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Pasty Asamoah; John Serbe Marfo; Matilda Kokui Owusu-Bio; Daniel Zokpe – Education and Information Technologies, 2024
In this brief we shift the current academic integrity conversation from "detecting and preventing plagiarism" to "examining how plagiarized contents can be corrected with an objective knowledge of the number of words to modify and properly acknowledged". We proposed a simple, yet useful and powerful mathematical model that is…
Descriptors: Error Correction, Plagiarism, Integrity, Prevention
R. Harrad; R. Keasley; L. Jefferies – Higher Education Research and Development, 2024
Academic misconduct and academic integrity are issues of importance to Higher Education Institutions (HEIs). Phraseologies and practices may conflate unintentional mistakes with attempts to gain illegitimate advantage, with some groups potentially at higher risk. HEIs across the United Kingdom (UK) responded to a Freedom of Information Act (FOI)…
Descriptors: Integrity, Cheating, College Students, Student Characteristics
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
Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2023
The proliferation of artificial intelligence (AI)-generated content, particularly from models like ChatGPT, presents potential challenges to academic integrity and raises concerns about plagiarism. This study investigates the capabilities of various AI content detection tools in discerning human and AI-authored content. Fifteen paragraphs each…
Descriptors: Artificial Intelligence, Integrity, Plagiarism, Educational Technology
Alexander K. Kofinas; Crystal Han-Huei Tsay; David Pike – British Journal of Educational Technology, 2025
Generative AI (hereinafter GenAI) technology, such as ChatGPT, is already influencing the higher education sector. In this work, we focused on the impact of GenAI on the academic integrity of assessments within higher education institutions, as GenAI can be used to circumvent assessment approaches within the sector, compromising their quality. The…
Descriptors: Artificial Intelligence, Technology Uses in Education, Integrity, Performance Based Assessment
Zifeng Liu; Wanli Xing; Xinyue Jiao; Chenglu Li; Wangda Zhu – Education and Information Technologies, 2025
The ability of large language models (LLMs) to generate code has raised concerns in computer science education, as students may use tools like ChatGPT for programming assignments. While much research has focused on higher education, especially for languages like Java and Python, little attention has been given to K-12 settings, particularly for…
Descriptors: High School Students, Coding, Artificial Intelligence, Electronic Learning
Evaluation of AI Content Generation Tools for Verification of Academic Integrity in Higher Education
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
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
Oscar Karnalim; Hapnes Toba; Meliana Christianti Johan – Education and Information Technologies, 2024
Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated…
Descriptors: Artificial Intelligence, Academic Achievement, Integrity, Introductory Courses
Weibel, Stephanie; Popp, Maria; Reis, Stefanie; Skoetz, Nicole; Garner, Paul; Sydenham, Emma – Research Synthesis Methods, 2023
Evidence synthesis findings depend on the assumption that the included studies follow good clinical practice and results are not fabricated or false. Studies which are problematic due to scientific misconduct, poor research practice, or honest error may distort evidence synthesis findings. Authors of evidence synthesis need transparent mechanisms…
Descriptors: Identification, Randomized Controlled Trials, Integrity, Evaluation Methods
Tyler Stillman – Journal of Educators Online, 2025
The current work introduces the concept of AI-trap questions as a tool for maintaining academic integrity in online courses. AI-trap questions are assessment tools designed to detect cheating by exploiting generative AI's tendency to provide answers that are common rather than context-specific. This paper explores theoretical perspectives of…
Descriptors: Integrity, Artificial Intelligence, Technology Uses in Education, Online Courses
Yang Zhen; Xiaoyan Zhu – Educational and Psychological Measurement, 2024
The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…
Descriptors: Artificial Intelligence, Models, Cheating, Identification
Elkhatat, Ahmed M. – International Journal for Educational Integrity, 2023
Academic plagiarism is a pressing concern in educational institutions. With the emergence of artificial intelligence (AI) chatbots, like ChatGPT, potential risks related to cheating and plagiarism have increased. This study aims to investigate the authenticity capabilities of ChatGPT models 3.5 and 4 in generating novel, coherent, and accurate…
Descriptors: Artificial Intelligence, Plagiarism, Integrity, Models
Cesare Giulio Ardito – New Directions for Teaching and Learning, 2025
This chapter presents a critical analysis of generative AI (GenAI) detection tools in higher education assessments. The rapid advancement and widespread adoption of GenAI, particularly in education, necessitates a reevaluation of traditional academic integrity mechanisms. I explore the effectiveness, vulnerabilities, and ethical implications of AI…
Descriptors: Artificial Intelligence, Technology Uses in Education, Higher Education, Identification
Gary D. Fisk – Teaching of Psychology, 2025
Introduction: Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Statement of the problem: Both humans and AI detection technologies have difficulty distinguishing…
Descriptors: Technology Uses in Education, Writing (Composition), Plagiarism, Identification

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