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Rick Somers; Sam Cunningham; Sarah Dart; Sheona Thomson; Caslon Chua; Edmund Pickering – IEEE Transactions on Learning Technologies, 2024
Academic misconduct stemming from file-sharing websites is an increasingly prevalent challenge in tertiary education, including information technology and engineering disciplines. Current plagiarism detection methods (e.g., text matching) are largely ineffective for combatting misconduct in programming and mathematics-based assessments. For these…
Descriptors: Assignments, Automation, Identification, Technology Uses in Education
Bailey, John – Education Next, 2023
This article reports on the release of AI tools that can generate text, images, music, and video with no need for complicated coding but simply in response to instructions given in natural language. AI is also raising pressing ethical questions around bias, appropriate use, and plagiarism. In the realm of education, this technology will influence…
Descriptors: Artificial Intelligence, Technology Uses in Education, Barriers, Affordances
Chat or Cheat? Academic Dishonesty, Risk Perceptions, and ChatGPT Usage in Higher Education Students
Silvia Ortiz-Bonnin; Joanna Blahopoulou – Social Psychology of Education: An International Journal, 2025
Academic dishonesty remains a persistent concern for educational institutions, threatening the reputation of universities. The emergence of Artificial Intelligence (AI) tools exacerbates this challenge as they can be used for chatting but also for cheating. Several scientific papers have analyzed the advantages and risks of using AI tools like…
Descriptors: Artificial Intelligence, Technology Uses in Education, Cheating, Risk
Ali Zeb; Rafid Ullah; Rehmat Karim – International Journal of Information and Learning Technology, 2024
Purpose: This paper aims to examine the opportunities and challenges of using ChatGPT in higher education. Furthermore, it is also discuss the potential risks and plunders of these tools. Design/methodology/approach: The paper discuss the use of artificial intelligence (AI) in academia and explores the opportunities and challenges of using ChatGPT…
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Barriers
Maite Alguacil; Noemí Herranz-Zarzoso; José C. Pernías; Gerardo Sabater-Grande – Journal of Computing in Higher Education, 2024
Cheating in online exams without face-to-face proctoring has been a general concern for academic instructors during the crisis caused by COVID-19. The main goal of this work is to evaluate the cost of these dishonest practices by comparing the academic performance of webcam-proctored students and their unproctored peers in an online gradable test.…
Descriptors: Cheating, Computer Assisted Testing, Randomized Controlled Trials, Supervision
David R. Firth; Adam Gonzales; Michelle Louch; Bryan Hammer – Information Systems Education Journal, 2025
ChatGPT is having an impact on students, and information systems (IS) and computing academic professionals alike. Our goal for this paper is to help faculty and students know the conditions in which generative AI such as ChatGPT should or should not be used. To that end, we describe the development of a 2x2 matrix. On the horizontal axis we have…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Information Systems
Renuka Sharma; Kiran Mehta; Vishal Vyas – Journal of Education for Business, 2024
The propensity to cheat is intrinsic to every kind of education or training that requires effort and commitment. Academic dishonesty is a significant issue among secondary and postsecondary students worldwide. The majority of students have been involved in at least one kind of academic dishonesty in the preceding academic year. The fraud triangle…
Descriptors: Ethics, Cheating, Business Administration Education, Integrity
Mike Perkins; Jasper Roe; Binh H. Vu; Darius Postma; Don Hickerson; James McGaughran; Huy Q. Khuat – International Journal of Educational Technology in Higher Education, 2024
This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity…
Descriptors: Artificial Intelligence, Inclusion, Computer Software, Word Processing
Integrity of Best-Answer Assignments in Large-Enrollment Classes: The Role of Compulsory Attribution
Kurt Schmitz; Veda C. Storey – Journal of Teaching and Learning with Technology, 2024
Many instructional methods that focus on analytical, skill, and competency development have a single or small set of appropriate answers. Best-answer assignments are popular for largeenrollment classes because of the relative ease with which scoring and feedback can be managed at scale. However, cheating is regularly confirmed at disturbingly high…
Descriptors: Large Group Instruction, Assignments, Integrity, Student Evaluation
Brian W. Stone – Teaching of Psychology, 2025
Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading. Method: I surveyed 733…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Hung Manh Nguyen; Daisaku Goto – Education and Information Technologies, 2024
The proliferation of artificial intelligence (AI) technology has brought both innovative opportunities and unprecedented challenges to the education sector. Although AI makes education more accessible and efficient, the intentional misuse of AI chatbots in facilitating academic cheating has become a growing concern. By using the indirect…
Descriptors: Academic Achievement, Cheating, Student Behavior, Artificial Intelligence
Ibrahim Adeshola; Adeola Praise Adepoju – Interactive Learning Environments, 2024
The launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the…
Descriptors: Opportunities, Barriers, Artificial Intelligence, Natural Language Processing
Tobias Kohn – Journal of Computer Assisted Learning, 2025
Background: The recent advent of powerful, exam-passing large language models (LLMs) in public awareness has led to concerns over students cheating, but has also given rise to calls for including or even focusing education on LLMs. There is a perceived urgency to react immediately, as well as claims that AI-based reforms of education will lead to…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Usability
Gregory Allen – ProQuest LLC, 2023
The main objective of this qualitative case study was to explore the causes of academic dishonesty at the tertiary level in Jamaica. The participants were 6 students and 6 faculty members who were selected purposefully from one tertiary institution in Jamaica. The main data-gathering instrument was semi-structured interviews. The study's findings…
Descriptors: Ethics, Cheating, College Students, Foreign Countries
Dilky Felsinger; Thilina Halloluwa; Ishani Fonseka – Education and Information Technologies, 2024
Academic misconduct is a growing problem in online education. While there are ways to curb academic misconduct in online exams, utilization of technology to proctor online exams in a simple manner in limited-resource settings remain unclear. This study set out to identify a reliable technique for utilizing webcam footage to identify instances of…
Descriptors: Video Technology, Computer Assisted Testing, Supervision, Depleted Resources