# An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration

arXiv: https://arxiv.org/abs/2606.12425 · HTML: https://mlsanity.com/paper/2606.12425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 12 of 12)

- [Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors](https://mlsanity.com/paper/2306.17156/) (2023)
- [Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models](https://mlsanity.com/paper/2302.04662/) (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)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Automated Clustering and Program Repair for Introductory Programming Assignments](https://mlsanity.com/paper/1603.03165/) (2016)
- [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)
- [Automated Grading and Feedback Tools for Programming Education: A Systematic Review](https://mlsanity.com/paper/2306.11722/) (2023)
- [Desirable Characteristics for AI Teaching Assistants in Programming Education](https://mlsanity.com/paper/2405.14178/) (2024)
- [Evaluating the Application of Large Language Models to Generate Feedback in Programming Education](https://mlsanity.com/paper/2403.09744/) (2024)
- [Automated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions](https://mlsanity.com/paper/2505.10913/) (2025)
- [SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs](https://mlsanity.com/paper/2410.16513/) (2024)
- [Facilitating Instructors-LLM Collaboration for Problem Design in Introductory Programming Classrooms](https://mlsanity.com/paper/2504.01259/) (2025)

## Abstract (arXiv)

Active learning is widely recognized as an effective approach for improving learning outcomes in introductory programming courses. However, insufficient instructional support often limits students' access to timely, personalized feedback, which is crucial for mastering foundational programming concepts. Although recent advances in AI, particularly large language models, offer scalable opportunities for feedback, concerns about explainability and reliability remain. In this paper, we present an AI-driven classroom assistant that leverages an explainable AI model to analyze student code, map logical errors to instructor-identified misconceptions, and deliver instructor-authored feedback, thereby grounding reliability in instructor-defined pedagogical knowledge. To evaluate the effectiveness of our framework, we conducted an expert evaluation to examine its alignment with instructor-verified feedback and deployed the system in a classroom setting to assess students' perceptions of its usability. Results indicate that the assistant can provide accurate, instructor-verified feedback to students while fostering a positive experience.