Developing an Interactive OpenMP Programming Book with Large Language Models

TL;DR

This paper explores using large language models to assist in creating an interactive OpenMP programming textbook, highlighting capabilities, limitations, and integration with traditional teaching methods for enhanced learning.

Contribution

It demonstrates a novel approach to textbook authoring using LLMs and presents a framework for interactive, executable programming education with AI assistance.

Findings

LLMs can generate initial textbook content effectively.

Manual revision is essential for accuracy and pedagogical quality.

The framework enables interactive learning with instant code execution.

Abstract

This paper presents an approach to authoring a textbook titled Interactive OpenMP Programming with the assistance of Large Language Models (LLMs). The writing process utilized state-of-the-art LLMs, including Gemini Pro 1.5, Claude 3, and ChatGPT-4, to generate the initial structure and outline of the book, as well as the initial content for specific chapters. This content included detailed descriptions of individual OpenMP constructs and practical programming examples. The outline and content have then undergone extensive manual revisions to meet our book goals. In this paper, we report our findings about the capabilities and limitations of these LLMs. We address critical questions concerning the necessity of textbook resources and the effectiveness of LLMs in creating fundamental and practical programming content. Our findings suggest that while LLMs offer significant advantages in generating textbook content, they require careful integration with traditional educational methodologies to ensure depth, accuracy, and pedagogical effectiveness. The Interactive OpenMP Programming book is developed with the framework of Jupyter Book, enabling the execution of code within the book from the web browser, providing instant feedback and a dynamic learning experience that stands in contrast to traditional educational resources. The book represents a significant step towards modernizing programming education, offering insights into practical strategies for generating the textbook through advanced AI tools.