CodeVaani: A Multilingual, Voice-Based Code Learning Assistant
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Abstract (arXiv)
Programming education often assumes English proficiency and text-based interaction, creating barriers for students from multilingual regions such as India. We present CodeVaani, a multilingual speech-driven assistant for understanding code, built into Bodhitree [1], a Learning Management System developed at IIT Bombay. It is a voice-enabled assistant that helps learners explore programming concepts in their native languages. The system integrates Indic ASR, a codeaware transcription refinement module, and a code model for generating relevant answers. Responses are provided in both text and audio for natural interaction. In a study with 28 beginner programmers, CodeVaani achieved 75% response accuracy, with over 80% of participants rating the experience positively. Compared to classroom assistance, our framework offers ondemand availability, scalability to support many learners, and multilingual support that lowers the entry barrier for students with limited English proficiency. The demo will illustrate these capabilities and highlight how voice-based AI systems can make programming education more inclusive. Supplementary artifacts and demo video are also made available.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 8 Oct 2025.
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References · 7
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2025Gemma 3 Technical Reportcited by 1,824
- 2022Adaptive Discounting of Implicit Language Models in RNN-Transducerscited by 5
- 2024Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Studycited by 5
- 2024Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languagescited by 5
- 2023Improving RNN-Transducers with Acoustic LookAheadcited by 4
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