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cs.HC20241 cited

Predictive Anchoring: A Novel Interaction to Support Contextualized Suggestions for Grid Displays

Cynthia Zastudil, Christine Holyfield, June A. Smith +2

Grid displays are the most common form of augmentative and alternative communication device recommended by speech-language pathologists for children. Grid displays present a large…

cs.HC20242 cited

Exploring the use of Generative AI to Support Automated Just-in-Time Programming for Visual Scene Displays

Cynthia Zastudil, Christine Holyfield, Christine Kapp +4

Millions of people worldwide rely on alternative and augmentative communication devices to communicate. Visual scene displays (VSDs) can enhance communication for these individuals…

cs.HC20246 cited

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…

cs.HC2024

Imagining Computing Education Assessment after Generative AI

Stephen MacNeil, Scott Spurlock, Ian Applebaum

In the contemporary landscape of computing education, the ubiquity of Generative Artificial Intelligence has significantly disrupted traditional assessment methods, rendering them…

cs.HC2024

The Effects of Generative AI on Computing Students' Help-Seeking Preferences

Irene Hou, Sophia Metille, Zhuo Li +3

Help-seeking is a critical way for students to learn new concepts, acquire new skills, and get unstuck when problem-solving in their computing courses. The recent proliferation of…

cs.HC202311 cited

Generative AI in Computing Education: Perspectives of Students and Instructors

Cynthia Zastudil, Magdalena Rogalska, Christine Kapp +2

Generative models are now capable of producing natural language text that is, in some cases, comparable in quality to the text produced by people. In the computing education contex…