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On Perception of Prevalence of Cheating and Usage of Generative AI

Roman Denkin

TL;DR

This study investigates how teaching staff perceive student cheating and the impact of Generative AI on academic integrity by combining an anonymous survey from the Department of Information Technology at Uppsala University with 2004–2023 cheating-investigation statistics. The results show that while cheating is not viewed as highly prevalent, there is a strong belief that its incidence is increasing, potentially driven by AI access, and that AI usage is widespread but not universally categorized as cheating. Perceptions closely track objective trends, underscoring educators' awareness of evolving academic dishonesty landscapes. The findings have implications for policy development, teaching practices, and AI-detection training, while highlighting the need for broader, demographically informed future research.

Abstract

This report investigates the perceptions of teaching staff on the prevalence of student cheating and the impact of Generative AI on academic integrity. Data was collected via an anonymous survey of teachers at the Department of Information Technology at Uppsala University and analyzed alongside institutional statistics on cheating investigations from 2004 to 2023. The results indicate that while teachers generally do not view cheating as highly prevalent, there is a strong belief that its incidence is increasing, potentially due to the accessibility of Generative AI. Most teachers do not equate AI usage with cheating but acknowledge its widespread use among students. Furthermore, teachers' perceptions align with objective data on cheating trends, highlighting their awareness of the evolving landscape of academic dishonesty.

On Perception of Prevalence of Cheating and Usage of Generative AI

TL;DR

This study investigates how teaching staff perceive student cheating and the impact of Generative AI on academic integrity by combining an anonymous survey from the Department of Information Technology at Uppsala University with 2004–2023 cheating-investigation statistics. The results show that while cheating is not viewed as highly prevalent, there is a strong belief that its incidence is increasing, potentially driven by AI access, and that AI usage is widespread but not universally categorized as cheating. Perceptions closely track objective trends, underscoring educators' awareness of evolving academic dishonesty landscapes. The findings have implications for policy development, teaching practices, and AI-detection training, while highlighting the need for broader, demographically informed future research.

Abstract

This report investigates the perceptions of teaching staff on the prevalence of student cheating and the impact of Generative AI on academic integrity. Data was collected via an anonymous survey of teachers at the Department of Information Technology at Uppsala University and analyzed alongside institutional statistics on cheating investigations from 2004 to 2023. The results indicate that while teachers generally do not view cheating as highly prevalent, there is a strong belief that its incidence is increasing, potentially due to the accessibility of Generative AI. Most teachers do not equate AI usage with cheating but acknowledge its widespread use among students. Furthermore, teachers' perceptions align with objective data on cheating trends, highlighting their awareness of the evolving landscape of academic dishonesty.
Paper Structure (12 sections, 8 figures)

This paper contains 12 sections, 8 figures.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Data Sources
  5. Survey Instrument
  6. Data Analysis
  7. Results
  8. General Perception
  9. Cheating Prevalence Trends
  10. Difference in Experience Groups' Perception
  11. Conclusion
  12. Limitations and Future Research

Figures (8)

  • Figure 1: Distribution of teachers number per years of experience (cumulative)
  • Figure 2: Responses of full group of teachers (1 to 32 years of experience)
  • Figure 3: Number of Cheating Investigations per Year
  • Figure 4: Perception of Trend in Cheating Prevalence per Starting Year of Experience
  • Figure 5: Perception of current cheating prevalence by experience group
  • ...and 3 more figures