Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming
Project Overview
The document investigates the application of generative AI, particularly ChatGPT, in delivering automated feedback on programming assignments within an introductory computer science course. It highlights students' perceptions of this AI-generated feedback, assessing its adherence to formative feedback principles, their desire for personalized comments that reference their specific code, and recommendations for enhancement. The findings suggest that students regard the AI feedback as generally effective, appreciating its ability to provide timely assistance while expressing a strong preference for feedback that includes specific examples from their work to improve clarity and correctness. Overall, the use of generative AI in this educational context appears to enhance the learning experience by offering students constructive insights that can facilitate their understanding and development in programming.
Key Applications
Automated feedback generation using ChatGPT for Java programming assignments.
Context: Undergraduate introductory computer science course (CS1) at a U.S. public university.
Implementation: The ChatGPT API was used to generate feedback based on students' Java code for lab assignments, with surveys conducted to assess student perceptions and preferences.
Outcomes: Students perceived the feedback as aligning with formative feedback guidelines, preferring feedback that included their code for specificity and clarity.
Challenges: Challenges include ensuring the accuracy of AI feedback, managing the tone of feedback, and limitations in token counts affecting feedback depth.
Implementation Barriers
Technical Barrier
Token limits on ChatGPT can restrict the depth of feedback that can be generated for extensive code assignments.
Proposed Solutions: Future research could explore methods to effectively manage token usage or optimize feedback generation processes.
Ethical Barrier
Concerns about academic integrity and the potential for students to misuse AI tools for cheating.
Proposed Solutions: Developing guidelines and policies for responsible use of AI in education and incorporating AI as a teaching aid.
Project Team
Zhengdong Zhang
Researcher
Zihan Dong
Researcher
Yang Shi
Researcher
Noboru Matsuda
Researcher
Thomas Price
Researcher
Dongkuan Xu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zhengdong Zhang, Zihan Dong, Yang Shi, Noboru Matsuda, Thomas Price, Dongkuan Xu
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