228 citations · 476 across the 4 of their papers we have counts for
4 papers
Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators
Paul Denny, Juho Leinonen, James Prather +4
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…
Computing Education in the Era of Generative AI
Paul Denny, James Prather, Brett A. Becker +7
The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student…
"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
James Prather, Brent N. Reeves, Paul Denny +6
Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likel…
Using Large Language Models to Enhance Programming Error Messages
Juho Leinonen, Arto Hellas, Sami Sarsa +4
A key part of learning to program is learning to understand programming error messages. They can be hard to interpret and identifying the cause of errors can be time-consuming. One…