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In this paper, we assess the efficacy of ChatGPT (version Feb 2023), alarge-scale language model, in solving probability problems typically presentedin introductory computer engineering exams. Our study comprised a set of 23probability exercises administered to students at Rey Juan Carlos University(URJC) in Madrid. The responses produced by ChatGPT were evaluated by a groupof five statistics professors, who assessed them qualitatively and assignedgrades based on the same criteria used for students. Our results indicate thatChatGPT surpasses the average student in terms of phrasing, organization, andlogical reasoning. The model's performance remained consistent for both theSpanish and English versions of the exercises. However, ChatGPT encountereddifficulties in executing basic numerical operations. Our experimentsdemonstrate that requesting ChatGPT to provide the solution in the form of an Rscript proved to be an effective approach for overcoming these limitations. Insummary, our results indicate that ChatGPT surpasses the average student insolving probability problems commonly presented in introductory computerengineering exams. Nonetheless, the model exhibits limitations in reasoningaround certain probability concepts. The model's ability to deliverhigh-quality explanations and illustrate solutions in any programming language,coupled with its performance in solving probability exercises, suggests thatlarge language models have the potential to serve as learning assistants.

The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students