As AI-assisted coding becomes standard in software development, computer science educators need a clearer understanding of how Large Language Models (LLMs) can support the learning process. Recent work has examined how students can benefit from using LLMs in their courses, but most studies rely on self-reported usage or controlled experiments with short, isolated programming tasks. To complement these approaches, this paper investigates how students organically leverage LLMs in an advanced computer science course where assignments reflect real-world complexity. We analyze 448 LLM chat logs from 147 students across two offerings of a senior-level web programming course at a large U.S. research university. Through open coding, we identified 14 distinct prompt–response pair types that cluster into three categories: to generate code, debug code, and explain programming concepts. Our analysis reveals that how students interact with LLMs correlates with academic performance. High-effort detailed specifications for code generation positively correlated with final grades (r = 0.25, p < 0.01), whereas low-effort behaviors such as pasting raw error messages showed negative correlations (r = −0.34, p < 0.01). We also observed a temporal shift toward explanation-oriented interactions, suggesting that students increasingly use LLMs as conceptual tutors and not just as code generators.

As part of teaching SQL, instructors often rely on auto-grading systems for marking students' assignments. However, such systems lack essential insights into the approaches students use to solve these assignments, allowing subtle flaws in student intuition to go unseen. Further, manual analysis of students' code submissions ranges from costly to impossible, depending on the assessments' frequency. In this paper, we present a system capable of extracting features that instructors deem significant from students' SQL queries and using them to generate clusters that capture the key approaches taken. To supplement this, we project the extracted information to an interactive dashboard and demonstrate its usefulness in allowing database systems professors and teaching staff to quickly identify trends in students' solutions.