Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding
Project Overview
The document explores the integration of large language models (LLMs) like ChatGPT and GitHub Copilot as coding assistants in education, emphasizing their dual role in enhancing efficiency and clarity while also presenting challenges such as inaccuracies and ethical dilemmas. It outlines key applications of LLMs, including debugging, code generation, and elucidating programming concepts, underscoring the significance of user engagement through iterative prompting to maximize their utility. The findings indicate that while LLMs can greatly assist learners and developers, barriers to their widespread adoption persist, including trust issues, a continued reliance on traditional learning resources, and ethical concerns. To address these challenges, the document proposes design guidelines aimed at improving the contextual awareness and transparency of LLMs, ultimately aiming to enhance their effectiveness as educational tools in programming.
Key Applications
LLM-based coding assistants (e.g., ChatGPT, GitHub Copilot)
Context: Used in programming courses and software development environments by students and developers.
Implementation: Students and developers interacted with LLMs through surveys and coding tasks, employing them for debugging, explanations, and code generation.
Outcomes: Reported benefits included increased productivity and better understanding of code, while challenges included inaccuracies and lack of contextual awareness.
Challenges: Users expressed concerns about over-reliance on AI, inaccuracies in generated code, and ethical considerations surrounding AI use.
Implementation Barriers
Trust Issues and Preference for Traditional Resources
Users are skeptical about the accuracy and reliability of AI-generated responses, preferring established resources like documentation and forums as more reliable sources of information.
Proposed Solutions: Incorporate confidence indicators and citations for AI-generated content to enhance trust. Integrate LLMs with existing resources to enhance their value without replacing traditional methods.
Learning Concerns
Users worry that reliance on LLMs might hinder their learning and problem-solving skills.
Proposed Solutions: Design LLMs to support active learning strategies rather than just providing direct solutions.
Ethical Concerns
Participants raised issues regarding academic integrity and the environmental impact of AI.
Proposed Solutions: Develop ethical guidelines for AI use in educational contexts and promote transparency.
Project Team
Mehmet Akhoroz
Researcher
Caglar Yildirim
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mehmet Akhoroz, Caglar Yildirim
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