Publication Date
| In 2026 | 1 |
| Since 2025 | 16 |
| Since 2022 (last 5 years) | 67 |
| Since 2017 (last 10 years) | 121 |
| Since 2007 (last 20 years) | 178 |
Descriptor
| Computer Software | 194 |
| Identification | 194 |
| Foreign Countries | 56 |
| Plagiarism | 52 |
| Artificial Intelligence | 40 |
| Teaching Methods | 36 |
| Computational Linguistics | 29 |
| College Students | 28 |
| Comparative Analysis | 26 |
| Cheating | 25 |
| Integrity | 25 |
| More ▼ | |
Source
Author
| Bahreini, Kiavash | 2 |
| Cheers, Hayden | 2 |
| Elkhatat, Ahmed M. | 2 |
| Joy, Mike | 2 |
| Lin, Yuqing | 2 |
| Nadolski, Rob | 2 |
| Westera, Wim | 2 |
| Abd-Elaal, El-Sayed | 1 |
| Abdalla, Mohamed | 1 |
| Abdullah, Nor Aniza | 1 |
| Adhitama, Egy | 1 |
| More ▼ | |
Publication Type
Education Level
Audience
| Researchers | 2 |
| Practitioners | 1 |
Location
| Turkey | 6 |
| United Kingdom | 6 |
| Netherlands | 5 |
| Japan | 4 |
| Taiwan | 4 |
| Australia | 3 |
| China | 3 |
| Germany | 3 |
| Canada | 2 |
| Colombia | 2 |
| Connecticut | 2 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Caner Dilber; Ismail Yosumaz – Journal of Academic Ethics, 2026
The rapid advancement of language translation tools and generative artificial intelligence applications has significantly facilitated the production of academic research while simultaneously introducing new challenges to maintaining academic ethics and detecting plagiarism. This study examines how plagiarism rates vary when academic texts are…
Descriptors: Plagiarism, Translation, Computer Software, Identification
Yiting Wang; Tong Li; Jiahui You; Xinran Zhang; Congkai Geng; Yu Liu – ACM Transactions on Computing Education, 2025
Understanding software modelers' difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers' analysis and thought processes. However, existing research primarily focuses on identifying obvious issues in the software…
Descriptors: Computer Software, Engineering Education, Models, Identification
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
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
Austin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
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
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
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
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
Sunilkumar, Dolly; Kelly, Steve W.; Stevenage, Sarah V.; Rankine, Dillon; Robertson, David J. – Applied Cognitive Psychology, 2023
In several applied contexts (e.g., earwitness testimony), the accurate recognition of unfamiliar voices can be a critical part of the person identification process. However, recognising unfamiliar voices is prone to error. While such errors could be reduced by testing the proficiency of listeners, the established tests of unfamiliar voice matching…
Descriptors: Identification, Audio Equipment, Computer Software, Automation
Siraprapa Kotmungkun; Wichuta Chompurach; Piriya Thaksanan – English Language Teaching Educational Journal, 2024
This study explores the writing quality of two AI chatbots, OpenAI ChatGPT and Google Gemini. The research assesses the quality of the generated texts based on five essay models using the T.E.R.A. software, focusing on ease of understanding, readability, and reading levels using the Flesch-Kincaid formula. Thirty essays were generated, 15 from…
Descriptors: Plagiarism, Artificial Intelligence, Computer Software, Essays
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
Drisko, James W. – Journal of Social Work Education, 2023
Plagiarism is a continuing and growing concern in higher education and in academic publishing. Educating to avoid plagiarism requires ongoing efforts at all levels and clear policies that explain the several types of plagiarism and potential consequences when it is found. Identifying plagiarism requires complex judgments and is not a simple matter…
Descriptors: Plagiarism, Computer Software, Identification, Computational Linguistics
James McGibney – ProQuest LLC, 2024
This phenomenological study's objective was to review the existing initiatives for cybersecurity awareness and training that are in place for the K-12 education system throughout the United States. Every facet of life is now plagued by the potential, perceived, and real threat of cyber warfare, which includes, but is not limited to, K-12 schools…
Descriptors: Information Security, Computer Security, Crime, Kindergarten

Peer reviewed
Direct link
