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Meljun Barnayha; Gamaliel Gonzales; Rachel Lavador; Jessamae Martel; Ma. Kathleen Urot; Roselyn Gonzales – Psychology in the Schools, 2025
This study examines the determinants of online academic dishonesty using the theory of planned behavior. We surveyed 1087 college students in Central Philippines and utilized a partial least squares-structural equation modeling analysis to evaluate a proposed model. Results demonstrate that 10 of the 11 hypothesized relationships are statistically…
Descriptors: Self Control, Cheating, Intervention, Ethics
Jonas Flodén – British Educational Research Journal, 2025
This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…
Descriptors: College Faculty, Artificial Intelligence, Comparative Testing, Scoring
Ilhama Mammadova; Fatime Ismayilli; Elnaz Aliyeva; Narmin Mammadova – Educational Process: International Journal, 2025
Background/purpose: Artificial Intelligence (AI) is increasingly shaping assessment practices in higher education, promising faster feedback and reduced instructor workload while also raising concerns about fairness and transparency. This study examines how AI technologies are transforming assessment processes and the experiences of stakeholders.…
Descriptors: Artificial Intelligence, Student Evaluation, Technology Uses in Education, Undergraduate Students
Daniel Lupiya Mpolomoka – Pedagogical Research, 2025
Overview: This systematic review explores the utilization of artificial intelligence (AI) for assessment, grading, and feedback in higher education. The review aims to establish how AI technologies enhance efficiency, scalability, and personalized learning experiences in educational settings, while addressing associated challenges that arise due…
Descriptors: Artificial Intelligence, Higher Education, Evaluation Methods, Literature Reviews
Rebecka Weegar; Peter Idestam-Almquist – International Journal of Artificial Intelligence in Education, 2024
Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers to spend more time on other tasks. However, when it comes to grading exams, fully eliminating manual work is not yet possible even with very accurate automated grading, as any grading mistakes could have significant consequences for…
Descriptors: Grading, Computer Assisted Testing, Introductory Courses, Computer Science Education
Liandi van den Berg – International Journal for Educational Integrity, 2025
Due to the coronavirus disease 2019 (COVID-2019) and the sudden shift to online learning, higher education institutions adopted various approaches to reduce cheating in online assessments, mainly involving online live proctoring (OLP). The international assessment integrity regulation trend also applied to a university in South Africa, where…
Descriptors: Foreign Countries, College Faculty, College Students, Computer Assisted Testing
Sundas Azeem; Muhammad Abbas – Education and Information Technologies, 2025
The study examined the association of big five personality traits (i.e., conscientiousness, openness to experience, and neuroticism) with use of Generative Artificial Intelligence (GenAI) among university students. It also examined the moderating role of perceived fairness in grading on the relationships of personality traits with GenAI usage.…
Descriptors: Personality Traits, Artificial Intelligence, Technology Uses in Education, Technology Integration
Oeding, Jill M. – Quarterly Review of Distance Education, 2022
One of the primary findings from this study is the importance of watching the exam proctoring videos for online, remotely proctored exams. Proctors do not need to be experts in academic dishonesty to detect the misconduct. The key to detecting academic dishonesty is to closely monitor the examinee's eyes, know the eyes' position when the examinee…
Descriptors: Prevention, Identification, Cheating, Ethics
Pisut Pongchaikul; Pornpun Vivithanaporn; Nanthicha Somboon; Jitpisuth Tantasiri; Thanyarat Suwanlikit; Amornrat Sukkul; Taddaw Banyen; Athinan Prommahom; Samart Pakakasama; Artit Ungkanont – Journal of Academic Ethics, 2025
The COVID-19 pandemic significantly impacted medical education, causing a shift towards online learning. However, this transition posed challenges in administering online assessments, particularly in proctoring and detecting academic misconduct. This study aimed to investigate the prevalence of academic misconduct among medical students during…
Descriptors: COVID-19, Pandemics, Medical Education, Online Courses
Necati Taskin; Mehmet Kokoç – Education and Information Technologies, 2025
This study examines the growing issue of academic dishonesty in online assessments, a challenge intensified by the expansion of digital learning platforms. Specifically, it investigates the relationship between students' online engagement and their performance in online versus traditional paper-and-pencil tests. Employing a cross-sectional…
Descriptors: Cheating, Ethics, Computer Assisted Testing, Technology Uses in Education
Monahan, Michael; Shah, Amit – Research in Higher Education Journal, 2023
Academic dishonesty is a major issue in education. Perhaps more so in the online environment where may times students are on their honor to complete exams without the use of the Internet, notes, or other prohibited materials. The age range of 18-24 encompasses the traditional aged student body. The non-traditional students are over the age of 25…
Descriptors: Ethics, Cheating, College Students, Nontraditional Students
Elahe Asadpour; Hooshang Yazdani – Journal of Educators Online, 2025
The advent of online learning in contemporary educational landscapes has established online examinations as key evaluative instruments. Despite their growing use, there remains a lack of scholarly inquiry into online examinations, academic integrity, and preventative measures against dishonesty. This research aims to explore the perceptions of 153…
Descriptors: Cheating, Computer Assisted Testing, Majors (Students), Grade Point Average
Rasha Mohamed Abdelrahman; Najeh Rajeh Alsalhi; Ahmad Mohammad Alzoubi; Abderrahim Benlahcene; Marei Ahmed; Abdalla Falah El-Mneizel – Educational Process: International Journal, 2025
Background/purpose: This study aims to better understand undergraduate students' perceptions of cheating in online learning programs at Ajman University, one of the higher education institutions in the United Arab Emirates. Materials/methods: The study used a descriptive method, employing a questionnaire instrument to collect data from faculty…
Descriptors: Foreign Countries, Undergraduate Students, Student Attitudes, Cheating
Elize du Plessis; Rebecca Y. Bayeck – Journal of Teaching and Learning, 2025
Even while Artificial Intelligence (AI) has long been a part of our lives, it has recently received more attention thanks to the introduction of ChatGPT, a Chat Generative Pre-Trained Transformer, since its launch in November 2022. The focus of this study is to investigate the potential of ChatGPT to assess student-teacher learning, which looks at…
Descriptors: Foreign Countries, Artificial Intelligence, Technology Uses in Education, Computer Assisted Testing
Esra Pinar Uça Günes; Nuray Gedik; Mehmet Ali Isikoglu; Baris Yigit; Ihsan Günes; Ayfer Beylik – Open Praxis, 2024
The primary objective of this manuscript is to examine the online assessment and exam security procedures during the pandemic, with a particular focus on higher education. In this context, the study investigates the measures employed by instructors, the challenges they encountered, and the strategies they employed to overcome these challenges in a…
Descriptors: Tests, Distance Education, Supervision, COVID-19

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