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Randy Connolly – ACM Transactions on Computing Education, 2024
The belief that AI technology is on the cusp of causing a generalized social crisis became a popular one in 2023. While there was no doubt an element of hype and exaggeration to some of these accounts, they do reflect the fact that there are troubling ramifications to this technology stack. This conjunction of shared concerns about social,…
Descriptors: Artificial Intelligence, Computers, Technology Uses in Education, Public Opinion
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Ramon Mayor Martins; Christiane G. Von Wangenheim; Marcelo F. Rauber; Adriano F. Borgatto; Jean C. R. Hauck – ACM Transactions on Computing Education, 2024
As Machine Learning (ML) becomes increasingly integrated into our daily lives, it is essential to teach ML to young people from an early age including also students from a low socioeconomic status (SES) background. Yet, despite emerging initiatives for ML instruction in K-12, there is limited information available on the learning of students from…
Descriptors: Artificial Intelligence, Computer Science Education, Socioeconomic Status, Correlation
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Vesin, Boban; Mangaroska, Katerina; Akhuseyinoglu, Kamil; Giannakos, Michail – ACM Transactions on Computing Education, 2022
Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students' development and engagement with programming is a challenging process. Early career computer…
Descriptors: Adaptive Testing, Online Courses, Programming, Computer Science Education
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Abdulhadi Shoufan – ACM Transactions on Computing Education, 2023
With the immense interest in ChatGPT worldwide, education has seen a mix of both excitement and skepticism. To properly evaluate its impact on education, it is crucial to understand how far it can help students without prior knowledge answer assessment questions. This study aims to address this question as well as the impact of the question type.…
Descriptors: Prior Learning, Artificial Intelligence, Technology Uses in Education, Computer Assisted Testing
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Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
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Nabor C. Mendonça – ACM Transactions on Computing Education, 2024
The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI's most…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Foreign Countries
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Allen, Becky; McGough, Andrew Stephen; Devlin, Marie – ACM Transactions on Computing Education, 2022
Artificial Intelligence and its sub-disciplines are becoming increasingly relevant in numerous areas of academia as well as industry and can now be considered a core area of Computer Science. The Higher Education sector are offering more courses in Machine Learning and Artificial Intelligence than ever before. However, there is a lack of research…
Descriptors: Artificial Intelligence, Audiences, Computer Science Education, Higher Education
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Sulmont, Elisabeth; Patitsas, Elizabeth; Cooperstock, Jeremy R. – ACM Transactions on Computing Education, 2019
Given its societal impacts and applications to numerous fields, machine learning (ML) is an important topic to understand for many students outside of computer science and statistics. However, machine-learning education research is nascent, and research on this subject for non-majors thus far has only focused on curricula and courseware. We…
Descriptors: Man Machine Systems, Artificial Intelligence, Nonmajors, College Faculty
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Lagus, Jarkko; Longi, Krista; Klami, Arto; Hellas, Arto – ACM Transactions on Computing Education, 2018
The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in…
Descriptors: Computer Science Education, Transfer of Training, Programming, Educational Objectives
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Fiebrink, Rebecca – ACM Transactions on Computing Education, 2019
This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education,…
Descriptors: Artificial Intelligence, Man Machine Systems, Population Groups, Creativity
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Russell, Ingrid; Markov, Zdravko; Neller, Todd; Coleman, Susan – ACM Transactions on Computing Education, 2010
Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that…
Descriptors: Artificial Intelligence, Program Effectiveness, Computer Science, Teaching Methods