Transforming Educational Assessment: Insights Into the Use of ChatGPT and Large Language Models in Grading
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) technologies in the field of education has prompted significant advancements, particularly in the domain of assessment and grading. This research delves into the potential of large language models, specifically OpenAI's ChatGPT, in simulating human-like interactions and accurately grading student assessments. To accomplish its objectives, the study compares the grading performance of ChatGPT with that of human correctors in a sample of second-year university students. The research findings indicate only a moderate correlation between the grades assigned by ChatGPT and those of human correctors, suggesting nuanced capabilities in providing comprehensive feedback and streamlining the grading process. While the study highlights the benefits of AI integration in education, it also addresses potential risks, including the exacerbation of educational inequalities and the limitations associated with AI's automated nature. This research contributes to the ongoing discourse surrounding AI's role in education, emphasizing the importance of striking a balance between AI and human instruction for optimal educational outcomes.
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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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".