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20242026
most citedExploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models

1 citations · 1 across the 12 of their papers we have counts for

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cs.CY2026

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…

cs.CY2026

Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks

Victor-Alexandru Pădurean, 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…

cs.CY2026

Interleaving Natural Language Prompting with Code Editing for Solving Programming Tasks with Generative AI Models

Victor-Alexandru Pădurean, 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…

cs.CY2025

The Right Kind of Help: Evaluating the Effectiveness of Feedback Methods in Elementary-Level Visual Programming

Ahana Ghosh, Liina Malva, Alkis Gotovos +2

We present a large-scale study comparing the effectiveness of various feedback methods in elementary-level programming. While prior work has explored different feedback methods, th…

cs.CY2025

Reflection-Satisfaction Tradeoff: Investigating Impact of Reflection on Student Engagement with AI-Generated Programming Hints

Heeryung Choi, Tung Phung, Mengyan Wu +2

Generative AI tools, such as AI-generated hints, are increasingly integrated into programming education to offer timely, personalized support. However, little is known about how to…

cs.CY2025

Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback

Victor-Alexandru Pădurean, Tung Phung, Nachiket Kotalwar +4

The growing need for automated and personalized feedback in programming education has led to recent interest in leveraging generative AI for feedback generation. However, current a…