[Paper Review] Is AI Changing the Rules of Academic Misconduct? An In-depth Look at Students' Perceptions of 'AI-giarism'

Cecilia Ka Yuk Chan|arXiv (Cornell University)|Jun 6, 2023

Academic integrity and plagiarism28 citations

TL;DR

The paper investigates students' perceptions of AI-giarism, revealing strong disapproval of AI-generated content but more mixed views on subtler AI-assisted practices, and introduces an initial measurement instrument for AI-related misconduct.

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.

Motivation & Objective

• Understand how students perceive AI-giarism in higher education contexts.
• Develop a novel instrument to conceptualize and measure AI-related academic misconduct.
• Highlight implications for pedagogy, policy, and assessment in the era of AI integration.
• Identify limitations and future directions for researching AI-enabled academic dishonesty.

Proposed method

• Survey of 393 undergraduate and postgraduate students from diverse disciplines.
• Assessment of perceptions across a range of AI-giarism scenarios.
• Introduction of a novel instrument as an initial conceptual framework for AI-giarism.

Experimental results

Research questions

• RQ1 What are students' perceptions of AI-giarism as an emergent form of academic dishonesty?
• RQ2 How do attitudes toward AI-giarism vary across different scenarios and levels of AI involvement?
• RQ3 What are the implications of these perceptions for pedagogy, policy, and assessment in higher education?

Key findings

• There is clear disapproval of direct AI content generation by students.
• Attitudes toward subtler AI uses are more ambivalent or nuanced.
• A novel instrument was developed to conceptualize AI-giarism, aiding educators and policymakers.
• The study emphasizes the need to adapt traditional definitions of academic misconduct in response to evolving AI technology.
• Limitations include rapidly changing AI capabilities and the use of convenience sampling.
• Findings offer pivotal insights for policy-making and the broader integration of AI in education.