Cheating and plagiarism have become serious problems in higher education institutions (HEIs). It affects educational quality as well as the reputation of higher education. The purpose of this study is to identify the most prevalent types of cheating and plagiarism, as well as the elements that contribute to cheating and plagiarism, and to present solutions to this recurring problem. This paper systematically reviews 45 articles published from 2018, to 2022, aligned with the PRISMA guidelines in the selection, filtering, and reporting of the papers. This review shows that factors such as increased pressure on students, poor academic integrity awareness, lack of up-to-date academic honor codes, and the unethical application of AI tools are prime contributing factors to cheating and plagiarism in HEIs. In a broader sense, all these factors are classified as individual, social, cultural, institutional, and technological factors that are responsible for this problem. This problem can be reduced by establishing ethical and moral development tutorials as well as formulating up-to-date honor codes considering AI tools. Furthermore, higher education institutions must develop anti-plagiarism detection software in order to detect plagiarism and aid students in improving academic writing and paraphrasing approaches. The findings of this systematic literature review provide useful insights for educators and policymakers to solve the complicated issue of cheating and plagiarism in higher education institutions.
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http://dx.doi.org/10.12688/f1000research.147140.2 | DOI Listing |
Pain Pract
January 2025
Department of Anesthesiology and Perioperative Medicine, Chronic Pain Division, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania, USA.
Objectives: Artificial intelligence (AI) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there is a paucity of literature analyzing the impact that AI may have on trainee education.
View Article and Find Full Text PDFStud Health Technol Inform
November 2024
Norwegian Directorate for Higher Education and Skills, Norway.
This study explores the proposition of requiring students to hand in universally designed coursework and the transferrable benefits of accessibility audits. Coursework that adheres to universal design (UD) principles will be more accessible to fellow students and teachers. In this study we investigate if the universal design perspective can have positive side effects as a vessel for plagiarism detection.
View Article and Find Full Text PDFHeliyon
November 2024
Department of Curriculum and Instruction, California State University, Fresno, Fresno, United States.
College athletes balance academic and athletic roles and, as a result, can hold different combinations of academic and athletic identities. The purpose of this study was to identify common identity profiles in a large sample of Division I (elite) college athletes in the U.S.
View Article and Find Full Text PDFF1000Res
October 2024
Universiti Tunku Abdul Rahman, Bandar Sungai Long, 43000 Kajang Petaling, Selangor, Malaysia.
Cheating and plagiarism have become serious problems in higher education institutions (HEIs). It affects educational quality as well as the reputation of higher education. The purpose of this study is to identify the most prevalent types of cheating and plagiarism, as well as the elements that contribute to cheating and plagiarism, and to present solutions to this recurring problem.
View Article and Find Full Text PDFGMS J Med Educ
October 2024
Institut für medizinische und pharmazeutische Prüfungsfragen (IMPP), Mainz, Germany.
The high performance of generative artificial intelligence (AI) and large language models (LLM) in examination contexts has triggered an intense debate about their applications, effects and risks. What legal aspects need to be considered when using LLM in teaching and assessment? What possibilities do language models offer? Statutes and laws are used to assess the use of LLM: - University statutes, state higher education laws, licensing regulations for doctors - Copyright Act (UrhG) - General Data Protection Regulation (DGPR) - AI Regulation (EU AI Act) LLM and AI offer opportunities but require clear university frameworks. These should define legitimate uses and areas where use is prohibited.
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