369 citations · 514 across the 23 of their papers we have counts for
23 papers
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…
To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks
Jessica Hutchison, Ian Tyler Applebaum, Kenneth Angelikas +6
AI code completion tools, such as Github Copilot, provide students with code suggestions to help them write programs. However, recent qualitative studies suggest that students fail…
Exploring the Value of Diverse LLM Explanations in Introductory Programming
Seth Bernstein, Paul Denny, Juho Leinonen +4
Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportunities for computer science ed…
Lightweight Social Computing Tools for Undergraduate Research Community Building
Noel Chacko, Hannah Vy Nguyen, Sophie Chen +1
Many barriers exist when new members join a research community, including impostor syndrome. These barriers can be especially challenging for undergraduate students who are new to…
Probing the Unknown: Exploring Student Interactions with Probeable Problems at Scale in Introductory Programming
Paul Denny, Viraj Kumar, Stephen MacNeil +2
Introductory programming courses often rely on small code-writing exercises that have clearly specified problem statements. This limits opportunities for students to practice how t…
Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails
Amanpreet Kapoor, Marc Diaz, Stephen MacNeil +2
AI-powered chatbots and digital teaching assistants (AI TAs) are gaining popularity in programming education, offering students timely and personalized feedback. Despite their pote…
The Role of Generative AI in Software Student CollaborAItion
Natalie Kiesler, Jacqueline Smith, Juho Leinonen +3
Collaboration is a crucial part of computing education. The increase in AI capabilities over the last couple of years is bound to profoundly affect all aspects of systems and softw…
The Evolving Usage of GenAI by Computing Students
Irene Hou, Hannah Vy Nguyen, Owen Man +1
Help-seeking is a critical aspect of learning and problem-solving for computing students. Recent research has shown that many students are aware of generative AI (GenAI) tools; how…
Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
James Prather, Juho Leinonen, Natalie Kiesler +12
Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and…
Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
James Prather, Brent N. Reeves, Paul Denny +11
Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction…
Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Models
Sebastian Gutierrez, Irene Hou, Jihye Lee +6
Recent advancements in generative AI systems have raised concerns about academic integrity among educators. Beyond excelling at solving programming problems and text-based multiple…
LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education
Juho Leinonen, Paul Denny, Olli Kiljunen +3
There is a great need for data in computing education research. Data is needed to understand how students behave, to train models of student behavior to optimally support students,…
Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing Students
Stephen MacNeil, Magdalena Rogalska, Juho Leinonen +3
Large language models (LLMs) present an exciting opportunity for generating synthetic classroom data. Such data could include code containing a typical distribution of errors, simu…
Integrating Natural Language Prompting Tasks in Introductory Programming Courses
Chris Kerslake, Paul Denny, David H Smith +4
Introductory programming courses often emphasize mastering syntax and basic constructs before progressing to more complex and interesting programs. This bottom-up approach can be f…
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…
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
James Prather, Brent Reeves, Juho Leinonen +6
Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounte…
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…
"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…
Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models
James Prather, Paul Denny, Juho Leinonen +7
Large Language Models (LLMs) have upended decades of pedagogy in computing education. Students previously learned to code through \textit{writing} many small problems with less emp…
The Effects of Generative AI on Computing Students' Help-Seeking Preferences
Irene Hou, Sophia Metille, Zhuo Li +3
Help-seeking is a critical way for students to learn new concepts, acquire new skills, and get unstuck when problem-solving in their computing courses. The recent proliferation of…
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Stephen MacNeil, Paul Denny, Andrew Tran +5
Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a…
More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems
Irene Hou, Owen Man, Sophie Mettille +3
The advent of large language models is reshaping computing education. Recent research has demonstrated that these models can produce better explanations than students, answer multi…
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
James Prather, Paul Denny, Juho Leinonen +12
Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret so…