  In the challenging field of introductory programming, high enrollments and
failure rates drive us to explore tools and systems to enhance student
outcomes, especially automated tools that scale to large cohorts. This paper
presents and evaluates the dcc --help tool, an integration of a Large Language
Model (LLM) into the Debugging C Compiler (DCC) to generate unique,
novice-focused explanations tailored to each error. dcc --help prompts an LLM
with contextual information of compile- and run-time error occurrences,
including the source code, error location and standard compiler error message.
The LLM is instructed to generate novice-focused, actionable error explanations
and guidance, designed to help students understand and resolve problems without
providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with
2,565 students using the tool over 64,000 times in ten weeks. We analysed a
subset of these error/explanation pairs to evaluate their properties, including
conceptual correctness, relevancy, and overall quality. We found that the
LLM-generated explanations were conceptually accurate in 90% of compile-time
and 75% of run-time cases, but often disregarded the instruction not to provide
solutions in code. Our findings, observations and reflections following
deployment indicate that dcc-help provides novel opportunities for scaffolding
students' introduction to programming.
