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

arXiv: https://arxiv.org/abs/2307.16364 · HTML: https://mlsanity.com/paper/2307.16364/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Using Large Language Models to Enhance Programming Error Messages](https://mlsanity.com/paper/2210.11630/) (2022)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?](https://mlsanity.com/paper/2303.11146/) (2023)
- [StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code](https://mlsanity.com/paper/2306.04556/) (2023)

## Built on by (most cited first, 17 of 17)

- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception](https://mlsanity.com/paper/2310.13712/) (2023)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems](https://mlsanity.com/paper/2311.04926/) (2023)
- [How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course](https://mlsanity.com/paper/2407.20792/) (2024)
- [Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT](https://mlsanity.com/paper/2405.19132/) (2024)
- ["ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses](https://mlsanity.com/paper/2404.17443/) (2024)
- [Substance Beats Style: Why Beginning Students Fail to Code with LLMs](https://mlsanity.com/paper/2410.19792/) (2024)
- [Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review](https://mlsanity.com/paper/2403.15274/) (2024)
- [Pinning "Reflection" on the Agenda: Investigating Reflection in Human-LLM Co-Creation for Creative Coding](https://mlsanity.com/paper/2402.09750/) (2024)
- ["Give me the code" -- Log Analysis of First-Year CS Students' Interactions With GPT](https://mlsanity.com/paper/2411.17855/) (2024)
- [Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Models](https://mlsanity.com/paper/2412.11088/) (2024)
- [The Role of Generative AI in Software Student CollaborAItion](https://mlsanity.com/paper/2501.14084/) (2025)
- [Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development](https://mlsanity.com/paper/2503.02532/) (2025)
- [Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications](https://mlsanity.com/paper/2511.04144/) (2025)
- [Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module](https://mlsanity.com/paper/2508.13962/) (2025)

## Abstract (arXiv)

With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice. They have created an urgent need for educators to rethink pedagogical approaches and teaching strategies for newly emerging skill sets. Traditional approaches to learning programming have focused on frequent and repeated practice at writing code. The ease with which code can now be generated has resulted in a shift in focus towards reading, understanding and evaluating LLM-generated code. In parallel with this shift, a new essential skill is emerging -- the ability to construct good prompts for code-generating models. This paper introduces a novel pedagogical concept known as a `Prompt Problem', designed to help students learn how to craft effective prompts for LLMs. A Prompt Problem challenges a student to create a natural language prompt that leads an LLM to produce the correct code for a specific problem. To support the delivery of Prompt Problems at scale, in this paper we also present a novel tool called Promptly which hosts a repository of Prompt Problems and automates the evaluation of prompt-generated code. We report empirical findings from a field study in which Promptly was deployed in a first-year Python programming course (n=54). We explore student interactions with the tool and their perceptions of the Prompt Problem concept. We found that Promptly was largely well-received by students for its ability to engage their computational thinking skills and expose them to new programming constructs. We also discuss avenues for future work, including variations on the design of Prompt Problems and the need to study their integration into the curriculum and teaching practice.