# Multimodal Programming in Computer Science with Interactive Assistance Powered by Large Language Model

arXiv: https://arxiv.org/abs/2503.06552 · HTML: https://mlsanity.com/paper/2503.06552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2025.

## Built on by (most cited first, 4 of 4)

- [Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets](https://mlsanity.com/paper/2506.09851/) (2025)
- [ViMoNet: A Multimodal Vision-Language Framework for Human Behavior Understanding from Motion and Video](https://mlsanity.com/paper/2508.09818/) (2025)
- [Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengali](https://mlsanity.com/paper/2601.17764/) (2026)
- [Pattern Recognition Tasks with Personalized Federated Learning](https://mlsanity.com/paper/2605.27816/) (2026)

## Abstract (arXiv)

LLM chatbot interfaces allow students to get instant, interactive assistance with homework, but doing so carelessly may not advance educational objectives. In this study, an interactive homework help system based on DeepSeek R1 is developed and first implemented for students enrolled in a large computer science beginning programming course. In addition to an assist button in a well-known code editor, our assistant also has a feedback option in our command-line automatic evaluator. It wraps student work in a personalized prompt that advances our educational objectives without offering answers straight away. We have discovered that our assistant can recognize students' conceptual difficulties and provide ideas, plans, and template code in pedagogically appropriate ways. However, among other mistakes, it occasionally incorrectly labels the correct student code as incorrect or encourages students to use correct-but-lesson-inappropriate approaches, which can lead to long and frustrating journeys for the students. After discussing many development and deployment issues, we provide our conclusions and future actions.