Human-AI Co-Creation of Worked Examples for Programming Classes

TL;DR

This paper presents a human-AI collaborative system for creating Java programming worked examples, aiming to reduce instructor workload by generating initial explanations for code that instructors can edit, and evaluates its effectiveness.

Contribution

It introduces a novel authoring system that automates initial explanation generation for Java code examples, facilitating efficient instructor editing and improving educational content creation.

Findings

Generated explanations are of comparable quality to manually created ones.

Instructors found the system helpful and efficient.

The approach reduces time spent on creating worked examples.

Abstract

Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary.We also present a study that assesses the quality of explanations created with this approach