# Exploring the Potential of Large Language Models to Generate Formative Programming Feedback

arXiv: https://arxiv.org/abs/2309.00029 · HTML: https://mlsanity.com/paper/2309.00029/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Using Large Language Models to Enhance Programming Error Messages](https://mlsanity.com/paper/2210.11630/) (2022)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [Large Language Models (GPT) for automating feedback on programming assignments](https://mlsanity.com/paper/2307.00150/) (2023)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessments](https://mlsanity.com/paper/2308.08572/) (2023)

## Built on by (most cited first, 28 of 28)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation](https://mlsanity.com/paper/2310.03780/) (2023)
- [Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics](https://mlsanity.com/paper/2503.23989/) (2025)
- [Next-Step Hint Generation for Introductory Programming Using Large Language Models](https://mlsanity.com/paper/2312.10055/) (2023)
- [Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge](https://mlsanity.com/paper/2405.05253/) (2024)
- [Feedback-Generation for Programming Exercises With GPT-4](https://mlsanity.com/paper/2403.04449/) (2024)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [Generative AI for Education (GAIED): Advances, Opportunities, and Challenges](https://mlsanity.com/paper/2402.01580/) (2024)
- [Evaluating Contextually Personalized Programming Exercises Created with Generative AI](https://mlsanity.com/paper/2407.11994/) (2024)
- [How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course](https://mlsanity.com/paper/2407.20792/) (2024)
- [Evaluating the Application of Large Language Models to Generate Feedback in Programming Education](https://mlsanity.com/paper/2403.09744/) (2024)
- [AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?](https://mlsanity.com/paper/2311.10737/) (2023)
- [Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT](https://mlsanity.com/paper/2405.19132/) (2024)
- [Evaluating Language Models for Generating and Judging Programming Feedback](https://mlsanity.com/paper/2407.04873/) (2024)
- [Grading Massive Open Online Courses Using Large Language Models](https://mlsanity.com/paper/2406.11102/) (2024)
- [Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation](https://mlsanity.com/paper/2405.06681/) (2024)
- [Knowledge of Pretrained Language Models on Surface Information of Tokens](https://mlsanity.com/paper/2402.09808/) (2024)
- [One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks](https://mlsanity.com/paper/2410.09268/) (2024)
- [Benchmarking Educational Program Repair](https://mlsanity.com/paper/2405.05347/) (2024)
- [Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners](https://mlsanity.com/paper/2412.12800/) (2024)
- [The Role of Generative AI in Software Student CollaborAItion](https://mlsanity.com/paper/2501.14084/) (2025)
- [Assessing Large Language Models for Automated Feedback Generation in Learning Programming Problem Solving](https://mlsanity.com/paper/2503.14630/) (2025)
- [Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models](https://mlsanity.com/paper/2401.10759/) (2024)
- [QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners](https://mlsanity.com/paper/2402.07913/) (2024)
- [Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects](https://mlsanity.com/paper/2412.04185/) (2024)
- [Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools](https://mlsanity.com/paper/2507.05305/) (2025)
- [Students' Feedback Requests and Interactions with the SCRIPT Chatbot: Do They Get What They Ask For?](https://mlsanity.com/paper/2507.17258/) (2025)
- [GenAI Voice Mode in Programming Education](https://mlsanity.com/paper/2509.10596/) (2025)

## Abstract (arXiv)

Ever since the emergence of large language models (LLMs) and related applications, such as ChatGPT, its performance and error analysis for programming tasks have been subject to research. In this work-in-progress paper, we explore the potential of such LLMs for computing educators and learners, as we analyze the feedback it generates to a given input containing program code. In particular, we aim at (1) exploring how an LLM like ChatGPT responds to students seeking help with their introductory programming tasks, and (2) identifying feedback types in its responses. To achieve these goals, we used students' programming sequences from a dataset gathered within a CS1 course as input for ChatGPT along with questions required to elicit feedback and correct solutions. The results show that ChatGPT performs reasonably well for some of the introductory programming tasks and student errors, which means that students can potentially benefit. However, educators should provide guidance on how to use the provided feedback, as it can contain misleading information for novices.