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Advances in natural language processing have resulted in large languagemodels (LLMs) that are capable of generating understandable and sensiblewritten text. Recent versions of these models, such as OpenAI Codex and GPT-3,can generate code and code explanations. However, it is unclear whether and howstudents might engage with such explanations. In this paper, we report on ourexperiences generating multiple code explanation types using LLMs andintegrating them into an interactive e-book on web software development. Wemodified the e-book to make LLM-generated code explanations accessible throughbuttons next to code snippets in the materials, which allowed us to track theuse of the explanations as well as to ask for feedback on their utility. Threedifferent types of explanations were available for students for eachexplainable code snippet; a line-by-line explanation, a list of importantconcepts, and a high-level summary of the code. Our preliminary results showthat all varieties of explanations were viewed by students and that themajority of students perceived the code explanations as helpful to them.However, student engagement appeared to vary by code snippet complexity,explanation type, and code snippet length. Drawing on our experiences, wediscuss future directions for integrating explanations generated by LLMs intoexisting computer science classrooms.

Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book