Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computational tasks by writing natural-language prompts for code-generating AI models. However, little is known about the specific prompt-level mistakes novice programmers make, the kinds of computational details they fail to communicate, and what strategies they use to recover when generated code is incorrect. In a CS1 course, we studied attempts by more than 900 students to solve dialogue-based Prompt Problems. We analyzed student reflections, unsuccessful prompts, and reported debugging strategies. Compared to traditional coding tasks, students generally found prompting easier, more enjoyable, and better targeted at developing problem-solving skills. The most common mistakes are related to the omission of key details, suggesting both a failure to acknowledge their importance and over-reliance on AI to infer them. When prompts failed, students focused more on clarifying their intent and reflecting on the provided problem details than on tracing generated code or examining test cases.

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.