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Showing 1 to 15 of 75 results Save | Export
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H. Murch; M. Worley; F. Volk – Journal of Academic Ethics, 2025
Academic misconduct is a prevalent issue in higher education with detrimental effects on the individual students, rigor of the program, and strength of the workplace. Recent advances in artificial intelligence (AI) have reinvigorated concern over academic integrity and the potential use and misuse of AI. However, there is a lack of research on…
Descriptors: Incidence, Artificial Intelligence, Technology Uses in Education, Plagiarism
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Muhammad Bilal Saqib; Saba Zia – Journal of Applied Research in Higher Education, 2025
Purpose: The notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Evaluation
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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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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
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Edmund Pickering; Clancy Schuller – Journal of Academic Ethics, 2025
Online tools are increasingly being used by students to cheat. File-sharing and homework-helper websites offer to aid students in their studies, but are vulnerable to misuse, and are increasingly reported as a major source of academic misconduct. Chegg.com is the largest such website. Despite this, there is little public information about the use…
Descriptors: Foreign Countries, Higher Education, Engineering Education, College Students
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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
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Marilyn U. Balagtas; Aurora B. Fulgencio; Joyce L. Bautista; Alvin B. Barcelona; Shiela Marie P. Jandusay; Ma. Danielle Renee Lim – Journal of Educators Online, 2025
The convenience and flexibility of online assessments can be beneficial in a variety of ways, but they can also pose risks and challenges, such as potential academic dishonesty by students. This study included 73 master's and doctoral students and investigated the relationship among their attitudes, experiences, and performance in an online…
Descriptors: Graduate Students, Student Attitudes, Student Experience, Academic Achievement
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Maame Afua Nkrumah; Ronald Osei Mensah; Alwyna Sackey Addaquay – Discover Education, 2025
The study sought to examine the gender, faculty and school-based disparity that exists in students' research self-efficacy, perception of ethics in research and the level of stress they face in conducting research. A sample of 385 undergraduate students from the faculty of business and hospitality were selected from three public universities in…
Descriptors: Gender Differences, Student Attitudes, Student Research, Self Efficacy
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Novak, Matija; Joy, Mike; Kermek, Dragutin – ACM Transactions on Computing Education, 2019
Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers.…
Descriptors: Plagiarism, Computer Software, Computer Software Evaluation, College Students
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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
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Autumn B. Hostetter; Natalie Call; Grace Frazier; Tristan James; Cassandra Linnertz; Elizabeth Nestle; Miaflora Tucci – Teaching of Psychology, 2025
Background: Psychology instructors frequently assign writing-to-learn exercises that include personal reflection. Generative Artificial Intelligence (GenAI) can write text that passes for humans in other domains. Objective: Do students and faculty rate a reflection written by GenAI differently than reflections written by students? Do students and…
Descriptors: Student Attitudes, Teacher Attitudes, College Faculty, Undergraduate Students
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Matthew Quesnel; Brenda M. Stoesz – Australasian Journal of Educational Technology, 2025
Contract cheating is a significant concern in higher education, requiring a multifaceted teaching and learning approach to address it. Quizzing students about their writing to promote engagement, confirm authorship and detect cheating has not yet been investigated systematically. Therefore, in this study, our objective was to explore the validity…
Descriptors: Cheating, Authors, Computer Uses in Education, Validity
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Jeong, Allan; Kim, Hae Young – Knowledge Management & E-Learning, 2022
Research shows that using computer-aided mapping tools improves critical thinking skills, but prior research provides limited evidence to show how the use of specific critical thinking skills increases map quality. This qualitative study observed 4 experts and 5 novices use a computer-aided mapping tool to construct argument maps. The analysis of…
Descriptors: Identification, Critical Thinking, Thinking Skills, Expertise
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Iouri Kotorov; Yuliya Krasylnykova; Mar Pérez-Sanagustín; Fernanda Mansilla; Julien Broisin – Journal of Learning Analytics, 2024
The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this…
Descriptors: Decision Making, Case Studies, Instructional Innovation, Teaching Methods
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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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