2023/08/15 by Natalie Kiesler, Kiesler, Natalie, Daniel Schiffner +1 · 2 citations
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning and Data Classification #Software Engineering (cs.SE) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2308.08572
openalex publication_date 2023/08/15 · openalex created_date 2023/08/22 · openalex updated_date 2026/07/28
This paper investigates the performance of the Large Language Models (LLMs) ChatGPT-3.5 and GPT-4 in solving introductory programming tasks. Based on the performance, implications for didactic scenarios and assessment formats utilizing LLMs are derived. For the analysis, 72 Python tasks for novice programmers were selected from the free site CodingBat. Full task descriptions were used as input to the LLMs, while the generated replies were evaluated using CodingBat's unit tests. In addition, the general availability of textual explanations and program code was analyzed. The results show high scores of 94.4 to 95.8% correct responses and reliable availability of textual explanations and program code, which opens new ways to incorporate LLMs into programming education and assessment.