195 citations · 261 across the 10 of their papers we have counts for
10 papers
Two's a Crowd: Human and AI-Based Copresence for Developers with ADHD
Veronica Pimenova, Seth Bernstein, Shalini Madan +2
Effective collaboration and communication are vital to developer productivity and well-being, yet remain constrained by human factors such as attention, intrinsic motivation, and i…
Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education
Seth Bernstein, Naaz Sibia
Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analogies compare an unfamiliar con…
Exploring the Value of Diverse LLM Explanations in Introductory Programming
Seth Bernstein, Paul Denny, Juho Leinonen +4
Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportunities for computer science ed…
Beyond the Benefits: A Systematic Review of the Harms and Consequences of Generative AI in Computing Education
Seth Bernstein, Ashfin Rahman, Nadia Sharifi +2
Generative artificial intelligence (GenAI) has already had a big impact on computing education with prior research identifying many benefits. However, recent studies have also iden…
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Seth Bernstein, Paul Denny, Juho Leinonen +5
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a goo…
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…