Data-driven learning (DDL) is an approach to language education that uses corpora and corpus tools to enhance instruction. DDL treats learners as “language detectives” who explore corpora to find common and useful language patterns. A key tool in DDL is the Key-Word-In-Context (KWIC) concordancer, which searches for words and phrases in a corpus and displays them in context. This entry explores the fundamental features and functions of concordancers, addresses some of the challenges learners face when using concordancers, and highlights strategies that can be introduced to minimize these challenges. Furthermore, the entry examines recent advancements in AI, such as word and sentence embedding technologies and Large Language Models (LLMs) and their potential to expand the scope, functionality, and effectiveness of concordance tools in educational settings.