Navigating the Complexities of AI Writing Detection: Comparing the Perception between Students and Instructors

The integration of AI generated content into academic research and paper writing has revolutionized scholarly practices, granting students unprecedented access to information and writing support. However, this advancement has also raised serious concerns regarding plagiarism and academic integrity, leading to the emergence of AI detection services. Despite the proliferation of these tools, questions persist about the reliability and consistency of the services, posing challenges to upholding academic integrity. Additionally, the use of grammar check programs like Grammarly by students has introduced further complexities, as their outputs may sometimes resemble those produced by AI writers, blurring distinctions between original work and automated assistance. This research poster investigates and compares the perceptions and attitudes of students and instructors towards AI writers and AI writing detection programs. By examining the perceived usefulness, trustworthiness, and efficiency of AI detection services in preventing plagiarism and promoting academic integrity, the research seeks to uncover divergent attitudes and perceptions between these two groups. Employing quantitative research methods, including surveys with students and instructors, this study delves into the actual use of AI writing detection services. Specifically, by exploring and comparing the perceptions and attitudes towards these technologies between the two groups, the research aims to uncover potential gaps and areas for improvement. Ultimately, the findings of this study aim to inform the development of strategies and guidelines to enhance AI writing detection services, thereby fostering a culture of academic integrity in the digital age.

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Publication Details

Journal
Proceedings of the ALISE Annual Conference
Published
2024-10-16
DOI
https://doi.org/10.21900/j.alise.2024.1740
Citations
1
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.41
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article

Navigating the Complexities of AI Writing Detection: Comparing the Perception between Students and Instructors

Brady Lund, Nishith Reddy Mannuru, Tae Hee Lee, Nikhila Arutla
1 citations
Proceedings of the ALISE Annual Conference
Online Learning and Analytics
0.41
article

Navigating the Complexities of AI Writing Detection: Comparing the Perception between Students and Instructors

Brady Lund, Nishith Reddy Mannuru, Tae Hee Lee, Nikhila Arutla
article en
1 citations

Abstract

The integration of AI generated content into academic research and paper writing has revolutionized scholarly practices, granting students unprecedented access to information and writing support. However, this advancement has also raised serious concerns regarding plagiarism and academic integrity, leading to the emergence of AI detection services. Despite the proliferation of these tools, questions persist about the reliability and consistency of the services, posing challenges to upholding academic integrity. Additionally, the use of grammar check programs like Grammarly by students has introduced further complexities, as their outputs may sometimes resemble those produced by AI writers, blurring distinctions between original work and automated assistance. This research poster investigates and compares the perceptions and attitudes of students and instructors towards AI writers and AI writing detection programs. By examining the perceived usefulness, trustworthiness, and efficiency of AI detection services in preventing plagiarism and promoting academic integrity, the research seeks to uncover divergent attitudes and perceptions between these two groups. Employing quantitative research methods, including surveys with students and instructors, this study delves into the actual use of AI writing detection services. Specifically, by exploring and comparing the perceptions and attitudes towards these technologies between the two groups, the research aims to uncover potential gaps and areas for improvement. Ultimately, the findings of this study aim to inform the development of strategies and guidelines to enhance AI writing detection services, thereby fostering a culture of academic integrity in the digital age.

Proceedings of the ALISE Annual Conference
University of North Texas (US)
Quality Education
Openalex Percentile: Top 26%
Online Learning and Analytics
0.41
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Navigating the Complexities of AI Writing Detection: Comparing the Perception between Students and Instructors — Brady Lund, Nishith Reddy Mannuru, et al. · Proceedings of the ALISE Annual Conference (2024) | TGRS Research Map | TGRS