Detecting the effect of artificial intelligence on internal audit performance: Empirical study in Saudia Arabia
Bibliographic record
Abstract
This research attempts to investigate the effect of types of AI systems (assisted, augmented, and autonomous) on internal auditing in Saudi Arabia. A questionnaire was used to collect data from 150 internal auditors in Riyadh City. To confirm that the study's goals were met, the descriptive analytical method was used. The questionnaire data is analyzed, and hypotheses are tested, using the Smart pls application. The study’s results show that there is a clear positive effect of AI systems, but different impacts vary according to the kind of AI systems, which is high in augmented systems, moderate in autonomous systems, and weak in assistive intelligence systems. Further studies on this subject can be conducted with larger sample sizes, especially if they are conducted globally. Based on these findings, future research can concentrate on national and cultural conditions. Additionally, companies should adopt more comprehensive involvement methods in internal auditing issues and the development of AI adoption in all company activities.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".