Data-driven learning (DDL) is an innovative approach in language education that leverages authentic linguistic data to facilitate learning. Common elements of DDL involve the use of authentic corpus data and student-centered exploratory learning activities. Naturally occurring language using corpora technology tools promotes learners’ active engagement in the learning process, leading learners to discover or explore language rules by themselves based on their observation and analysis of the concordance output, empowering students to become more autonomous and proficient language users. This chapter explores how the DDL approach can liberate students through technology in higher education by fostering autonomous learning. Students gain the tools to independently analyze and understand language by engaging with authentic linguistic data, moving beyond passive reception to active exploration. This empowerment aligns with the educational goal of nurturing autonomous, confident individuals capable of applying their language and technology skills in varied and complex real-world situations, thus emancipating them from traditional, dependency-based learning models. Moreover, following Nunan’s levels of autonomy: awareness, involvement, intervention, creation, and transcendence, this chapter will also discuss how technology, particularly the DDL approach, helps learners to progressively take more control over their learning process. Starting with awareness and involvement in analyzing data, learners move towards intervention by experimenting with language use, creation through generating their own data, and finally, transcendence as they go beyond the classroom when they become fully autonomous. Besides, the chapter will also elaborate on the differences and similarities of emerging technologies such as generative AI and discuss future directions.