# GenAI Voice Mode in Programming Education

arXiv: https://arxiv.org/abs/2509.10596 · HTML: https://mlsanity.com/paper/2509.10596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 23 of 23)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Exploring the Potential of Large Language Models to Generate Formative Programming Feedback](https://mlsanity.com/paper/2309.00029/) (2023)
- [User Interaction Patterns and Breakdowns in Conversing with LLM-Powered Voice Assistants](https://mlsanity.com/paper/2309.13879/) (2023)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [Student-AI Interaction: A Case Study of CS1 students](https://mlsanity.com/paper/2407.00305/) (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)
- [Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT](https://mlsanity.com/paper/2405.19132/) (2024)
- [Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation](https://mlsanity.com/paper/2405.06681/) (2024)
- [You're (Not) My Type -- Can LLMs Generate Feedback of Specific Types for Introductory Programming Tasks?](https://mlsanity.com/paper/2412.03516/) (2024)
- [Unlimited Practice Opportunities: Automated Generation of Comprehensive, Personalized Programming Tasks](https://mlsanity.com/paper/2503.11704/) (2025)
- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)
- [The Role of Generative AI in Software Student CollaborAItion](https://mlsanity.com/paper/2501.14084/) (2025)
- [That's Not the Feedback I Need! -- Student Engagement with GenAI Feedback in the Tutor Kai](https://mlsanity.com/paper/2506.20433/) (2025)
- [Cross-Cultural Validation of Partner Models for Voice User Interfaces](https://mlsanity.com/paper/2405.09002/) (2024)
- [Students' Feedback Requests and Interactions with the SCRIPT Chatbot: Do They Get What They Ask For?](https://mlsanity.com/paper/2507.17258/) (2025)

## Abstract (arXiv)

Real-time voice interfaces using multimodal Generative AI (GenAI) can potentially address the accessibility needs of novice programmers with disabilities (e.g., related to vision). Yet, little is known about how novices interact with GenAI tools and their feedback quality in the form of audio output. This paper analyzes audio dialogues from nine 9th-grade students using a voice-enabled tutor (powered by OpenAI's Realtime API) in an authentic classroom setting while learning Python. We examined the students' voice prompts and AI's responses (1210 messages) by using qualitative coding. We also gathered students' perceptions via the Partner Modeling Questionnaire. The GenAI Voice Tutor primarily offered feedback on mistakes and next steps, but its correctness was limited (71.4% correct out of 416 feedback outputs). Quality issues were observed, particularly when the AI attempted to utter programming code elements. Students used the GenAI voice tutor primarily for debugging. They perceived it as competent, only somewhat human-like, and flexible. The present study is the first to explore the interaction dynamics of real-time voice GenAI tutors and novice programmers, informing future educational tool design and potentially addressing accessibility needs of diverse learners.