Challenges to Audit Education for the 21st Century: A Survey of Curricula, Course Content, and Delivery Methods
Bibliographic record
Abstract
This paper reports the results of a survey of auditing and assurance courses in the U.S. and several other countries conducted during 2000–2001. The survey, commissioned by the Auditing Section of the American Accounting Association, yielded data on a total of 285 auditing and assurance courses taught at 188 colleges and universities in the United States, Canada, and several other countries. The syllabi data were analyzed on a number of dimensions and the results compared to two prior surveys of auditing courses (Frakes 1987; Groomer and Heintz 1994). Our findings document substantial changes in content (e.g., new or expanded coverage of fraud, information technology, and assurance services) and pedagogy (e.g., increased use of team projects, student presentations, cases, and the Internet) in both introductory and advanced auditing courses over the past several years. These changes are discussed in the context of events that significantly impacted auditing education and practice from the late 1980s through the end of the 1990s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".