How Good are Large Language Models at Generating Subgoal Labels?

The use of subgoal labels in introduction to programming classrooms has been shown to improve student performance, learning, retention, and reduce students' drop out rates. However, creating and adding subgoal labels to programming assignments is often hard to articulate and very time-intensive for instructors. In Computing Education Research, Large Language Models (LLMs) have been widely used to generate human-like outputs such as worked examples and source code. In this work, we explore whether ChatGPT could be used to generate high-quality and appropriate subgoal labels in two programming curricula. Our qualitative data analysis suggests that LLMs can assist instructors in creating subgoal labels in their classrooms, opening up directions to empower students' learning experience in programming classrooms.