35 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…
Interleaving Natural Language Prompting with Code Editing for Solving Programming Tasks with Generative AI Models
Victor-Alexandru PÄdurean, Alkis Gotovos, Ahana Ghosh +5
Modern computing students often rely on both natural-language prompting and manual code editing to solve programming tasks. Yet we still lack a clear understanding of how these two…
CONTRA: Red-Teaming Configurations of Personalizable Agents
Jonathan Nöther, Adish Singla, Goran Radanovic
Recent tools such as OpenClaw have extended the capabilities of LLM-based agents from simple dialog-based systems to fully autonomous agents. These systems allow personalization of…
Corruption Robust Offline Reinforcement Learning with Human Feedback
Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban +2
We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting. Given an offline dataset of pairs of trajectories along with feedba…