Is AI Changing the Rules of Academic Misconduct? An In-depth Look at Students' Perceptions of 'AI-giarism'
Project Overview
The document explores the implications of generative AI in education, particularly focusing on students' perceptions of "AI-giarism," a new form of academic dishonesty that merges AI use with traditional plagiarism. Based on a survey of 393 undergraduate and postgraduate students, the study uncovers a nuanced understanding of AI-giarism, revealing that while students largely disapprove of using AI for direct content generation, they exhibit ambivalence towards more subtle applications of AI tools. This complexity underscores the necessity for educators and policymakers to adapt academic integrity definitions and guidelines to effectively navigate the challenges posed by AI integration in academic settings. The research introduces an innovative framework for conceptualizing AI-giarism, which can assist in developing strategies to uphold academic integrity while embracing the benefits of AI technology in education. The findings highlight the imperative for continuous dialogue and policy adaptation in response to the evolving landscape of AI in higher education.
Key Applications
AI-giarism conceptualization tool
Context: Higher education context involving undergraduate and postgraduate students
Implementation: A survey conducted to gather student perceptions regarding AI-giarism scenarios
Outcomes: Insight into students' understanding of AI-giarism, aiding in pedagogical design and policy development
Challenges: Rapidly changing nature of AI, reliance on convenience sampling, and potential misinterpretations of AI-giarism
Implementation Barriers
Ethical barrier
Ambiguity in defining academic misconduct related to AI-generated content due to the evolving nature of AI technology.
Proposed Solutions: Development of clear and consistent guidelines for referencing AI-generated content, informed by stakeholders and ethical considerations. Integrating AI literacy into academic integrity policies.
Technical barrier
Current plagiarism detection tools struggle to identify AI-generated content that is original in wording but not in ideas or structure.
Proposed Solutions: Developing AI tools with built-in citation features.
Educational barrier
Students display varying levels of understanding of traditional plagiarism and AI-plagiarism, indicating a need for improved educational initiatives.
Proposed Solutions: Implementing workshops and training on academic integrity and ethical AI use to enhance students' understanding.
Project Team
Cecilia Ka Yuk Chan
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Cecilia Ka Yuk Chan
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18