An LLM-powered tutor provides immediate feedback on students' self-explanations of programming examples, promoting active learning and improved understanding.

In plain terms

Learning from step-by-step problem solutions, called worked examples, can be a passive activity. A more effective approach is self-explanation, where students actively explain problems and solutions to themselves, but providing feedback on these free-text explanations has been a challenge for human instructors and automated systems. This research investigates if a Large Language Model (LLM)—an advanced AI capable of understanding and generating human-like text—can offer timely, automated feedback. They developed ESSE, a self-explanation tutor for introductory programming (CS1) students, which uses an LLM to assess student explanations of code line-by-line. The study found that the LLM's feedback was sufficiently reliable when compared to human experts. Furthermore, students using ESSE refined their explanations, making them more thorough and conceptually deep, and demonstrated improved learning outcomes.