Impact of Artificial Intelligence in Promoting Academic Integrity in Education: A Systematic review
Bibliographic record
Abstract
Technological advancements are improving very fast, just recently there was the introduction of ChatGPT has had a serious impact on education. This poses a significant challenge to education integrity, accessibility, and inclusivity. Many students worldwide do online learning due to the pandemic that hit the world back in 2019. This led to integrating online as a strategy to control in-person interactions. Online learning has thus led to increased cases of cheating and a lack of academic integrity. Using Meta-Analysis, this study intends to determine how using artificial intelligence has helped promote academic integrity. This brief scoping study aims to gauge the depth of the literature exploring the relationship between academic honesty and AI in schools of higher learning. This review will examine the papers included to draw conclusions about this developing field and the ethical considerations that must be considered. The higher education administration, faculty, administration, and those studying academic integrity will all find our findings helpful.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".