Cogniroot Edge: A Quest for A Fair AI Grader
Bibliographic record
Abstract
This paper proposes an AI-based grading software to ensure fair and high-quality grading for large classes. Fair and high-quality grading is a fundamental necessity in an education system; however, it can be quite challenging for large classes. Due to manpower limitations and the complexity of grading, multiple-choice questions (MCQs) are often the preferred approach for assessing the performance of large classes. MCQs allow automated grading, minimizing manpower requirements and grading errors. However, MCQ-based approaches limit the quality of the evaluation process since they do not accommodate other forms of assessment, such as short-answer questions, essays, and mathematical problems. To address this challenge, this research work developed an AI-based grading method capable of automatically assessing short answers, essays, and math problems. This research conducted extensive usability testing on the developed AI grader. The results indicate that the AI grader has significant potential to enhance the evaluation process for large classes; however, there is room for improvement in its performance which have been identified and highlighted in this paper for further research in this domain.
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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 | 0.038 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".