Improve voice prompting usability and offer native-language voice prompting before wider instructional adoption¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's discussion and future-work sections recommend improving voice input so students gain "student control over success", listing better hardware, appropriate environments, native language options, explicit instruction, and adaptive transcription. It also recommends future studies examine voice prompting in students' native languages and evaluate retention effects with more complex tasks in controlled settings.

Design Implications¶

Context¶

Requirements¶

• Voice tools need better hardware support, appropriate (quiet) environments, and adaptive transcription to give students control over success.

Constraints¶

• Current results are limited by self-selection into modality, few voice users, simple short tasks, and unmeasured transcription delay; causality cannot be established.

Target Learners¶

• Introductory programming students, including non-native English speakers

Target Learning Goals¶

• Effective natural-language prompting of LLM code generators
• Deeper cognitive engagement and self-explanation during problem solving

Related Strategies¶

• Teacher strategies for alleviating EFL speaking anxiety: warm atmosphere, encouragement, L1 use, and learner-centered activities

Examples¶

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Key Sources¶

• Kaitlin 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. Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2026). https://doi.org/10.1145/3803400.3809397