[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83001-en":3,"doc-seo-83001-105":29,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83001,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",6,"Technology","Say What Examining Text and Voice Input Modalities for Prompt Based Programming in Computing Education","Large language models are increasingly used in computing education, but most research emphasizes text-only interaction. As voice-enabled interfaces advance, the study examines how voice input influences students’ use of LLM-powered tools for Prompt Problems, where learners craft natural-language prompts to generate correct code. In an exploratory study with 919 students, prompt submissions and surveys were analyzed across text versus voice modalities to compare accuracy, persistence, and student perspectives. Results show typed prompts outperform unedited voice prompts on two tasks, with no difference after editing transcripts, and qualitative findings discuss modality roles and trade-offs.","Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education  \nKaitlin Riegel  \nUniversity of Auckland Auckland, New Zealand [kaitlin.riegel@auckland.ac.nz](kaitlin.riegel@auckland.ac.nz)  \nYan Cathy Hua  \nUniversity of Auckland Auckland, New Zealand [yhua219@aucklanduni.ac.nz](yhua219@aucklanduni.ac.nz)  \nPaul Denny University of Auckland Auckland, New Zealand [paul@cs.auckland.ac.nz](paul@cs.auckland.ac.nz)  \nVictor-Alexandru Pădurean  \nMPI-SWS Saarbrücken, Germany [vpadurea@mpi-sws.org](vpadurea@mpi-sws.org)  \nJuho Leinonen  \nAalto University Espoo, Finland [juho.2.leinonen@aalto.fi](juho.2.leinonen@aalto.fi)  \nJames Prather  \nAbilene Christian University Abilene, TX, USA [james.prather@acu.edu](james.prather@acu.edu)  \nAdish Singla  \nMPI-SWS Saarbrücken, Germany [adishs@mpi-sws.org](adishs@mpi-sws.org)  \narXiv :2607 .05808v 1 [ cs .CY] 7 Jul 2026  \nAbstract  \nLarge 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 submissionsas 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.  \nITiCSE 2026, Madrid, Spain  \n© 2026 Copyright held by the owner/author(s) .  \nThis is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 31stACM Conference on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2026), July 10–15, 2026, Madrid, Spain, [https://doi.org/10.1145/3803400.3809397](https://doi.org/10.1145/3803400.3809397) .  \nCCS Concepts  \n• Social and professional topics → Computing education.  \nKeywords  \nNatural language programming; Code-generating AI; Prompt problems; Voice-enabled prompting; Student perceptions  \nACM Reference Format:  \nKaitlin Riegel, Yan Cathy Hua, Paul Denny, Victor-Alexandru Pădurean, Juho Leinonen, James Prather, and Adish Singla. 2026. Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education. In Proceedings of the 31stACM Conference on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2026), July 10–15, 2026, Madrid, Spain. ACM, New York, NY, USA, 7 pages. [https:](https:)//[doi.org/10.1145/3803400.3809397](doi.org/10.1145/3803400.3809397)  \n1 Introduction  \nVoice-based assistants have become increasingly capable, evolving from simple command tools to conversational systems that can understand context and support a w","cbCaiqAqyBeBHURv","https://ap.wps.com/l/cbCaiqAqyBeBHURv","pdf",629195,1,7,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"What were the key findings about typing versus voice prompting?\",\"answer\":\"For two of three problems, students who typed prompts were more likely to succeed on the first attempt than those submitting unedited voice prompts. 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