5 papers
Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education
Kaitlin Riegel, Yan Cathy Hua, Paul Denny +4
Large language models (LLMs) are increasingly integrated into computing education, yet nearly all prior research has focused on text-based interactions. As voice-enabled interfaces…
When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code
Victor-Alexandru PÄdurean, Kaitlin Riegel, Alkis Gotovos +6
As Generative AI (GenAI) becomes increasingly central to software development, CS education is integrating prompt-centered workflows where students describe intended program behavi…
Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks
Victor-Alexandru PÄdurean, Kaitlin Riegel, Gweneth Barbre +7
Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computation…
Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms
Nicholas Gardella, James Prather, Juho Leinonen +3
Code-generating Artificial Intelligence has gained popularity within both professional and educational programming settings over the past several years. While research and pedagogy…
From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions
Ali Alfageeh, Sadegh AlMahdi Kazemi Zarkouei, Daye Nam +9
Background and Context. The increasing integration of large language models (LLMs) in computing education presents an emerging challenge in understanding how students use LLMs and…