This entry examines the importance of corpus tools in data-driven learning (DDL) for language education, focusing on the methodological choices involved in their selection and design for pedagogical use. It emphasizes the need for tools that align with educational goals, offer user-friendly interfaces, and facilitate linguistic discovery. Key considerations include the simplification of data analysis functions, customization options, and the promotion of inductive learning. The future of DDL tool development is highlighted, with potential advancements such as integration with AI, improved user experience, mobile accessibility, and access through collaborative learning environments. The evolving landscape of corpus tools for DDL promises to enhance language learning through more personalized, engaging, and community-oriented approaches.