Automatically Generating CS Learning Materials with Large Language Models
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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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2022.
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Citing papers per year: 2022: 1; 2023: 7; 2024: 2; 2025: 1; 2026: 1.
12 papers.
- 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
- 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagramscited by 65
- 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 44
- 2023Learning gain differences between ChatGPT and human tutor generated algebra hintscited by 42
- 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Modelscited by 17
- 2022A Survey on Pretrained Language Models for Neural Code Intelligencecited by 13
- 2025The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Coursecited by 7
- 2023Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Modelscited by 6
- 2023Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?cited by 3
- 2024LLM-aided explanations of EDA synthesis errorscited by 3
- 2026Automatic Classification of Pedagogical Materials against CS Curriculum Guidelinescited by 1
- 2024Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmerscited by 0
- 2026Automatic Classification of Pedagogical Materials against CS Curriculum Guidelines3 Feb 2026
- 2025The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Course11 Mar 2025
- 2024Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmers22 Sep 2024
- 2024LLM-aided explanations of EDA synthesis errors7 Apr 2024
- 2023Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models27 Nov 2023
- 2023Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?1 Nov 2023
- 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Education1 Oct 2023
- 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models29 Sep 2023
- 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests9 Jun 2023
- 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagrams19 May 2023
- 2023Learning gain differences between ChatGPT and human tutor generated algebra hints14 Feb 2023
- 2022A Survey on Pretrained Language Models for Neural Code Intelligence20 Dec 2022