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Pasty Asamoah; John Serbe Marfo; Matilda Kokui Owusu-Bio; Daniel Zokpe – Education and Information Technologies, 2024
In this brief we shift the current academic integrity conversation from "detecting and preventing plagiarism" to "examining how plagiarized contents can be corrected with an objective knowledge of the number of words to modify and properly acknowledged". We proposed a simple, yet useful and powerful mathematical model that is…
Descriptors: Error Correction, Plagiarism, Integrity, Prevention
Elkhatat, Ahmed M. – International Journal for Educational Integrity, 2023
Academic plagiarism is a pressing concern in educational institutions. With the emergence of artificial intelligence (AI) chatbots, like ChatGPT, potential risks related to cheating and plagiarism have increased. This study aims to investigate the authenticity capabilities of ChatGPT models 3.5 and 4 in generating novel, coherent, and accurate…
Descriptors: Artificial Intelligence, Plagiarism, Integrity, Models
Cheers, Hayden; Lin, Yuqing – Computer Science Education, 2023
Background and Context: Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, such tools do not identify plagiarism, nor suggest what assignment submissions are suspicious of plagiarism. Source code plagiarism…
Descriptors: Plagiarism, Programming, Computer Science Education, Identification
Siraprapa Kotmungkun; Wichuta Chompurach; Piriya Thaksanan – English Language Teaching Educational Journal, 2024
This study explores the writing quality of two AI chatbots, OpenAI ChatGPT and Google Gemini. The research assesses the quality of the generated texts based on five essay models using the T.E.R.A. software, focusing on ease of understanding, readability, and reading levels using the Flesch-Kincaid formula. Thirty essays were generated, 15 from…
Descriptors: Plagiarism, Artificial Intelligence, Computer Software, Essays
Drisko, James W. – Journal of Social Work Education, 2023
Plagiarism is a continuing and growing concern in higher education and in academic publishing. Educating to avoid plagiarism requires ongoing efforts at all levels and clear policies that explain the several types of plagiarism and potential consequences when it is found. Identifying plagiarism requires complex judgments and is not a simple matter…
Descriptors: Plagiarism, Computer Software, Identification, Computational Linguistics
Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2021
One of the main goals of assignments in the academic environment is to assess the students' knowledge and mastery of a specific topic, and it is crucial to ensure that the work is original and has been solely made by the students to assess their competence acquisition. Therefore, Text-Matching Software Products (TMSPs) are used by academic…
Descriptors: Plagiarism, Identification, Assignments, Computer Software
Cheers, Hayden; Lin, Yuqing; Yan, Weigen – Informatics in Education, 2023
Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work…
Descriptors: Plagiarism, Assignments, Computer Software, Computer Science Education
Kamzola, Laima; Anohina-Naumeca, Alla – Journal of Academic Ethics, 2020
There are many internationally developed text-matching software systems that help successfully identify potentially plagiarized content in English texts using both their internal databases and web resources. However, many other languages are not so widely spread but they are used daily to communicate, conduct research and acquire education. Each…
Descriptors: Indo European Languages, Computer Software, Plagiarism, Identification
Yovav Eshet – Education and Information Technologies, 2024
The COVID-19 pandemic has forced higher education institutions worldwide to shift from face-to-face (F2F) to emergency remote teaching (ERT), which has led to an increased concern about academic integrity. This study examines the relationship between learning environment and academic integrity via plagiarism detection software in different…
Descriptors: Plagiarism, Integrity, Ethics, Student Behavior
Ibrahim, Karim – Language Testing in Asia, 2023
The release of ChatGPT marked the beginning of a new era of AI-assisted plagiarism that disrupts traditional assessment practices in ESL composition. In the face of this challenge, educators are left with little guidance in controlling AI-assisted plagiarism, especially when conventional methods fail to detect AI-generated texts. One approach to…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Artificial Intelligence
Youngjin Lee – TechTrends: Linking Research and Practice to Improve Learning, 2024
This study investigates community sentiments and opinions regarding the use of ChatGPT in education within online user forums. Large Language Model (LLM)-based sentiment analysis was conducted on the posts and comments submitted to Teachers Subreddit, an online community for educators. The analysis revealed that the members of Teachers Subreddit…
Descriptors: Community Attitudes, Artificial Intelligence, Discourse Analysis, Computer Mediated Communication
Maertens, Rien; Van Petegem, Charlotte; Strijbol, Niko; Baeyens, Toon; Jacobs, Arne Carla; Dawyndt, Peter; Mesuere, Bart – Journal of Computer Assisted Learning, 2022
Background: Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use…
Descriptors: Plagiarism, Benchmarking, Coding, Computer Science Education
Ochieng, Dunlop – Journal of Learning and Teaching in Digital Age, 2019
Education sector has embraced and increasingly use digital technologies to address its longstanding challenges. However, considering that every technology has its limitations, I evaluated the progress of citation and reference technologies in solving the chronic problem of plagiarism in academic writing. In this pursuit, I confirmed with several…
Descriptors: Educational Technology, Citations (References), Plagiarism, Error Patterns
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
Thaweesak Chanpradit; Phakkaramai Samran; Siriprapa Saengpinit; Pailin Subkasin – Journal of English Teaching, 2024
AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Phrase Structure