Raise or stick? Should I penalise cheaters or ignore cheating?

Academic honesty is fundamental to effective education and learning within degree programmes and to students’ future workplace success. However, academic dishonesty, or ‘cheating’ is growing in scale and scope with more means and forms of cheating being identified by educators. This is only partly due to the emergence of Generative Artificial Intelligence. Educators’ attitudes toward cheating and the impacts it has on them personally and professionally appear to be influencing their response to suspected cheating. In turn this may be contributing to changing attitudes of students towards cheating. Educators must decide whether to focus on academic enforcement or avoidance. Enforcement requires acting on suspected cheating to formally raise work for an academic dishonesty review which results in additional work and potential stress and harassment, whilst avoidance allows the educator to stick to an informal ‘see no evil’ response which addresses the cheating by manipulating marking to fail the work, marking the work as if there is no issue, or not reviewing papers for academic dishonesty. This choice leads to an inconsistent response which can encourage students to play Assignment Russian Roulette by evaluating the risk-reward of cheating and create a difficult raise or stick decision for educators. Choosing ‘raise’ may open an academic Pandora’s Box that can affect workload and wellbeing; choosing ‘stick’ is self-protective but may encourage academic dishonesty and limit learning.

Publication Details

Journal
Worcester Research and Publications (University of Worcester)
Published
2025-10-01
Primary Topic
Academic integrity and plagiarism
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Raise or stick? Should I penalise cheaters or ignore cheating?

Worcester Research and Publications (University of Worcester)
Academic integrity and plagiarism
article

Raise or stick? Should I penalise cheaters or ignore cheating?

article en

Abstract

Academic honesty is fundamental to effective education and learning within degree programmes and to students’ future workplace success. However, academic dishonesty, or ‘cheating’ is growing in scale and scope with more means and forms of cheating being identified by educators. This is only partly due to the emergence of Generative Artificial Intelligence. Educators’ attitudes toward cheating and the impacts it has on them personally and professionally appear to be influencing their response to suspected cheating. In turn this may be contributing to changing attitudes of students towards cheating. Educators must decide whether to focus on academic enforcement or avoidance. Enforcement requires acting on suspected cheating to formally raise work for an academic dishonesty review which results in additional work and potential stress and harassment, whilst avoidance allows the educator to stick to an informal ‘see no evil’ response which addresses the cheating by manipulating marking to fail the work, marking the work as if there is no issue, or not reviewing papers for academic dishonesty. This choice leads to an inconsistent response which can encourage students to play Assignment Russian Roulette by evaluating the risk-reward of cheating and create a difficult raise or stick decision for educators. Choosing ‘raise’ may open an academic Pandora’s Box that can affect workload and wellbeing; choosing ‘stick’ is self-protective but may encourage academic dishonesty and limit learning.

Worcester Research and Publications (University of Worcester)
Openalex Percentile: Top 35%
Academic integrity and plagiarism
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.