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Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving.
The LISP tutor
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The hint factory: Automatic generation of contextualized help for existing computer aided instruction. In Proceedings of the 9th International Conference on Intelligent Tutoring Systems Young Researchers Track . Springer, Montreal,Canada, 71–78
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iSnap: Towards Intelligent Tutoring in Novice Programming Environments. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education . ACM, Seattle Washington USA, 483–488
Thomas W. Price, Yihuan Dong, and Dragan Lipovac. 2017 · 2017
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Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
Kelly Rivers and Kenneth R. Koedinger. 2017 · 2017
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Exploring the Design Space of Automatically Synthesized Hints for Introductory Programming Assignments. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ’17) . Association for Computing Machinery, New York, NY, USA, 2951–2958
Ryo Suzuki, Gustavo Soares, Elena Glassman, Andrew Head, Loris D’Antoni, and Björn Hartmann. 2017 · 2017
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Intelligent tutoring systems for programming education: a systematic review. In Proceedings of the 20th Australasian Computing Education Conference (ACE ’18) . Association for Computing Machinery, New York, NY, USA, 53–62
Tyne Crow, Andrew Luxton-Reilly, and Burkhard Wuensche. 2018 · 2018
GPTutor: a ChatGPT-powered programming tool for code explanation. In International Conference on Artificial Intelligence in Education . Springer, Tokyo, Japan, 321–327
Eason Chen, Ray Huang, Han-Shin Chen, Yuen-Hsien Tseng, and Liang-Yi Li. 2023 · 2023
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Understanding the Effects of Using Parsons Problems to Scaffold Code Writing for Students with Varying CS Self-Efficacy Levels. In Proceedings of the 23rd Koli Calling International Conference on Computing Education Research . ACM, Koli, Finland, 1–12
Xinying Hou, Barbara Jane Ericson, and Xu Wang. 2023 · 2023
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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 (CHI ’23) . Association for Computing Machinery, New York, NY, USA, 1–23
Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J. Ericson, David Weintrop, and Tovi Grossman. 2023a · 2023
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QuickTA: Exploring the Design Space of Using Large Language Models to Provide Support to Students. In Learning Analytics and Knowledge Conference . Learning Analytics and Knowledge Conference 2023 (LAK’23), ACM, Arlington, Texas
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Cited alongside, same era.
A Systematic Literature Review of Automated Feedback Generation for Programming Exercises
Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. 2019 · 2019
Cited alongside, same era.
On Designing Programming Error Messages for Novices: Readability and its Constituent Factors. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21) . Association for Computing Machinery, New York, NY, USA, 1–15
Paul Denny, James Prather, Brett A. Becker, Catherine Mooney, John Homer, Zachary C Albrecht, and Garrett B. Powell. 2021 · 2021
Cited alongside, same era.
Investigating Best Practices in the Design of Automated Hints and Formative Feedback to Improve Students’ Cognitive and Affective Outcomes - Samiha Marwan PhD Thesis - 2021
Samiha Marwan. 2021 · 2021
Cited alongside, same era.
Using Adaptive Parsons Problems to Scaffold Write-Code Problems. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 . ACM, Lugano and Virtual Event Switzerland, 15–26
Xinying Hou, Barbara Jane Ericson, and Xu Wang. 2022 · 2022
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Towards Generalized Methods for Automatic Question Generation in Educational Domains. In Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption (Lecture Notes in Computer Science) , Isabel Hilliger, Pedro J. Muñoz-Merino, Tinne De Laet, Alejandro Ortega-Arranz, and Tracie Farrell (Eds.). Springer International Publishing, Cham, 272–284
Huy A. Nguyen, Shravya Bhat, Steven Moore, Norman Bier, and John Stamper. 2022 · 2022
Cited alongside, same era.
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues. In Proceedings of the 15th International Conference on Educational Data Mining , Antonija Mitrovic and Nigel Bosch (Eds.). International Educational Data Mining Society, Durham, United Kingdom, 522–529
Anaïs Tack and Chris Piech. 2022 · 2022
Cited alongside, same era.
How novices use LLM-based code generators to solve CS1 coding tasks in a self-paced learning environment. In Proceedings of the 23rd Koli Calling International Conference on Computing Education Research . ACM, Koli, Finland, 1–12
Majeed Kazemitabaar, Xinying Hou, Austin Henley, Barbara Jane Ericson, David Weintrop, and Tovi Grossman. 2023b
Cited in the paper.
Harsh Kumar, Ilya Musabirov, Joseph Jay Williams, and Michael Liut. 2023 · 2023
Later among the works it cites.
CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes
Mark Liffiton, Brad Sheese, Jaromir Savelka, and Paul Denny. 2023 · 2023
Later among the works it cites.
“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23) . Association for Computing Machinery, New York, NY, USA, 1–31
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu, Benjamin Zorn, Jack Williams, Neil Toronto, and Andrew D. Gordon. 2023 · 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 . ACM, Toronto ON Canada, 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.
Improving the Coverage of GPT for Automated Feedback on High School Programming Assignments. In NeurIPS’23 Workshop Generative AI for Education (GAIED) . MIT Press, New Orleans, Louisiana, USA, 46
Shubham Sahai, Umair Z Ahmed, and Ben Leong. 2023 · 2023
Later among the works it cites.
Next-Step Hint Generation for Introductory Programming Using Large Language Models. In Proceedings of the 26th Australasian Computing Education Conference . ACM, Melbourne, Australia, 144–153
Lianne Roest, Hieke Keuning, and Johan Jeuring. 2024 · 2024
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