DATA-DRIVEN LEARNING AND LEARNER AUTONOMY IN THE AGE OF AI

Abstract

Data-driven learning (DDL) represents a powerful pedagogical approach in language education, enabling learners to engage with authentic corpus data through exploratory, student-centered activities. By analyzing naturally occurring language with technological tools, learners move from passive reception to active discovery, fostering autonomy and deeper linguistic awareness. This review article synthesizes current research on the intersection of DDL and learner autonomy, highlighting how corpus-based methods and emerging AI technologies reshape the learning process in higher education. It examines opportunities for empowering learners to independently analyze, experiment with, and generate language, while also addressing challenges such as over-reliance on technology and ethical considerations in AI-driven environments. By mapping recent developments and future directions, the article underscores the role of DDL in cultivating autonomous, confident learners equipped to navigate complex real-world contexts in the age of AI.