, adaptive content delivery, and intelligent learning recommendations in fostering active engagement and user autonomy. The research was carried out quantitatively with a quasi-experimental method, with the subjects of 300 undergraduate EFL students from three public and private universities. The study involved two groups: the experimental group (n = 150) and the control group (n = 150). The experimental group went through one academic semester of instruction using the AI-supported adaptive learning platform, while the control group received traditional EFL instruction. The data were gathered from pre- and post-language proficiency tests, and student engagement and technology acceptance questionnaires. Descriptive statistics, paired sample t-tests, independent sample t-tests and regression analysis were used in data analysis. The results showed that students in the AI-assisted adaptive learning pathway group had significantly greater improvements in language proficiency, engagement, motivation, and self-directed learning behaviors than students in the traditional instructional group. The findings also revealed that the perceived usefulness, ease of use, and personalized feedback factors were significant predictors of students' acceptance and sustained use of AI language learning tools. Qualitative feedback was also provided by the participants, who reported that the AI systems were helpful for vocabulary acquisition, improving writing, instant error correction, and personalized learning trajectories. It also adds to the literature on the use of ICTs in language education because it offers an empirical study of the pedagogical benefits of AI and personalization in the EFL context. This research differs from past work that mostly looks at technology adoption and/or at general digital learning environments, focusing instead on the measurable learning impact of adaptive AI pathways. The conclusions of the study suggest that AI-powered tools should complement and not replace the role of teachers in EFL teaching and learning, and that their use must be ethical, promote digital competence, and maintain a balanced relationship between humans and technology.
Keywords: Artificial Intelligence, ICT for Language Learning, Adaptive Learning, EFL Education, Student Engagement, Personalized Learning
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