Student perceptions of academic misconduct in the age of generative AI

Lund, Brady; Lee, Tae Hee; Mannuru, Nishith Reddy; Arutla, Nikhila

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https://hdl.handle.net/2142/130858 Copy

Description

Title

Student perceptions of academic misconduct in the age of generative AI

Author(s)

• Lund, Brady
• Lee, Tae Hee
• Mannuru, Nishith Reddy
• Arutla, Nikhila

Issue Date

2024-10-16

Keyword(s)

• Artificial Intelligence
• Academic Misconduct
• Student Perception
• Higher Education
• Academic Writing
• Information ethics
• Information use
• Students

Abstract

In the contemporary landscape of academia, the integration of artificial intelligence (AI) has significantly altered the dynamics of academic research and writing. This study delves into the perceptions of academic misconduct among university students in the age of AI. Through a structured online survey, participants will articulate their perspectives on various activities and their classification as academic misconduct, as well as the severity of such transgressions. By probing into students' beliefs and attitudes, the research seeks to uncover potential disparities in the understanding and interpretation of academic misconduct within higher education settings. The advent of AI technologies has revolutionized the academic environment, providing students with unparalleled access to information and writing assistance. However, this paradigm shift has also brought forth heightened concerns surrounding academic integrity, particularly with regards to plagiarism. Moreover, the widespread adoption of grammar-checking tools such as Grammarly adds further layers of complexity, as the outputs sometimes resemble those generated by AI, blurring the lines between original work and automated assistance. Utilizing quantitative research methods, this study employs an online survey platform to collect data on student perceptions of academic misconduct. The insights collected from this investigation aim to contribute to a nuanced understanding of the multifaceted nature of academic integrity in the era of AI. Ultimately, the findings seek to inform educational policies and practices, fostering a cohesive and informed approach to upholding academic standards across diverse higher education institutions.

Publisher

Association for Library and Information Science Education

Series/Report Name or Number

Proceedings of the ALISE Annual Conference, 2024

Type of Resource

text

Genre of Resource

• Conference Paper
• Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130858

DOI

https://doi.org/10.21900/j.alise.2024.1753

Copyright and License Information

• Copyright 2024 Brady Lund, Tae Hee Lee, Nishith Reddy Mannuru, Nikhila Arutla
• This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/).

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