195 citations · 250 across the 5 of their papers we have counts for
5 papers
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…
Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances
Stephen MacNeil, Andrew Tran, Joanne Kim +3
To help users do complex work, researchers have developed techniques to integrate AI and human intelligence into user interfaces (UIs). With the recent introduction of large langua…
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…
Automatically Generating CS Learning Materials with Large Language Models
Stephen MacNeil, Andrew Tran, Juho Leinonen +5
Recent breakthroughs in Large Language Models (LLMs), such as GPT-3 and Codex, now enable software developers to generate code based on a natural language prompt. Within computer s…
Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book
Stephen MacNeil, Andrew Tran, Arto Hellas +5
Advances in natural language processing have resulted in large language models (LLMs) that are capable of generating understandable and sensible written text. Recent versions of th…