The Impact of Technology on Academic Dishonesty: Perspectives from Accounting Faculty
Bibliographic record
Abstract
New technology has had a significant impact on higher education, including the area of academic dishonesty. Technology provides new opportunities for students to engage in dishonest behaviour while simultaneously providing faculty members with new ways to control such behaviour. The purpose of this study is to investigate academic dishonesty in accounting programs from the perspective of accounting faculty members with a focus on the impacts of technology. Over 375 survey responses were received from faculty members across Canada and the United States. The results reveal that accounting faculty perceive academic dishonesty to be a significant issue that is compromising the integrity of the classroom and that incidences of academic dishonesty have increased over the past five to ten years. The proliferation of technology has resulted in increased incidences of academic dishonesty, and has a more significant impact on plagiarism as opposed to exams. The three types of academic dishonesty impacted the most by technology are: i) using information without proper referencing; ii) using unauthorized materials during a test; and iii) using another students assignments from a previous semester. There is also broader agreement that creating and using new exams, cases and assignments every year is an effective control against academic dishonesty. For greater effectiveness, assessment should be designed such that student responses are unique; however, this type of assessment requires significant time and effort and faculty members’ contributions in this regard should be encouraged, facilitated and recognised by the academic administration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".