# Automatically Generating CS Learning Materials with Large Language Models

arXiv: https://arxiv.org/abs/2212.05113 · HTML: https://mlsanity.com/paper/2212.05113/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2022.

## Built on by (most cited first, 12 of 12)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [Learning gain differences between ChatGPT and human tutor generated algebra hints](https://mlsanity.com/paper/2302.06871/) (2023)
- [ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models](https://mlsanity.com/paper/2310.00117/) (2023)
- [A Survey on Pretrained Language Models for Neural Code Intelligence](https://mlsanity.com/paper/2212.10079/) (2022)
- [The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Course](https://mlsanity.com/paper/2503.07928/) (2025)
- [Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models](https://mlsanity.com/paper/2311.16017/) (2023)
- [Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?](https://mlsanity.com/paper/2311.00382/) (2023)
- [LLM-aided explanations of EDA synthesis errors](https://mlsanity.com/paper/2404.07235/) (2024)
- [Automatic Classification of Pedagogical Materials against CS Curriculum Guidelines](https://mlsanity.com/paper/2602.03962/) (2026)
- [Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmers](https://mlsanity.com/paper/2409.14368/) (2024)

## Abstract (arXiv)

Recent breakthroughs in Large Language Models (LLMs), such as GPT-3 and Codex, now enable software developers to generate code based on a natural language prompt. Within computer science education, researchers are exploring the potential for LLMs to generate code explanations and programming assignments using carefully crafted prompts. These advances may enable students to interact with code in new ways while helping instructors scale their learning materials. However, LLMs also introduce new implications for academic integrity, curriculum design, and software engineering careers. This workshop will demonstrate the capabilities of LLMs to help attendees evaluate whether and how LLMs might be integrated into their pedagogy and research. We will also engage attendees in brainstorming to consider how LLMs will impact our field.