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• Paper·arXiv — AI in Education (cs.CY)·7 Jul 2026

  Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education

  Kaitlin Riegel, Yan Cathy Hua, Paul Denny, Victor-Alexandru Pădurean et al.

  This exploratory study examined how 919 introductory programming students used text versus voice input when crafting prompts for LLM-powered code generation tasks. Students who typed prompts were more likely to succeed on the first attempt for two of three problems, while editing transcribed voice prompts eliminated the performance gap. Most students preferred and only used text input, though some switched between modalities depending on the problem.

  Original abstract

  Large language models (LLMs) are increasingly integrated into computing education, yet nearly all prior research has focused on text-based interactions. As voice-enabled interfaces become more capable and more common, there is growing interest in understanding how voice input might shape students' use of LLM-powered tools. In this exploratory study, we investigated how introductory programming students interact with Prompt Problems, which are programming tasks that require crafting natural-language prompts to generate correct code. Students (N = 919) solved a series of Prompt Problems with the freedom to select or switch between text and voice input modalities. We collected their prompt submissions as well as post-activity survey responses, then analysed differences in prompt accuracy, persistence, and perspectives by modality. For two of the three problems, we found that students who typed their prompts using text were more likely to have those prompts succeed on the first attempt than students who submitted unedited voice prompts. There was no difference in success rate if students edited their transcribed voice prompts before submission. Across the problems, we found evidence that students who tried voice prompting varied in their usage of modality - perhaps indicating a complementary, or non-preferential approach. However, most students only tried and reported preferring text. Our qualitative analysis revealed how students' perceived the roles of voice and text input in shaping their problem-solving process, as well as the reported drawbacks and advantages of each modality. We discuss implications for future multimodal tools and instructional design in computing education.

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