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Zifeng Liu; Wanli Xing; Xinyue Jiao; Chenglu Li; Wangda Zhu – Education and Information Technologies, 2025
The ability of large language models (LLMs) to generate code has raised concerns in computer science education, as students may use tools like ChatGPT for programming assignments. While much research has focused on higher education, especially for languages like Java and Python, little attention has been given to K-12 settings, particularly for…
Descriptors: High School Students, Coding, Artificial Intelligence, Electronic Learning
Oscar Karnalim; Hapnes Toba; Meliana Christianti Johan – Education and Information Technologies, 2024
Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated…
Descriptors: Artificial Intelligence, Academic Achievement, Integrity, Introductory Courses
Oscar Karnalim – Informatics in Education, 2024
Programming students need to be informed about plagiarism and collusion. Hence, we developed an assessment submission system to remind students about the matter. Each submission will be compared to others and any similarities that do not seem a result of coincidence will be reported along with their possible reasons. The system also employs…
Descriptors: Programming, Integrity, Academic Achievement, Plagiarism
Lakshminarayanan, Srinivasan; Rao, N. J. – Higher Education for the Future, 2022
There are many grey areas in the interpretation of academic integrity in the course on Introduction to Programming, commonly known as CS1. Copying, for example, is a method of learning, a method of cheating and a reuse method in professional practice. Many institutions in India publish the code in the lab course manual. The students are expected…
Descriptors: Integrity, Cheating, Duplication, Introductory Courses
M. V. Lubarda; A. M. Phan; C. Schurgers; N. Delson; M. Ghazinejad; S. Baghdadchi; M. Minnes; M. Kim; C. Pilegard; J. Relaford-Doyle; C. L. Sandoval; H. Qi – Computer Science Education, 2025
Background and context: Pair programming and oral exams were deployed in tandem in a remote undergraduate computer programming course to promote social interaction and enhance learning. Objectives: We investigate their impact on social interactions, sense of connection, academic performance, and academic integrity within a virtual learning…
Descriptors: Distance Education, Undergraduate Students, Integrity, Computer Science Education

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