Artificial Intelligence: Integration in Higher-Level Accounting Teaching and Learning Practices
Bibliographic record
Abstract
This literature review examines the issues related to the integration of artificial intelligence in accounting education within the Ontario college context. A review of current scholarly literature reveals important benefits including improved teaching and learning practices. However, the research also cautions about some of the disadvantages including bias and academic integrity breaches. Stakeholder perceptions to artificial intelligence are also explored, including those of educators, students, employers, governments, advocacy groups, and developers. The literature revealed that artificial intelligence can be effectively integrated into classrooms and teaching/learning practices via course design, grading, intelligent tutoring, and planning. However, it also cautioned about the major issues and challenges associated with the use of AI tools and technologies. Notably, the rapid emergence of artificial intelligence has prompted the accounting industry as well as the accounting certification body, the Chartered Professional Accountants of Canada, to aggressively adopt and adapt to AI technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".