CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs
Project Overview
The document explores the integration of generative AI, specifically through tools like CodeAid, in educational settings, particularly in programming courses at the higher education level. CodeAid is a Large Language Model (LLM)-powered programming assistant designed to enhance students' understanding of programming concepts and facilitate debugging and code writing without directly providing code solutions. It offers features such as inline code exploration and support for fixing code, all while employing strategies to encourage cognitive engagement and motivated learning. Through a semester-long deployment involving 700 students, the effectiveness of CodeAid was analyzed, revealing insights into the types of queries students posed and the tool's success in addressing their needs. The findings underscore the dual nature of AI's role in education, highlighting both the significant benefits—such as improved comprehension and problem-solving skills—and the challenges associated with its implementation, particularly in maintaining a balance between direct assistance and fostering independent learning. Overall, the document emphasizes the transformative potential of generative AI in enhancing programming education while also considering the complexities involved in its effective application.
Key Applications
CodeAid - LLM-based programming assistant
Context: Applied in various higher education programming courses, including an introductory programming course with 700 university students and other programming-focused classes targeting computer science students.
Implementation: Deployed as a semester-long tool to facilitate student interaction and assistance with programming tasks, with iterative design based on feedback from students and instructors.
Outcomes: Improved student engagement and understanding of programming concepts, with a reported correctness rate of 79% for responses, enhanced ability to debug, and clarify code.
Challenges: Concerns regarding over-reliance on the tool, potential for incorrect or misleading information, and reliance on the accuracy of the LLM responses.
Implementation Barriers
Technical Limitations
Issues with response accuracy, the tool's capability to understand complex queries, and LLMs potentially providing incorrect or misleading information which can hinder learning.
Proposed Solutions: Implementing better guardrails, feedback mechanisms to correct and improve model responses, ensuring regular updates and training on educational content.
User Engagement
Students may not use the tool due to a preference for existing resources, lack of awareness, or finding the tool intimidating to engage with.
Proposed Solutions: Incentivizing use through integration into the curriculum, providing clear guidelines, fostering a supportive environment, and offering training sessions on its effective use.
Project Team
Majeed Kazemitabaar
Researcher
Runlong Ye
Researcher
Xiaoning Wang
Researcher
Austin Z. Henley
Researcher
Paul Denny
Researcher
Michelle Craig
Researcher
Tovi Grossman
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Majeed Kazemitabaar, Runlong Ye, Xiaoning Wang, Austin Z. Henley, Paul Denny, Michelle Craig, Tovi Grossman
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