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  1. ; ; ; ( , Proceedings of The Generative AI in Education Workshop in conjunction with the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeuIPS))
    This paper systematically investigates the generation of code explanations by Large Language Models (LLMs) for code examples commonly encountered in introductory programming courses. Our findings reveal significant variations in the nature of code explanations produced by LLMs, influenced by factors such as the wording of the prompt, the specific code examples under consideration, the programming language involved, the temperature parameter, and the version of the LLM. However, a consistent pattern emerges for Java and Python, where ex- planations exhibit a Flesch-Kincaid readability level of approximately 7-8 grade and a consistent lexical density, indicating the proportion of meaningful words relative to the total explanation size. Additionally, the generated explanations consistently achieve high scores for correctness, but lower scores on three other metrics: completeness, conciseness, and specificity. 
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    Free, publicly-accessible full text available December 15, 2024
  2. ; ; ; ( , Proceedings of the 22nd International Conference on Artificial Intelligence in Education)
    Full Text Available 
  3. ; ; ; ( , 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop in Conjunction with The 14th International Educational Data Mining Conference)
    Accepted Manuscript Full Text Available