Evaluating Artificial Intelligence Competency in Education: Performance of ChatGPT-4 in the American Registry of Radiologic Technologists (ARRT) Radiography Certification Exam
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: The American Registry of Radiologic Technologists (ARRT) leads the certification process with an exam comprising 200 multiple-choice questions. This study aims to evaluate ChatGPT-4's performance in responding to practice questions similar to those found in the ARRT board examination. MATERIALS AND METHODS: We used a dataset of 200 practice multiple-choice questions for the ARRT certification exam from BoardVitals. Each question was fed to ChatGPT-4 fifteen times, resulting in 3000 observations to account for response variability. RESULTS: ChatGPT's overall performance was 80.56%, with higher accuracy on text-based questions (86.3%) compared to image-based questions (45.6%). Response times were longer for image-based questions (18.01 s) than for text-based questions (13.27 s). Performance varied by domain: 72.6% for Safety, 70.6% for Image Production, 67.3% for Patient Care, and 53.4% for Procedures. As anticipated, performance was best on on easy questions (78.5%). CONCLUSION: ChatGPT demonstrated effective performance on the BoardVitals question bank for ARRT certification. Future studies could benefit from analyzing the correlation between BoardVitals scores and actual exam outcomes. Further development in AI, particularly in image processing and interpretation, is necessary to enhance its utility in educational settings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".