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20232026
most citedThe Robots are Here: Navigating the Generative AI Revolution in Computing Education

369 citations · 514 across the 23 of their papers we have counts for

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Showing 2024Show all

13 papers · 1 filter

cs.CY2024★ 17 cited

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…

cs.CY2024★ 2 cited

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…

cs.CY2024★ 1 cited

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…

cs.AI2024

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…

cs.CY2024

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,…

cs.CY2024★ 11 cited

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…

cs.CY2024

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…

cs.CY2024★ 3 cited

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…

cs.AI2024★ 5 cited

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…

cs.CY2024★ 89 cited

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…

cs.HC2024

"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…

cs.HC2024★ 6 cited

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…

cs.HC2024

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…