Can ChatGPT Be a Certified Accountant? Assessing the Responses of ChatGPT for the Professional Access Exam in Portugal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: From an exploratory perspective, this paper aims to assess how well ChatGPT scores in an accounting proficiency exam in Portugal, as well as its overall understanding of the issues, purpose and context underlying the questions under assessment. Design/methodology/approach: A quasi-experimental method is used in this study. The questions from an exam by the Portuguese Order of Chartered Accountants (OCC, in the Portuguese acronym) served as input queries, while the responses (outputs) from ChatGPT were compared with those from the OCC. Findings: The findings indicate that ChatGPT’s responses were able to deduce the primary issue underlying the matters assessed, although some responses were inaccurate or imprecise. Also, the tool did not have the same score in all matters, being less accurate in those requiring more professional judgment. The findings also show that the ChatGPT did not pass the exam, although it was close to doing so. Originality: To the best of the authors’ knowledge, there is little research on ChatGPT accuracy in accounting proficiency exams, this being the first such study in Portugal. Practical implications: The findings from this research can be useful to accounting professionals to understand how ChatGPT may be used for practitioners, stressing that it could assist them and improve efficiency, but cannot, at least for now, replace the human professional. It also highlights the potential use of ChatGPT as an additional resource in the classroom, encouraging students to engage in critical thinking and facilitating open discussion with the guidance of teachers. Consequently, it can also prove beneficial for academic purposes, aiding in the learning process.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it