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Worked code examples 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 of a large number of examples typically used in a programming class. This paper explores the opportunity to facilitate the development of worked examples for Java programming through a human-AI collaborative authoring approach. The idea of collaborative authoring is to generate a starting version of code explanations using LLM and present it to the instructor to edit if necessary. The critical step towards implementing this idea is to ensure that LLM can produce code explanations that look meaningful and acceptable to instructors and students. To achieve this goal, we performed an extensive prompt engineering study and evaluated the explanation produced by the selected prompt in a user study with students and authors. 

Worked examples are among the most popular types of learning content in programming classes. 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 generate 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. 

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.