15 citations · 42 across the 7 of their papers we have counts for
7 papers
Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
James Prather, Brent N. Reeves, Paul Denny +11
Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction…
From Automation to Cognition: Redefining the Roles of Educators and Generative AI in Computing Education
Tony Haoran Feng, Andrew Luxton-Reilly, Burkhard C. Wünsche +1
Generative Artificial Intelligence (GenAI) offers numerous opportunities to revolutionise teaching and learning in Computing Education (CE). However, educators have expressed conce…
Automating Personalized Parsons Problems with Customized Contexts and Concepts
Andre del Carpio Gutierrez, Paul Denny, Andrew Luxton-Reilly
Parsons problems provide useful scaffolding for introductory programming students learning to write code. However, generating large numbers of high-quality Parsons problems that ap…
Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models
James Prather, Paul Denny, Juho Leinonen +7
Large Language Models (LLMs) have upended decades of pedagogy in computing education. Students previously learned to code through \textit{writing} many small problems with less emp…
Prompt Problems: A New Programming Exercise for the Generative AI Era
Paul Denny, Juho Leinonen, James Prather +4
Large Language Models (LLMs) are revolutionizing the field of computing education with their powerful code-generating capabilities. Traditional pedagogical practices have focused o…
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…