473 citations · 1.1k across the 16 of their papers we have counts for
5 papers · 1 filter
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Stephen MacNeil, Paul Denny, Andrew Tran +5
Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a…
Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources
Paul Denny, Hassan Khosravi, Arto Hellas +2
As an increasing number of students move to online learning platforms that deliver personalized learning experiences, there is a great need for the production of high-quality educa…
Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
Arto Hellas, Juho Leinonen, Sami Sarsa +3
Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and…
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…
Comparing Code Explanations Created by Students and Large Language Models
Juho Leinonen, Paul Denny, Stephen MacNeil +5
Reasoning about code and explaining its purpose are fundamental skills for computer scientists. There has been extensive research in the field of computing education on the relatio…