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How can AI enhance creative coding experiences for families? This study explores the potential of large language models (LLMs) in helping families with creative coding using Scratch.
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Child speech recognition in human-robot interaction: evaluations and recommendations. In Proceedings of the 2017 ACM/IEEE international conference on human-robot interaction . 82–90
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OurKidsCode: Facilitating Families to Be Creative with Computing. In Proceedings of the 11th International Conference on Computer Supported Education . SCITEPRESS - Science and Technology Publications
Nina Bresnihan, Glenn Strong, Lorraine Fisher, Richard Millwood, and Áine Lynch. 2019 · 2019
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Intercultural Computing Education: Toward Justice Across Difference
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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 . 27–43
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen. 2022 · 2022
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Insights from Virtual Culturally Responsive Computing Camps. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2 . ACM
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts . 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 2022
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Problem solved, but how? An exploratory study into students’ problem solving processes in creative coding tasks
Karen Woo and Garry Falloon. 2022 · 2022
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Tomi Slotte Dufva. 2021 · 2021
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Contextualizing AI Education for K-12 Students to Enhance Their Learning of AI Literacy Through Culturally Responsive Approaches
Amy Eguchi, Hiroyuki Okada, and Yumiko Muto. 2021 · 2021
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Teaching in an open village: a case study on culturally responsive computing in compulsory education
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StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics
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Masked Language Models as Stereotype Detectors?
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Is GitHub copilot a substitute for human pair-programming? An empirical study. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings . 319–321
Saki Imai. 2022 · 2022
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Exploring the Learnability of Program Synthesizers by Novice Programmers. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology . 1–15
Dhanya Jayagopal, Justin Lubin, and Sarah E Chasins. 2022 · 2022
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WhyGen: explaining ML-powered code generation by referring to training examples. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings . 237–241
Weixiang Yan and Yuanchun Li. 2022 · 2022
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StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement. In CHI Conference on Human Factors in Computing Systems . ACM
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Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Stefania Druga and Amy J. Ko. 2023 · 2023
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Co-Writing with Opinionated Language Models Affects Users’ Views. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–15
Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naaman. 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 . ACM
Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J. Ericson, David Weintrop, and Tovi Grossman. 2023 · 2023
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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
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Learning performance-improving code edits
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A Study of Editor Features in a Creative Coding Classroom. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–15
Andrew M Mcnutt, Anton Outkine, and Ravi Chugh. 2023 · 2023
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GitHub Next | Hey, GitHub!
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