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Levin, Nathan; Baker, Ryan S.; Nasiar, Nidhi; Fancsali, Stephen; Hutt, Stephen – International Educational Data Mining Society, 2022
Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Cheating
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Gülü, Mehmet; Yapici, Hakan – International Journal of Curriculum and Instruction, 2022
The aim of this research was to explore the attitudes of elite athletes towards performance enhancement through banned substances. In this study, survey model, which is one of the quantitative research methods, was used. Purposive sampling method was used in the data collection process. Participants consisted of a sample group of elite athletes (n…
Descriptors: Athletes, Attitudes, Drug Use, Cheating
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Stoesz, Brenda M.; Eaton, Sarah Elaine – Educational Policy, 2022
We examined 45 academic integrity policy documents from 24 publicly-funded universities in Canada's four western provinces using a qualitative research design. We extracted data related to 5 core elements of exemplary academic integrity policy (i.e., access, detail, responsibility, approach, support). Most documents pointed to punitive approaches…
Descriptors: Integrity, Ethics, Educational Policy, Cheating
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Hung Manh Nguyen; Daisaku Goto – Education and Information Technologies, 2024
The proliferation of artificial intelligence (AI) technology has brought both innovative opportunities and unprecedented challenges to the education sector. Although AI makes education more accessible and efficient, the intentional misuse of AI chatbots in facilitating academic cheating has become a growing concern. By using the indirect…
Descriptors: Academic Achievement, Cheating, Student Behavior, Artificial Intelligence
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Sarah Moore; Kathryn Lookadoo – Business and Professional Communication Quarterly, 2024
This article presents the ongoing conversation about generative AI guidance and policy in higher education. The article examines syllabus policies, including analyzing sentiment, emotion, and common themes in GenAI policies. Findings show that policies should be audience-focused, clearly written, and grounded in strategies to promote ethical AI…
Descriptors: Artificial Intelligence, Educational Policy, Course Descriptions, Audience Awareness
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Juuso Henrik Nieminen; Sarah Elaine Eaton – Assessment & Evaluation in Higher Education, 2024
Assessment accommodations are used globally in higher education systems to ensure that students with disabilities can participate fairly in assessment. Even though assessment accommodations are supposed to promote access, not success, they are commonly portrayed as potentially being "cheating" in that they provide certain students with…
Descriptors: Foreign Countries, Higher Education, Testing Accommodations, Academic Accommodations (Disabilities)
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Richard Fendler; David Beard; Jonathan Godbey – Electronic Journal of e-Learning, 2024
The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning,…
Descriptors: Cheating, Computer Assisted Testing, Supervision, Student Behavior
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Ibrahim Adeshola; Adeola Praise Adepoju – Interactive Learning Environments, 2024
The launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the…
Descriptors: Opportunities, Barriers, Artificial Intelligence, Natural Language Processing
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Eunjeong Park – Language Teaching Research Quarterly, 2024
It is fundamental for language teachers to assess their students' performance. Therefore, they should be familiar with various forms of assessments because teaching and assessing languages are closely related and have a great deal to do with one another. This study examined EFL preservice teachers' perceptions of assessment literacy at a…
Descriptors: Preservice Teachers, Student Attitudes, Assessment Literacy, Preservice Teacher Education
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Tobias Kohn – Journal of Computer Assisted Learning, 2025
Background: The recent advent of powerful, exam-passing large language models (LLMs) in public awareness has led to concerns over students cheating, but has also given rise to calls for including or even focusing education on LLMs. There is a perceived urgency to react immediately, as well as claims that AI-based reforms of education will lead to…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Usability
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Cinzia Zanetti; Fabrizio Butera – Educational Psychology, 2025
Collective cheating is a widespread phenomenon in school and academia. A large majority of students report having cheated--individually or collectively--at school. In many settings, collective cheating is part of a culture and reveals a descriptive norm. However, no measure exists, to our knowledge, that captures the presence of a collective…
Descriptors: Cheating, Rating Scales, School Culture, Educational Environment
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Lydia Arnold; James Croxford – Teaching in Higher Education, 2025
Authentic assessment is a widely discussed concept in higher education, but it has a problem: the concept has become so all-encompassing that its meaning is now unclear. The notion has been expanded and diluted. For example, adding social justice to the definition or positioning exams as authentic, adds to the contradictions inherent within in the…
Descriptors: Performance Based Assessment, Higher Education, Vocabulary, Evaluation Methods
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Emily J. Ryan; Lori Sherlock; Edward Ryan; Miriam Leary – Advances in Physiology Education, 2025
Academic dishonesty is becoming more common among university students in science, technology, engineering, and mathematics (STEM)-based programs. This is concerning because these students hold positions of responsibility in their professional careers. The purpose of this qualitative study was to examine if a student's academic standing and/or…
Descriptors: College Freshmen, Ethics, Integrity, Cheating
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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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Manit Malhotra; Indu Chhabra; Salil Bharany; Ateeq Ur Rehman; Seada Hussen – Discover Education, 2025
The field of online education has increasingly recognized digitally automated proctoring as a prominent and pressing issue in recent years. The increasing prevalence of online examination deception has prompted a heated debate. The purpose of the study is to capture advancements in this research field, offering a concise overview of current…
Descriptors: Electronic Learning, Online Courses, Supervision, Integrity
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