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Showing 1 to 15 of 54 results Save | Export
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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
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Shengnan Han; Shahrokh Nikou; Workneh Yilma Ayele – International Journal of Educational Management, 2024
Purpose: To improve the academic integrity of online examinations, digital proctoring systems have recently been implemented in higher education institutions (HEIs). The paper aims to understand how digital proctoring has been practised in higher education (HE) and proposes future research directions for studying digital proctoring in HE.…
Descriptors: Computer Assisted Testing, Supervision, Higher Education, Cheating
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Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2021
One of the main goals of assignments in the academic environment is to assess the students' knowledge and mastery of a specific topic, and it is crucial to ensure that the work is original and has been solely made by the students to assess their competence acquisition. Therefore, Text-Matching Software Products (TMSPs) are used by academic…
Descriptors: Plagiarism, Identification, Assignments, Computer Software
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E. A. J. Terblanche; Annelien Adriana van Rooyen; P. C. Enwereji – Discover Education, 2024
The COVID-19 pandemic accelerated the implementation of online assessments at an unprecedented pace. The pandemic required most higher education institutions worldwide to implement online assessments almost overnight. The study aimed to gain an understanding of auditing students' perceptions of online assessments and e-proctoring systems during…
Descriptors: Computer Assisted Testing, Supervision, Student Attitudes, Electronic Learning
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Yingying Jiang; Lindai Xie; Guohui Lin; Fangfang Mo – Education and Information Technologies, 2024
ChatGPT has surprised academia with its remarkable abilities but also raised substantial concerns regarding academic integrity and misconduct. Despite the debate, empirical research exploring the issue is limited. The purpose of this study is to bridge this gap by analyzing Twitter data to understand how academia is perceiving ChatGPT. A total of…
Descriptors: Artificial Intelligence, Computer Software, College Faculty, Social Media
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Silvia Sierra-Martínez; María-Esther Martínez-Figueira; María Dolores Castro Pais; Teresa Pessoa – British Educational Research Journal, 2024
Academic integrity is part of the process that explains the communication of information in an ethical manner. Although the prevalence of dishonest acts at university has been noted, it is an aim of the educational system to analyse what motivates them from an age prior to their incorporation into university studies. The aim of this work is to…
Descriptors: Plagiarism, Photography, Participatory Research, High School Students
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Ma, Leo F. H.; Horban, Yurii; Skachenko, Olena – portal: Libraries and the Academy, 2022
This study explores the role of the Chinese University of Hong Kong (CUHK) Library and the Scientific Library of the Kyiv National University of Culture and Arts (KNUCA) in Ukraine in enhancing academic integrity in the university community. It compares the methods of academic integrity training offered by the libraries of the two universities…
Descriptors: Foreign Countries, Integrity, Ethics, Cheating
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Dawson, Phillip; Sutherland-Smith, Wendy; Ricksen, Mark – Assessment & Evaluation in Higher Education, 2020
Contract cheating happens when students outsource their assessed work to a third party. One approach that has been suggested for improving contract cheating detection is comparing students' assignment submissions with their previous work, the rationale being that changes in style may indicate a piece of work has been written by somebody else. This…
Descriptors: Cheating, Identification, Accuracy, Computer Software
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Lakshminarayanan, Srinivasan; Rao, N. J. – Higher Education for the Future, 2022
There are many grey areas in the interpretation of academic integrity in the course on Introduction to Programming, commonly known as CS1. Copying, for example, is a method of learning, a method of cheating and a reuse method in professional practice. Many institutions in India publish the code in the lab course manual. The students are expected…
Descriptors: Integrity, Cheating, Duplication, Introductory Courses
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Huiling Ma; Lilliati Ismail; Weijing Han – Education and Information Technologies, 2024
The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nevertheless, there are currently few studies that…
Descriptors: Bibliometrics, Artificial Intelligence, Computer Software, Second Language Learning
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Emery-Wetherell, Meaghan; Wang, Ruoyao – Assessment & Evaluation in Higher Education, 2023
Over four semesters of a large introductory statistics course the authors found students were engaging in contract cheating on Chegg.com during multiple choice examinations. In this paper we describe our methodology for identifying, addressing and eventually eliminating cheating. We successfully identified 23 out of 25 students using a combination…
Descriptors: Computer Assisted Testing, Multiple Choice Tests, Cheating, Identification
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Peter Bannister; Elena Alcalde Peñalver; Alexandra Santamaría Urbieta – Journal for Multicultural Education, 2024
Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and…
Descriptors: Language of Instruction, English (Second Language), Second Language Learning, Integrity
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Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
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Judy Lambert; Mark Stevens – Computers in the Schools, 2024
ChatGPT has garnered unprecedented popularity since its release in November 2022. This artificial intelligence (AI) large language model (LLM) is designed to generate human-like text based on patterns found in massive amounts of data scraped from the internet. ChatGPT is significantly different from previous versions of GPT by its quality of…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Writing Instruction
Editorial Projects in Education, 2024
Addressing academic integrity in the age of AI is essential to ensure honesty and student success. This Spotlight will help you learn about how educators nationwide are approaching AI in teaching and learning; review data investigating how many students are actually using AI to cheat; examine strategies teachers are using to fight AI cheating;…
Descriptors: Integrity, Artificial Intelligence, Teaching Methods, Computer Software
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