Automatically Generating CS Learning Materials with Large Language Models

• URL: http://arxiv.org/abs/2212.05113v1
• Date: Fri, 9 Dec 2022 20:37:44 GMT
• Title: Automatically Generating CS Learning Materials with Large Language Models
• Authors: Stephen MacNeil, Andrew Tran, Juho Leinonen, Paul Denny, Joanne Kim, Arto Hellas, Seth Bernstein, Sami Sarsa
• Abstract summary: Large Language Models (LLMs) enable software developers to generate code based on a natural language prompt. LLMs may enable students to interact with code in new ways while helping instructors scale their learning materials. LLMs also introduce new implications for academic integrity, curriculum design, and software engineering careers.
• Score: 4.526618922750769
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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