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With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice.
CodeWrite: Supporting Student-Driven Practice of Java. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (Dallas, TX, USA) (SIGCSE ’11) . Association for Computing Machinery, New York, NY, USA, 471–476
Paul Denny, Andrew Luxton-Reilly, Ewan Tempero, and Jacob Hendrickx. 2011 · 2011
Earlier work this paper cites.
On the Differences between Correct Student Solutions. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (Canterbury, England, UK) (ITiCSE ’13) . Association for Computing Machinery, New York, NY, USA, 177–182
Andrew Luxton-Reilly, Paul Denny, Diana Kirk, Ewan Tempero, and Se-Young Yu. 2013 · 2013
Earlier work this paper cites.
Coderunner: A Tool for Assessing Computer Programming Skills
Richard Lobb and Jenny Harlow. 2016 · 2016
Earlier work this paper cites.
A Systematic Literature Review of Automated Feedback Generation for Programming Exercises
Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. 2018 · 2018
Earlier work this paper cites.
Many Small Programs in CS1: Usage Analysis from Multiple Universities. In 2019 ASEE Annual Conference & Exposition " . ASEE Conferences, Tampa, Florida, 1–13
Joe Michael Allen, Kelly Downey, Kris Miller, Alex Daniel Edgcomb, and Frank Vahid. 2019 · 2019
Earlier work this paper cites.
A Closer Look at Metacognitive Scaffolding: Solving Test Cases Before Programming. In Proceedings of the 19th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling ’19) . Association for Computing Machinery, New York, NY, USA, Article 11, 10 pages
Paul Denny, James Prather, Brett A. Becker, Zachary Albrecht, Dastyni Loksa, and Raymond Pettit. 2019 · 2019
Earlier work this paper cites.
A Review of Research on Parsons Problems. In Proceedings of the Twenty-Second Australasian Computing Education Conference (Melbourne, VIC, Australia) (ACE’20) . Association for Computing Machinery, New York, NY, USA, 195–202
Yuemeng Du, Andrew Luxton-Reilly, and Paul Denny. 2020 · 2020
Earlier work this paper cites.
Parsons Problems and Beyond: Systematic Literature Review and Empirical Study Designs. In Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education (Dublin, Ireland) (ITiCSE-WGR ’22) . Association for Computing Machinery, New York, NY, USA, 191–234
Barbara J. Ericson, Paul Denny, James Prather, Rodrigo Duran, Arto Hellas, Juho Leinonen, Craig S. Miller, Briana B. Morrison, Janice L. Pearce, and Susan H. Rodger. 2022 · 2022
Earlier work this paper cites.
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Proceedings of the 24th Australasian Computing Education Conference (Virtual Event, Australia) (ACE ’22) . Association for Computing Machinery, New York, NY, USA, 10–19
James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather. 2022 · 2022
Earlier work this paper cites.
Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 (Lugano and Virtual Event, Switzerland) (ICER ’22) . Association for Computing Machinery, New York, NY, USA, 27–43
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen. 2022 · 2022
Earlier work this paper cites.
Solving Probability And Statistics Problems By Probabilistic Program Synthesis At Human Level And Predicting Solvability. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II (Durham, United Kingdom). Springer-Verlag, Berlin, Heidelberg, 612–615
Leonard Tang, Elizabeth Ke, Nikhil Singh, Bo Feng, Derek Austin, Nakul Verma, and Iddo Drori. 2022 · 2022
Earlier work this paper cites.
StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Hannah McLean Babe, Sydney Nguyen, Yangtian Zi, Arjun Guha, Molly Q Feldman, and Carolyn Jane Anderson. 2023 · 2023
Cited alongside, same era.
Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 500–506
Brett A. Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos. 2023 · 2023
Cited alongside, same era.
GPT-3 vs Object Oriented Programming Assignments: An Experience Report. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023) . Association for Computing Machinery, New York, NY, USA, 61–67
Bruno Pereira Cipriano and Pedro Alves. 2023 · 2023
Cited alongside, same era.
Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 931–937
Stephen MacNeil, Andrew Tran, Arto Hellas, Joanne Kim, Sami Sarsa, Paul Denny, Seth Bernstein, and Juho Leinonen. 2023b · 2023
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On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023) . Association for Computing Machinery, New York, NY, USA, 47–53
Kamil Malinka, Martin Peresíni, Anton Firc, Ondrej Hujnák, and Filip Janus. 2023 · 2023
Closest in time.
Empowering Education with LLMs-The Next-Gen Interface and Content Generation. In International Conference on Artificial Intelligence in Education . Springer, 32–37
Steven Moore, Richard Tong, Anjali Singh, Zitao Liu, Xiangen Hu, Yu Lu, Joleen Liang, Chen Cao, Hassan Khosravi, Paul Denny, Chris Brooks, and John Stamper. 2023 · 2023
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Chat Overflow: Artificially Intelligent Models for Computing Education - RenAIssance or ApocAIypse?. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023) . Association for Computing Machinery, New York, NY, USA, 3–4
Paul Denny, Brett A. Becker, Juho Leinonen, and James Prather. 2023a · 2023
Cited alongside, same era.
Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 1136–1142
Paul Denny, Viraj Kumar, and Nasser Giacaman. 2023b · 2023
Cited alongside, same era.
My AI Wants to Know If This Will Be on the Exam: Testing OpenAI’s Codex on CS2 Programming Exercises. In Proceedings of the 25th Australasian Computing Education Conference (Melbourne, VIC, Australia) (ACE ’23) . Association for Computing Machinery, New York, NY, USA, 97–104
James Finnie-Ansley, Paul Denny, Andrew Luxton-Reilly, Eddie Antonio Santos, James Prather, and Brett A. Becker. 2023 · 2023
Cited alongside, same era.
Studying the Effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 455, 23 pages
Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J. Ericson, David Weintrop, and Tovi Grossman. 2023 · 2023
Cited alongside, same era.
From “Ban It Till We Understand It” to “Resistance is Futile”’: How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools such as ChatGPT and GitHub Copilot. ACM ICER 2023 to appear
Sam Lau and Philip J Guo. 2023 · 2023
Cited alongside, same era.
Using Large Language Models to Enhance Programming Error Messages. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 563–569
Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, James Prather, and Brett A. Becker. 2023b · 2023
Cited alongside, same era.
The Implications of Large Language Models for CS Teachers and Students. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 1255
Stephen MacNeil, Joanne Kim, Juho Leinonen, Paul Denny, Seth Bernstein, Brett A. Becker, Michel Wermelinger, Arto Hellas, Andrew Tran, Sami Sarsa, James Prather, and Viraj Kumar. 2023a · 2023
Cited alongside, same era.
Computing Education in the Era of Generative AI
Paul Denny, James Prather, Brett A. Becker, James Finnie-Ansley, Arto Hellas, Juho Leinonen, Andrew Luxton-Reilly, Brent N. Reeves, Eddie Antonio Santos, and Sami Sarsa. 2023c
Cited in the paper.
Comparing Code Explanations Created by Students and Large Language Models
Juho Leinonen, Paul Denny, Stephen MacNeil, Sami Sarsa, Seth Bernstein, Joanne Kim, Andrew Tran, and Arto Hellas. 2023a
Cited in the paper.
Metacodenition: Scaffolding the Problem-Solving Process for Novice Programmers. In Proceedings of the 25th Australasian Computing Education Conference (Melbourne, VIC, Australia) (ACE ’23) . Association for Computing Machinery, New York, NY, USA, 59–68
Yulia Pechorina, Keith Anderson, and Paul Denny. 2023 · 2023
Closest in time.
Learn AI-Assisted Python Programming: With Github Copilot and ChatGPT
Leo Porter and Daniel Zingaro. 2023 · 2023
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James Prather, Brent N. Reeves, Paul Denny, Brett A. Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023 · 2023
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Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt Variations. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023) . Association for Computing Machinery, New York, NY, USA, 299–305
Brent Reeves, Sami Sarsa, James Prather, Paul Denny, Brett A. Becker, Arto Hellas, Bailey Kimmel, Garrett Powell, and Juho Leinonen. 2023 · 2023
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K-12 Computing Education for the AI Era: From Data Literacy to Data Agency. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023) . Association for Computing Machinery, New York, NY, USA, 1–2
Matti Tedre and Henriikka Vartiainen. 2023 · 2023
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Using GitHub Copilot to Solve Simple Programming Problems. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 172–178
Michel Wermelinger. 2023 · 2023
Closest in time.
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C. Schmidt. 2023 · 2023
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