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With their remarkable ability to generate code, large language models (LLMs)are a transformative technology for computing education practice. They havecreated an urgent need for educators to rethink pedagogical approaches andteaching strategies for newly emerging skill sets. Traditional approaches tolearning programming have focused on frequent and repeated practice at writingcode. The ease with which code can now be generated has resulted in a shift infocus towards reading, understanding and evaluating LLM-generated code. Inparallel with this shift, a new essential skill is emerging -- the ability toconstruct good prompts for code-generating models. This paper introduces anovel pedagogical concept known as a `Prompt Problem', designed to helpstudents learn how to craft effective prompts for LLMs. A Prompt Problemchallenges a student to create a natural language prompt that leads an LLM toproduce the correct code for a specific problem. To support the delivery ofPrompt Problems at scale, in this paper we also present a novel tool calledPromptly which hosts a repository of Prompt Problems and automates theevaluation of prompt-generated code. We report empirical findings from a fieldstudy in which Promptly was deployed in a first-year Python programming course(n=54). We explore student interactions with the tool and their perceptions ofthe Prompt Problem concept. We found that Promptly was largely well-received bystudents for its ability to engage their computational thinking skills andexpose them to new programming constructs. We also discuss avenues for futurework, including variations on the design of Prompt Problems and the need tostudy their integration into the curriculum and teaching practice.

Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators