AI Teaches the Art of Elegant Coding: Timely, Fair, and Helpful Style Feedback in a Global Course

• URL: http://arxiv.org/abs/2403.14986v1
• Date: Fri, 22 Mar 2024 06:45:39 GMT
• Title: AI Teaches the Art of Elegant Coding: Timely, Fair, and Helpful Style Feedback in a Global Course
• Authors: Juliette Woodrow, Ali Malik, Chris Piech,
• Abstract summary: We present our experience deploying a novel, real-time style feedback tool in Code in Place, a large-scale online CS1 course. We show that students who received style feedback in real-time were five times more likely to view and engage with their feedback compared to students who received delayed feedback. Those who viewed feedback were more likely to make significant style-related edits to their code, with over 79% of these edits directly incorporating their feedback.
• Score: 8.176398354378088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Teaching students how to write code that is elegant, reusable, and comprehensible is a fundamental part of CS1 education. However, providing this "style feedback" in a timely manner has proven difficult to scale. In this paper, we present our experience deploying a novel, real-time style feedback tool in Code in Place, a large-scale online CS1 course. Our tool is based on the latest breakthroughs in large-language models (LLMs) and was carefully designed to be safe and helpful for students. We used our Real-Time Style Feedback tool (RTSF) in a class with over 8,000 diverse students from across the globe and ran a randomized control trial to understand its benefits. We show that students who received style feedback in real-time were five times more likely to view and engage with their feedback compared to students who received delayed feedback. Moreover, those who viewed feedback were more likely to make significant style-related edits to their code, with over 79% of these edits directly incorporating their feedback. We also discuss the practicality and dangers of LLM-based tools for feedback, investigating the quality of the feedback generated, LLM limitations, and techniques for consistency, standardization, and safeguarding against demographic bias, all of which are crucial for a tool utilized by students.

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