# Authoring Worked Examples for Java Programming with Human-AI Collaboration

arXiv: https://arxiv.org/abs/2312.02105 · HTML: https://mlsanity.com/paper/2312.02105/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [GPTutor: a ChatGPT-powered programming tool for code explanation](https://mlsanity.com/paper/2305.01863/) (2023)
- [Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks](https://mlsanity.com/paper/2206.03545/) (2022)

## Built on by (most cited first, 2 of 2)

- [Human-AI Co-Creation of Worked Examples for Programming Classes](https://mlsanity.com/paper/2402.16235/) (2024)
- [Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code](https://mlsanity.com/paper/2605.13280/) (2026)

## Abstract (arXiv)

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.