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Showing 1 to 15 of 22 results Save | Export
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Mike Perkins; Jasper Roe; Darius Postma; James McGaughran; Don Hickerson – Journal of Academic Ethics, 2024
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI's ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors…
Descriptors: Artificial Intelligence, Student Evaluation, Identification, Natural Language Processing
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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
Xuandong Zhao – ProQuest LLC, 2024
The rapid advancement of powerful Large Language Models (LLMs), such as ChatGPT and Llama, has revolutionized the world by bringing new creative possibilities and enhancing productivity. However, these advancements also pose significant challenges and risks, including the potential for misuse in the form of fake news, academic dishonesty,…
Descriptors: Computational Linguistics, Intellectual Property, Artificial Intelligence, Productivity
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Dawson, Phillip; Sutherland-Smith, Wendy; Ricksen, Mark – Assessment & Evaluation in Higher Education, 2020
Contract cheating happens when students outsource their assessed work to a third party. One approach that has been suggested for improving contract cheating detection is comparing students' assignment submissions with their previous work, the rationale being that changes in style may indicate a piece of work has been written by somebody else. This…
Descriptors: Cheating, Identification, Accuracy, Computer Software
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Emery-Wetherell, Meaghan; Wang, Ruoyao – Assessment & Evaluation in Higher Education, 2023
Over four semesters of a large introductory statistics course the authors found students were engaging in contract cheating on Chegg.com during multiple choice examinations. In this paper we describe our methodology for identifying, addressing and eventually eliminating cheating. We successfully identified 23 out of 25 students using a combination…
Descriptors: Computer Assisted Testing, Multiple Choice Tests, Cheating, Identification
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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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Jaramillo-Morillo, Daniel; Ruipérez-Valiente, José A.; Burbano Astaiza, Claudia Patricia; Solarte, Mario; Ramirez-Gonzalez, Gustavo; Alexandron, Giora – Journal of Computer Assisted Learning, 2022
Background: Small private online courses (SPOCs) are one of the strategies to introduce the massive open online courses (MOOCs) within the university environment and to have these courses validates for academic credit. However, numerous researchers have highlighted that academic dishonesty is greatly facilitated by the online context in which…
Descriptors: Learning Analytics, Cheating, Integrated Learning Systems, Intervention
Editorial Projects in Education, 2024
Addressing academic integrity in the age of AI is essential to ensure honesty and student success. This Spotlight will help you learn about how educators nationwide are approaching AI in teaching and learning; review data investigating how many students are actually using AI to cheat; examine strategies teachers are using to fight AI cheating;…
Descriptors: Integrity, Artificial Intelligence, Teaching Methods, Computer Software
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Jia, Jiyou; He, Yunfan – Interactive Technology and Smart Education, 2022
Purpose: The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic…
Descriptors: Artificial Intelligence, Supervision, Computer Assisted Testing, Electronic Learning
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C. Christine Fair – Journal of College and Character, 2023
The demand for online invigilation programs had dramatically increased due to the expansion of online learning; however, demand was further galvanized by the COVID-19 pandemic. Unfortunately, there are many technical and ethical problems with these programs that cannot be easily mitigated. Notably, they are beset by several inherent racial,…
Descriptors: Evidence, Cheating, COVID-19, Pandemics
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Abd-Elaal, El-Sayed; Gamage, Sithara H. P. W.; Mills, Julie E. – European Journal of Engineering Education, 2022
Authentic writing is an important aspect in education and research. Unfortunately, academic misconduct occurs among students and researchers. Consequently, written articles undergo certain detection measures and most teaching and research institutions use a range of software to detect plagiarism. However, state-of-the-art Automatic Article…
Descriptors: College Faculty, Identification, Computational Linguistics, Computer Software
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Ison, David C. – Online Learning, 2020
"Contract cheating," instances in which a student enlists someone other than themselves to produce coursework, has been identified as a growing problem within academic integrity literature and in news headlines. The percentage of students who have used this type of cheating has been reported to range between 6% and 15.7%. Generational…
Descriptors: Cheating, Contracts, Language Styles, Computational Linguistics
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Vista, Alvin – European Journal of Educational Research, 2019
Cheating detection is an important issue in standardized testing, especially in large-scale settings. Statistical approaches are often computationally intensive and require specialised software to conduct. We present a two-stage approach that quickly filters suspected groups using statistical testing on an IRT-based answer-copying index. We also…
Descriptors: Cheating, Identification, Computer Software, Standardized Tests
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Jeske, Heimo J.; Lall, Manoj; Kogeda, Okuthe P. – Journal of Information Technology Education: Innovations in Practice, 2018
Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of…
Descriptors: Plagiarism, Identification, Computer Software, Computer Assisted Testing
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Rogerson, Ann M.; McCarthy, Grace – International Journal for Educational Integrity, 2017
A casual comment by a student alerted the authors to the existence and prevalence of Internet-based paraphrasing tools. A subsequent quick Google search highlighted the broad range and availability of online paraphrasing tools which offer free 'services' to paraphrase large sections of text ranging from sentences, paragraphs, whole articles, book…
Descriptors: Plagiarism, Computer Software, Incidence, Cheating
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