Automatically Generating CS Learning Materials with Large Language Models

TL;DR

This paper explores how large language models can be used to generate computer science learning materials, discussing their potential benefits and implications for education and curriculum design.

Contribution

It provides an overview of LLM capabilities in generating educational content and discusses their impact on CS pedagogy and research.

Findings

LLMs can generate code explanations and assignments

Potential to enhance student interaction with code

Implications for academic integrity and curriculum design

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.