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Mastering SQL is a key data science competence. While most large language models are able to translate natural language queries to SQL, their ability to tutor learners and authentically assess student assignments are at the least fragile. In this paper, we introduce {\em ExplainS} as an experimental prototype. In this web-based system, we augment Gemini with abstract syntax tree (AST) to enhance Gemini's semantic analysis power to be able to assist and tutor students better. This edition of ExplainS provides a collection of exercises with varying difficulty levels, covering core SQL concepts. Users interact with a dynamic schema display, and their queries are validated against carefully crafted solutions. To provide context-aware personalized feedback, ExplainS leverages Gemini and the SQLglot library to analyze query AST differences between user queries and correct solutions, pinpointing the root cause of errors. This emerging research is part of a wider Data Science effort, and in this paper, we only focus on the meaningful feedback generation component of the ExplainS system.