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Navigating the Pitfalls: Analyzing the Behavior of LLMs as a Coding Assistant for Computer Science Students-A Systematic Review of the Literature

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IEEE ACCESS 2024年 12卷 112605-112625页

作者： Pirzado, Farman Ali Ahmed, Awais Mendoza-Urdiales, Roman Alejandro Terashima-Marin, Hugo Tecnol Monterrey Sch Engn & Sci Monterrey Monterrey 64849 Nuevo Leon Mexico Univ Elect Sci & Technol China UESTC Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China

In recent years, large language models (LLMs) have been employed significantly in different domains of computing education. Nevertheless, these models have been focused on essential adherence to their integration as coding assistants in computing education. However, attention has been switched to thoroughly examining and analyzing LLM behavior, particularly in computing education for programming tasks such as code generation, code explanation, and programming error message explanation. Therefore, it becomes imperative to understand their behavior to examine potential pitfalls. This article addresses this gap systematically and details how different LLM-based coding chatbots, such as ChatGPT, Codex, Copilot, and others, react to various coding inputs within computing education. To achieve this objective, we collected and analyzed articles from 2021 to 2024, and 72 studies were thoroughly examined. These objectives include investigating the existing limitations and challenges associated with utilizing these systems for coding tasks, assessing their responses to prompts containing coding syntax, examining the impact of their output on student learning, and evaluating their performance as debugging tools. The findings of this review highlight that it is premature to incorporate these systems into computing education due to their limitations that may limit their effectiveness as comprehensive coding assistants for computer science students. These limitations include issues with handling prompts containing code snippets, potential negative impacts on student learning, limited debugging capabilities, and other ineffectiveness. The finding also reports multiple research directions that can be considered in future research related to LLMs in computing education.

关键词： Large language models computing education code generation code explanation programming error messages explanation Large language models computing education code generation code explanation programming error messages explanation

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