Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks

Learning to communicate with code-generating AI is an emerging skill for novice programmers. 'Prompt Problems' — having students solve computational tasks by writing natural-language prompts for code-generating models — is a recent pedagogical approach, yet little was known about the specific prompt-level mistakes novices make, the computational details they fail to communicate, and how they recover when generated code is wrong. Padurean et al. (2026) studied attempts by more than 900 students to solve dialogue-based Prompt Problems in a CS1 course, analyzing the misconceptions and repair strategies that surface when learners must specify intent in English rather than code. The study extends the Prompt Based Programming Lesson lineage and the broader Reshaping CS Education GenAI movement, situating prompt-writing as a core AI Literacy competency within CS Education. It also connects to Programming ITS (where natural-language specification has long been a goal) and highlights the need for Scaffolding that helps novices articulate computational detail. Findings on Student Experience and recovery behavior inform Higher Ed course design as LLM pair-programming becomes routine.

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Victor-Alexandru Padurean, Kaitlin Riegel, Gweneth Barbre, Musa Blake, Paul Denny, Adish Singla (2026). Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks. arXiv:2607.05034. arXiv:2607.05034 [cs.CY]. - Voice Text Prompt Problems Computing Education — Text vs voice modality on Prompt Problems