Fetching the paper…
Reading the bibliography…
The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education.
Research methods in human-computer interaction
Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser. 2017 · 2017
Earlier work this paper cites.
Teacher Attitude towards Use of Chatbots in Routine Teaching
PK Bii, JK Too, and CW Mukwa. 2018 · 2018
Earlier work this paper cites.
Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics
Raquel Chocarro, Mónica Cortiñas, and Gustavo Marcos-Matás. 2023 · 2020
Earlier work this paper cites.
Identification of Variables that Predict Teachers’ Attitudes toward ICT in Higher Education for Teaching and Research: A Study with Regression
Francisco D. Guillén-Gámez and María J. Mayorga-Fernández. 2020 · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
The great transformer: Examining the role of large language models in the political economy of AI
Dieuwertje Luitse and Wiebke Denkena. 2021 · 2021
Earlier work this paper cites.
Confirmation bias and trust: Human factors that influence teachers’ attitudes towards AI-based educational technology
Tanya Nazaretsky, Mutlu Cukurova, Moriah Ariely, and Giora Alexandron. 2021 · 2021
Earlier work this paper cites.
Artificial intelligence in education: Addressing ethical challenges in K-12 settings
Selin Akgun and Christine Greenhow. 2022 · 2022
Earlier work this paper cites.
The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research
Ismail Celik, Muhterem Dindar, Hanni Muukkonen, and Sanna Järvelä. 2022 · 2022
Earlier work this paper cites.
Robosourcing Educational Resources–Leveraging Large Language Models for Learnersourcing
Paul Denny, Sami Sarsa, Arto Hellas, and Juho Leinonen. 2022 · 2022
Earlier work this paper cites.
Ai-driven development is here: Should you worry?
Neil A Ernst and Gabriele Bavota. 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.
Teacher’s Perceptions of Using an Artificial Intelligence-Based Educational Tool for Scientific Writing
Nam Ju Kim and Min Kyu Kim. 2022 · 2022
Cited alongside, same era.
Generating Diverse Code Explanations Using the GPT-3 Large Language Model. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2 (Lugano and Virtual Event, Switzerland) (ICER ’22) . Association for Computing Machinery, New York, NY, USA, 37–39
Stephen MacNeil, Andrew Tran, Dan Mogil, Seth Bernstein, Erin Ross, and Ziheng Huang. 2022 · 2022
Cited alongside, same era.
Github copilot in the classroom: learning to code with AI assistance
Ben Puryear and Gina Sprint. 2022 · 2022
Cited alongside, same era.
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
Using large language models to enhance programming error messages. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 . 563–569
Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, James Prather, and Brett A Becker. 2023 · 2023
Later among the works it cites.
Summary of chatgpt/gpt-4 research and perspective towards the future of large language models
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al · 2023
Later among the works it cites.
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. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 2022
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 . 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.
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.
Exploring Teachers’ Attitudes towards Using Chat GPT
Nayab Iqbal, Hassaan Ahmed, and Kaukab Azhar. 2023 · 2023
Cited alongside, same era.
Examining teachers’ behavioural intention for online teaching after COVID-19 pandemic: A large-scale survey
Hang Khong, Ismail Celik, Tinh T. T. Le, Van Thi Thanh Lai, Andy Nguyen, and Hong Bui. 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
Sam Lau and Philip J Guo. 2023 · 2023
Cited alongside, same era.
James Prather, Paul Denny, Juho Leinonen, Brett A. Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, Stephen MacNeil, Andrew Petersen, Raymond Pettit, Brent N. Reeves, and Jaromir Savelka. 2023a · 2023
Later among the works it cites.
“It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers
James Prather, Brent N. Reeves, Paul Denny, Brett A. Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023b · 2023
Later among the works it cites.
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
Later among the works it cites.
A Case Study in Engineering a Conversational Programming Assistant’s Persona
Steven I Ross, Michael Muller, Fernando Martinez, Stephanie Houde, and Justin D Weisz. 2023 · 2023
Later among the works it cites.
Large language models (gpt) struggle to answer multiple-choice questions about code
Jaromir Savelka, Arav Agarwal, Christopher Bogart, and Majd Sakr. 2023 · 2023
Later among the works it cites.
Prompt Problems: A New Programming Exercise for the Generative AI Era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (Portland, OR, USA) (SIGCSE 2024) . ACM, NY, USA, 7 pages
Paul Denny, Juho Leinonen, James Prather, Andrew Luxton-Reilly, Thezyrie Amarouche, Brett A. Becker, and Brent Reeves. 2024a · 2024
Closest in time.
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. 2024b · 2024
Closest in time.
More than calculators: Why large language models threaten learning, teaching, and education
Amy J. Ko. [n. d.] · 2024
Closest in time.