An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration
arXiv:2606.12425v1 Announce Type: cross Abstract: 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 exp
Advances in large language models make it possible to deploy scalable AI assistants in educational settings, addressing long-standing needs for personalized feedback in programming education.
Improving the reliability and explainability of AI-driven educational tools will accelerate their adoption, enhancing learning outcomes in critical technical fields and potentially democratizing access to high-quality instruction.
The focus is shifting from general AI feedback to explainable and reliable AI feedback, leveraging instructor collaboration to build trust and efficacy in educational AI systems.
- · Education technology companies
- · Students in STEM fields
- · Programming educators
- · AI-driven learning platforms
- · Traditional tutoring services
- · Inefficient educational models
More students receive timely, personalized feedback in programming, improving their foundational skills.
Increased student proficiency leads to a larger and more skilled pipeline for technical jobs, benefiting the broader technology sector.
The success of instructor-AI collaboration in education sets a precedent for similar models in other professional domains, accelerating adoption of human-AI teaming.
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Read at arXiv cs.AI