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Direct linkPasty 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
Tian Luo; Pauline S. Muljana; Xinyue Ren; Dara Young – Educational Technology Research and Development, 2025
The emergence of generative artificial intelligence (GenAI) has caused significant disruptions on a global scale in various workplace settings, including the field of instructional design (ID). Given the paucity of research investigating the impact of GenAI on ID work, we conducted a mixed methods study to understand instructional designers (IDs)'…
Descriptors: Artificial Intelligence, Familiarity, Instructional Design, Brainstorming
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
Özsen, Tolga; Saka, Irem; Çelik, Özgür; Razi, Salim; Akkan, Senem Çente; Dlabolova, Dita Henek – Education and Information Technologies, 2023
Plagiarism has been among the top forms of academic misconduct. Detective, reactive and proactive measures are taken to mitigate plagiarism in scholarly works. Text-matching tools play a significant role in the detection of plagiarism. Many studies have tested the performance of text-matching tools in detecting plagiarism from various…
Descriptors: Plagiarism, Japanese, Academic Language, Writing (Composition)
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
James W. Drisko – Journal of Teaching in Social Work, 2025
The rise of AI generated texts offers promise but creates new challenges for social work teaching. A recent survey found that 89% of higher education students used AI on their homework. AI generated text may be difficult to distinguish from a student's own work, yet are being submitted as the student's own work. This poses new challenges to…
Descriptors: Plagiarism, Social Work, Counselor Training, Artificial Intelligence
Leen Adel Gammoh – Education and Information Technologies, 2025
This qualitative study examines the risks educators in Jordan face with the integration of ChatGPT, an emerging AI technology, into academic settings. While considerable attention has been given to risks affecting university students, there remains a gap in understanding the specific challenges encountered by educators themselves. Through…
Descriptors: Foreign Countries, Artificial Intelligence, Educational Technology, Technology Integration
Download full textSiraprapa 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
Luke Parker; Chris Carter; Alice L. Karakas; Jane A. Loper; Ahmad Sokkar – AERA Online Paper Repository, 2024
In 2023, ChatGPT emerged as a transformative force in education, igniting widespread interest across academia. This paper rigorously investigates ChatGPT's impact on higher education using a mixed-methods approach, comparing its (ChatGPT) performance with real students' work in undergraduate assignments. Key findings reveal ChatGPT consistently…
Descriptors: Artificial Intelligence, Technology Education, Higher Education, Influence of Technology
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
Mads Paludan Goddiksen; Mikkel Willum Johansen; Anna Catharina Vieira Armond; Mateja Centa; Christine Clavien; Eugenijus Gefenas; Nóra Kovács; Marcus Tang Merit; I. Anna S. Olsson; Margarita Poškute; Júlio Borlido Santos; Rita Santos; Vojko Strahovnik; Orsolya Varga; P. J. Wall; Peter Sandøe; Thomas Bøker Lund – International Journal for Educational Integrity, 2024
Text-matching software (TMS) is a standard part of efforts to prevent and detect plagiarism in upper secondary and higher education. While there are many studies on the potential benefits of using this technology, few studies look into potential unintended side effects. These side effects include students worrying about being accused of plagiarism…
Descriptors: Computer Software, Secondary School Students, Bachelors Degrees, Undergraduate Students
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
Perkins, Mike – Journal of University Teaching and Learning Practice, 2023
This paper explores the academic integrity considerations of students' use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond,…
Descriptors: Artificial Intelligence, College Students, Higher Education, Technology Uses in Education
