We present a Large Language Model (LLM)-assisted SQL learning system that closes the loop from problem discovery to grading. Grounded in real-world data-wrangling scenarios, our agentic workflow (i) synthesizes industry-style practice problems with pedagogical metadata, (ii) produces executable reference SQL via a multi-step operator-planning pipeline, and (iii) grades student submissions against rich rubrics while explaining partial credit and surfacing actionable feedback for revision. We evaluate two core capabilities. First, on a large corpus of realistic SQL problems, our zero-shot, multi-step reference-answer generator, implemented with OpenAI's o4-mini, substantially outperforms a single-prompt baseline while approaching the state-of-the-art pipelines trained with supervised learning. Second, in a classroom deployment, we compare LLM-assisted grading with human graders across four exam questions, encompassing 326 submissions evaluated by six graders. The results indicate that LLMs can provide grading signals competitive with those of human graders for many question types. Overall, the system is designed for responsible educational use through real-world problems, generated reference solutions, and grading assistance. Together, these features enable scalable practice generation and grading, which improves student learning while augmenting instructor capacity.

Data transformation is a critical challenge in modern data management systems, particularly when handling complex operations over multiple data sources. However, existing approaches rely on supervised learning, which requires tremendous data labeling and training overhead. To alleviate such overhead while improving accuracy, we demonstrate a novel system DataMorpher that leverages Large Language Models (LLMs) to generate code that transforms source datasets into a user-specified target format. To generate a high-quality and token-efficient prompt, we leverage data profiling to extract features from the source datasets and historical examples of the target data. We also select a subset of features to reduce noise and costs using a ranking algorithm. These selected features are finally translated into a declarative language, which is inspired by SQL's data definition language (DDL), before being added to the prompt. We will demonstrate the workflow and effectiveness of DATAMORPHER using real-world data transformation workflows from Microsoft's GitHub benchmark, smart building, and medical data integration. (A5-min video of our demo is available at https://youtu.be/CuDm46K-_eA.)