473 citations · 2.3k across the 48 of their papers we have counts for
52 papers
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…
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…
Prompts First, Finally
Brent N. Reeves, James Prather, Paul Denny +4
Generative AI (GenAI) and large language models in particular, are disrupting Computer Science Education. They are proving increasingly capable at more and more challenges. Some ed…
Evaluating Language Models for Generating and Judging Programming Feedback
Charles Koutcheme, Nicola Dainese, Arto Hellas +4
The emergence of large language models (LLMs) has transformed research and practice across a wide range of domains. Within the computing education research (CER) domain, LLMs have…
Desirable Characteristics for AI Teaching Assistants in Programming Education
Paul Denny, Stephen MacNeil, Jaromir Savelka +2
Providing timely and personalized feedback to large numbers of students is a long-standing challenge in programming courses. Relying on human teaching assistants (TAs) has been ext…
Benchmarking Educational Program Repair
Charles Koutcheme, Nicola Dainese, Sami Sarsa +3
The emergence of large language models (LLMs) has sparked enormous interest due to their potential application across a range of educational tasks. For example, recent work in prog…
"Sometimes You Just Gotta Risk It for the Biscuit": A Portrait of Student Risk-Taking
Juho Leinonen, Paul Denny
Understanding how individuals make decisions involving risk is a fundamental aspect of behavioral research. Despite the ubiquity of risk in various aspects of life, limited empiric…
Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi +5
Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning. Current LLM usage involves conversati…
Automating Personalized Parsons Problems with Customized Contexts and Concepts
Andre del Carpio Gutierrez, Paul Denny, Andrew Luxton-Reilly
Parsons problems provide useful scaffolding for introductory programming students learning to write code. However, generating large numbers of high-quality Parsons problems that ap…
Enhancing Student Engagement in Large-Scale Capstone Courses: An Experience Report
Asma Shakil, Paul Denny
Computer science (CS) capstone courses offer students a valuable opportunity to gain hands-on experience in software development, practice essential soft skills, and enhance their…
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Seth Bernstein, Paul Denny, Juho Leinonen +5
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a goo…
A Picture Is Worth a Thousand Words: Exploring Diagram and Video-Based OOP Exercises to Counter LLM Over-Reliance
Bruno Pereira Cipriano, Pedro Alves, Paul Denny
Much research has highlighted the impressive capabilities of large language models (LLMs), like GPT and Bard, for solving introductory programming exercises. Recent work has shown…
Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills
Paul Denny, David H. Smith, Max Fowler +3
Reading, understanding and explaining code have traditionally been important skills for novices learning programming. As large language models (LLMs) become prevalent, these founda…
CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs
Majeed Kazemitabaar, Runlong Ye, Xiaoning Wang +4
Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may h…