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Providing personalized assistance at scale is a long-standing challenge forcomputing educators, but a new generation of tools powered by large languagemodels (LLMs) offers immense promise. Such tools can, in theory, provideon-demand help in large class settings and be configured with appropriateguardrails to prevent misuse and mitigate common concerns around learnerover-reliance. However, the deployment of LLM-powered tools in authenticclassroom settings is still rare, and very little is currently known about howstudents will use them in practice and what type of help they will seek. Toaddress this, we examine students' use of an innovative LLM-powered tool thatprovides on-demand programming assistance without revealing solutions directly.We deployed the tool for 12 weeks in an introductory computer and data sciencecourse ($n = 52$), collecting more than 2,500 queries submitted by studentsthroughout the term. We manually categorized all student queries based on thetype of assistance sought, and we automatically analyzed several additionalquery characteristics. We found that most queries requested immediate help withprogramming assignments, whereas fewer requests asked for help on relatedconcepts or for deepening conceptual understanding. Furthermore, students oftenprovided minimal information to the tool, suggesting this is an area in whichtargeted instruction would be beneficial. We also found that students whoachieved more success in the course tended to have used the tool morefrequently overall. Lessons from this research can be leveraged by programmingeducators and institutions who plan to augment their teaching with emergingLLM-powered tools.

Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant