In recent times, the necessity for competent programmers has increased the number of individuals learning to code. Nevertheless, a teacher shortage makes it difficult to create tailored learning experiences. Students sometimes need assistance with novel programming languages and difficult code samples. Natural Language Generation (NLG) models, reminiscent of ChatGPT, transform programming education by providing tailored training. These models comprehend difficult programming ideas and deliver human-like explanations. NLG provides learners access to personalized lectures, code examples, and personalized explanations.

On this context, a research team from Taiwan recently published a paper to introduce  GPTutor. GPTutor, a ChatGPT-powered programming tool, is a Visual Studio Code extension that leverages the capabilities of the ChatGPT API to offer comprehensive explanations for programming code. The important idea of the proposed tool is to utilize NLG models as programming tutors to offer code explanations. By leveraging the OpenAI ChatGPT API, GPTutor retrieves pertinent code and provides highly precise and concise explanations. Existing NLG applications have limitations in offering comprehensive, accurate, and up-to-date descriptions for programming code. GPTutor goals to beat these limitations and supply concise and accurate code explanations by analyzing the source code.

The source code for GPTutor is freely available on GitHub and has been successfully published on the Visual Studio Code Extension Marketplace. Users install the extension, set their OpenAI API key, and choose the GPT model if desired. They will then hover over a code block within the supported language (currently Move) to receive explanations, comments, or audits for the chosen code. Students, programming teachers, and coding boot camp instructors have all expressed satisfaction with GPTutor’s user-friendly interface and its capability to deliver adequate code explanations. Users are especially bowled over by GPTutor’s ability to offer pertinent source code for functions in prompts, leading to more thorough explanations. As well as, comparative evaluations exhibit that GPTutor outperforms Vanilla ChatGPT and GitHub Copilot in delivering accurate code explanations.

The authors of the paper propose several areas of future work for GPTutor. One key focus is enhancing performance and personalization by applying prompt programming techniques. This involves optimizing prompts and employing heuristic search methods to discover relevant code, with the last word goal of providing personalized explanations and an enhanced user experience. Moreover, the authors plan to guage the effectiveness of GPTutor in real-world scenarios by observing student interactions with the tool during programming assignments. This evaluation will involve collaborating with coding course lecturers and utilizing appropriate evaluation techniques to evaluate the connection between student grades and the frequency of GPTutor usage.

In conclusion, GPTutor is a ChatGPT-powered programming tool that addresses the challenges in programming education by providing comprehensive code explanations. It has received positive feedback from users, and future work includes enhancing performance and personalization through prompt programming techniques. The effectiveness of GPTutor shall be evaluated in real-world scenarios. GPTutor continues to evolve as a precious tool for programming education.

GPTutor shall be evaluated in real-world scenarios to measure its impact on student learning outcomes. Observing how students interact with the tool during programming assignments and analyzing the correlation between grades and GPTutor usage frequency will validate its effectiveness as an academic tool.

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