5 papers
Can LLM Code Explanations Adapt to Diverse Problem-Solvers' Needs?
Andrew Anderson, David Piorkowski, Justin Weisz +2
Large language model (LLM) code explanations can support people in solving code-related problems, yet prior work has shown that people have diverse problem-solving styles. If expla…
Thinking Less, Trusting More: GenAI's Impacts on Students' Cognitive Habits
Rudrajit Choudhuri, Christopher Sanchez, Margaret Burnett +1
Objectives: When students use generative AI in coursework, what are its persistent effects on their intellectual development? We investigate (RQ1-How) how students' trust in and ro…
"Over-the-Hood" AI Inclusivity Bugs and How 3 AI Product Teams Found and Fixed Them
Andrew Anderson, Fatima A. Moussaoui, Jimena Noa Guevara +2
While much research has shown the presence of AI's "under-the-hood" biases (e.g., algorithmic, training data, etc.), what about "over-the-hood" inclusivity biases: barriers in user…
An LLM's Attempts to Adapt to Diverse Software Engineers' Problem-Solving Styles: More Inclusive & Equitable?
Andrew Anderson, David Piorkowski, Margaret Burnett +1
Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. W…
Measuring SES-related traits relating to technology usage: Two validated surveys
Chimdi Chikezie, Pannapat Chenpaiseng, Puja Agarwal +9
Software producers are now recognizing the importance of improving their products' suitability for diverse populations, but little attention has been given to measurements to shed…