GPTutor: a ChatGPT-powered programming tool for code explanation
Project Overview
The document highlights the innovative use of generative AI in education through the introduction of GPTutor, a ChatGPT-powered tool that enhances programming learning. As a Visual Studio Code extension, GPTutor leverages the ChatGPT API to analyze coding problems and deliver personalized, concise explanations, effectively addressing common challenges faced by learners in the programming domain. Evaluations of GPTutor demonstrate its superiority over traditional code explanation tools, as it consistently provides accurate and relevant insights that significantly improve the learning experience for various users, including students, new employees, and participants in coding boot camps. The findings suggest that integrating generative AI in educational tools can foster better understanding and engagement in programming, showcasing its potential to transform the landscape of coding education.
Key Applications
GPTutor - A Visual Studio Code extension that provides programming code explanations using ChatGPT.
Context: Educational context for computer science students and coding boot camp participants.
Implementation: Developed as a Visual Studio Code extension using Typescript and the OpenAI ChatGPT API, the tool analyzes code and generates explanations based on context.
Outcomes: GPTutor delivers the most concise and accurate explanations compared to existing tools like vanilla ChatGPT and GitHub Copilot. User feedback indicates it is user-friendly and effective in enhancing understanding of programming code.
Challenges: Challenges include the potential for superficial explanations and the need for continuous updates to handle new coding libraries.
Implementation Barriers
Technical Limitations
Existing NLG code explainers can provide superficial insights and may not handle domain-specific business logic adequately.
Proposed Solutions: Enhancing GPTutor's performance through prompt programming and heuristic search to identify relevant code.
Content Relevance
Existing tools may provide excessive or irrelevant information when explaining code.
Proposed Solutions: Utilizing advanced prompt designs to ensure concise and relevant explanations.
Data Recency
NLG tools may not be up-to-date with the latest programming libraries or practices.
Proposed Solutions: Regular updates and training with new data to improve the tool's effectiveness.
Project Team
Eason Chen
Researcher
Ray Huang
Researcher
Han-Shin Chen
Researcher
Yuen-Hsien Tseng
Researcher
Liang-Yi Li
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Eason Chen, Ray Huang, Han-Shin Chen, Yuen-Hsien Tseng, Liang-Yi Li
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai