Is AI Changing the Rules of Academic Misconduct? An In-depth Look at Students' Perceptions of 'AI-giarism'
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ID: 283243
2023
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Abstract
This pioneering study explores students' perceptions of AI-giarism, an
emergent form of academic dishonesty involving AI and plagiarism, within the
higher education context. A survey, undertaken by 393 undergraduate and
postgraduate students from a variety of disciplines, investigated their
perceptions of diverse AI-giarism scenarios. The findings portray a complex
landscape of understanding, with clear disapproval for direct AI content
generation, yet more ambivalent attitudes towards subtler uses of AI. The study
introduces a novel instrument, as an initial conceptualization of AI-giarism,
offering a significant tool for educators and policy-makers. This scale
facilitates understanding and discussions around AI-related academic
misconduct, aiding in pedagogical design and assessment in an era of AI
integration. Moreover, it challenges traditional definitions of academic
misconduct, emphasizing the need to adapt in response to evolving AI
technology. Despite limitations, such as the rapidly changing nature of AI and
the use of convenience sampling, the study provides pivotal insights for
academia, policy-making, and the broader integration of AI technology in
education.
| Reference Key |
chan2023is
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| Authors | Cecilia Ka Yuk Chan |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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