The database systems course in an undergraduate computer science degree program has gained increasing importance due to the widespread usage of relational databases (RDBMS) in the real world as well as the rise of Data Science. A key learning goal for students taking such a course is to understand how SQL queries are executed in an RDBMS in practice. An off-the-shelf RDBMS typically exposes a query execution plan (QEP) in a visual or textual format, which describes the execution steps for a given query. However, it is often daunting for a student to comprehend these QEPs containing vendor-specific implementation details. In this paper, we discuss our experience of using a state-of-the-art tool called LANTERN in teaching QEPs to two cohorts of students taking database systems course in our academic institution. Specifically, LANTERN generates a natural language (NL)-based description of the execution strategy (i.e., QEP) chosen by the underlying RDBMS to process a user-specified query. We emphasize on how LANTERN is used as a supplementary tool to facilitate students to explore and learn about QEPs for their queries. In addition, we correlate the academic performance of the students with their LANTERN usage. Drawing on our experiences, we discuss future directions of LANTERN-augmented learning of relational query processing.