EDBooks: AI-Enhanced Interactive Narratives for Programming Education

TL;DR

This paper introduces EDBooks, an innovative approach combining large language models with traditional e-books to enhance personalized programming education and active learning, making it more accessible and pedagogically sound.

Contribution

It presents a novel framework that integrates LLMs with structured learning materials to improve engagement and personalization in programming education.

Findings

Enhanced student engagement through personalized questions and answers.

Improved learning outcomes with structured, pedagogically sound content.

Broader accessibility of programming education using AI-enhanced materials.

Abstract

Large Language Models (LLMs) have shown the potential to be valuable teaching tools, with the potential of giving every student a personalized tutor. However, one challenge with using LLMs to learn new concepts is that when learning a topic in an unfamiliar domain, it can be difficult to know what questions to ask. Further, language models do not always encourage "active learning" where students can test and assess their understanding. In this paper, we propose ways to combine large language models with "traditional" learning materials (like e-books) to give readers the benefits of working with LLMs (the ability to ask personally interesting questions and receive personalized answers) with the benefits of a traditional e-book (having a structure and content that is pedagogically sound). This work shows one way that LLMs have the potential to improve learning materials and make personalized programming education more accessible to a broader audience.