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Large language models (LLMs) have shown great potential for the automatic generation of feedback in a wide range of computing contexts.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, et al · 1910
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Codewebs: Scalable Homework Search for Massive Open Online Programming Courses. In Proc. of the 23rd Int. Conf. on World Wide Web . ACM, 491–502
Andy Nguyen, Christopher Piech, Jonathan Huang, and Leonidas Guibas. 2014 · 2014
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Towards a Systematic Review of Automated Feedback Generation for Programming Exercises. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education . ACM, 41–46
Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. 2016 · 2016
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A Systematic Literature Review of Automated Feedback Generation for Programming Exercises
Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. 2018 · 2018
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Assessing and responding to the growth of computer science undergraduate enrollments
National Academies of Sciences, Engineering, and Medicine. 2018 · 2018
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Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. In AAAI
Mike Wu, M. Mosse, Noah D. Goodman, and C. Piech. 2019 · 2019
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Evaluating language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, et al · 2021
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Measuring Coding Challenge Competence With APPS
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, et al · 2021
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Towards Open Natural Language Feedback Generation for Novice Programmers Using Large Language Models. In Proc. of the 22nd Koli Calling Int. Conf. on Computing Education Research . ACM
Charles Koutcheme. 2022 · 2022
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Automated Assessment in Computer Science Education: A State-of-the-Art Review
José Carlos Paiva, José Paulo Leal, and Álvaro Figueira. 2022 · 2022
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Investigating the Potential of GPT-3 in Providing Feedback for Programming Assessments. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 . 292–298
Rishabh Balse, Bharath Valaboju, Shreya Singhal, Jayakrishnan Madathil Warriem, and Prajish Prasad. 2023 · 2023
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Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language. In Proc. of the 54th ACM Technical Symposium on Computer Science Education V. 1 . ACM, New York, NY, USA, 1136–1142
Paul Denny, Viraj Kumar, and Nasser Giacaman. 2023 · 2023
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Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests. In Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1 . ACM, 93–105
Arto Hellas, Juho Leinonen, Sami Sarsa, Charles Koutcheme, Lilja Kujanpää, and Juha Sorva. 2023 · 2023
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AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs
Yann Hicke, Anmol Agarwal, Qianou Ma, and Paul Denny. 2023 · 2023
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Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech. In Companion Proceedings of the ACM Web Conference 2023 . ACM
Fan Huang, Haewoon Kwak, and Jisun An. 2023 · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, et al · 2023
Cited alongside, same era.
Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. In Proc. of the 2023 CHI Conf. on Human Factors in Computing Systems . ACM, 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.
Exploring the Potential of Large Language Models to Generate Formative Programming Feedback
Natalie Kiesler, Dominic Lohr, and Hieke Keuning. 2023 · 2023
Cited alongside, same era.
Can foundation models label data like humans?
Nazneen Rajani, Nathan Lambert, Sheon Han, Jean Wang, Osvald Nitski, et al · 2023
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GPT-3-Powered Type Error Debugging: Investigating the Use of Large Language Models for Code Repair. In Proc. of the 16th ACM SIGPLAN Int. Conf. on Software Language Engineering . ACM, 111–124
Francisco Ribeiro, José Nuno Castro de Macedo, Kanae Tsushima, Rui Abreu, and João Saraiva. 2023 · 2023
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Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, et al · 2023
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Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses. In Proc. of the 2023 ACM Conf. on Int. Computing Education Research - Volume 1 . ACM, 78–92
Jaromir Savelka, Arav Agarwal, Marshall An, Chris Bogart, and Majd Sakr. 2023a · 2023
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Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, et al · 2023
Cited alongside, same era.
Training Language Models for Programming Feedback Using Automated Repair Tools. In Artificial Intelligence in Education . Springer Nature Switzerland, 830–835
Charles Koutcheme. 2023 · 2023
Cited alongside, same era.
Automated Program Repair Using Generative Models for Code Infilling. In Artificial Intelligence in Education . Springer Nature Switzerland, 798–803
Charles Koutcheme, Sami Sarsa, Juho Leinonen, Arto Hellas, and Paul Denny. 2023 · 2023
Cited alongside, same era.
Using Large Language Models to Enhance Programming Error Messages. In Proc. of the 54th ACM Technical Symposium on Computer Science Education V. 1 . ACM, New York, NY, USA, 563–569
Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, et al · 2023
Cited alongside, same era.
Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods. In Responsive and Sustainable Educational Futures . Springer Nature Switzerland, 229–245
Steven Moore, Huy A. Nguyen, Tianying Chen, and John Stamper. 2023 · 2023
Cited alongside, same era.
Large Language Models (GPT) for automating feedback on programming assignments
Maciej Pankiewicz and Ryan S. Baker. 2023 · 2023
Cited alongside, same era.
Examining Zero-Shot Vulnerability Repair with Large Language Models. In 2023 IEEE Symposium on Security and Privacy . 2339–2356
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt. 2023 · 2023
Cited alongside, same era.
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
Tung Phung, Victor-Alexandru Pădurean, José Cambronero, Sumit Gulwani, Tobias Kohn, et al · 2023
Cited alongside, same era.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, et al · 2023
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Zephyr: Direct Distillation of LM Alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, et al · 2023
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PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, et al · 2023
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Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review
Lixiang Yan, Lele Sha, Linxuan Zhao, Yuheng Li, Roberto Martinez-Maldonado, et al · 2023
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Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, et al · 2023
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LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, et al · 2023
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JudgeLM: Fine-tuned Large Language Models are Scalable Judges
Lianghui Zhu, Xinggang Wang, and Xinlong Wang. 2023 · 2023
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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
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CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. In Proc. of the 23rd Koli Calling Int. Conf. on Computing Education Research . ACM, New York, NY, USA, Article 8, 11 pages
Mark Liffiton, Brad E Sheese, Jaromir Savelka, and Paul Denny. 2024 · 2024
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Hunter McNichols, Wanyong Feng, Jaewook Lee, Alexander Scarlatos, Digory Smith, et al · 2024
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