473 citations · 2.3k across the 48 of their papers we have counts for
11 papers · 1 filter
Correctness, Convergence, and AI-Generated Code Detection: A Longitudinal Study of Student and Large Language Model Code in Introductory Programming
Runlong Ye, Jing Fan, Angela Zavaleta Bernuy +4
Large language models can generate plausible solutions to programming assignments, making it tempting to detect their use by matching student code against a reference bank of gener…
Guardrails or Roadblocks? Effects of Pedagogical Style and Context Awareness in AI Teaching Assistants for Programming
Madeleine Eastwood, Harshith Narne, Joseph Hilby +3
AI teaching assistants (AI TAs) backed by large language models (LLMs) and pedagogical guardrails are increasingly being integrated into programming courses, providing students wit…
Testing Our Foundations: Citation Trends, Errors, and Emerging Hallucinations in the Computing Education Literature
Paul Denny, Gweneth Barbre, Musa Blake +5
Accurate references are foundational to scholarly work, enabling verification, attribution, and systematic review. However, the rapid adoption of large language models has introduc…
Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications
Nasser Giacaman, Valerio Terragni, Paul Denny +1
As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shi…
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 Pădurean, 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 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…
Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content
Juho Leinonen, Paul Denny
As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can…
Reshaping Undergraduate Computer Science Education in the Generative AI Era
Yi-Chieh Lee, Nattapat Boonprakong, Yugin Tan +20
Generative AI represents a turning point for Computer Science (CS) education. In recent decades, post-secondary CS education has largely focused on what has been seen as practical…
Building AI Companions that Prioritise Learning over Performance
Hassan Khosravi, Dragan Gasevic, Shazia Sadiq +7
Large language models (LLMs) are rapidly transforming knowledge work by improving the quality and efficiency of tasks such as writing, coding, and data analysis. However, their gro…
RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization
Qiming Bao, Juho Leinonen, Paul Denny +1
Direct Preference Optimization (DPO), the efficient alternative to PPO-based RLHF, falls short on knowledge-intensive generation: standard preference signals from human annotators…