Cross-evaluation of Large Language Model Assessment Behaviours in Educational Tasks by Cognitive Level
Bibliographic record
Abstract
Large language models show promise for educational assessment, but their comparative capability across different cognitive domains remains understudied. This paper presents a systematic analysis of seven leading LLMs—ChatGPT, Claude, Gemini, Perplexity, Mistral, Command R+, and Grok—in their ability to both generate and evaluate educational responses across different levels of the revised Bloom’s taxonomy. Using a novel cross-evaluation methodology, 6,045 evaluations were analyzed using a standard rubric examining content accuracy, cognitive alignment, communication clarity, and response depth. The findings revealed three distinct clusters of grading behaviour: lenient evaluators (Mistral, Gemini, and ChatGPT), moderate evaluators (Claude and Grok), and strict evaluators (Command R+ and Perplexity). Significant variations in grading consistency emerged, with ChatGPT showing the greatest consistency and Perplexity the most variability. Notable systematic biases were observed, including Gemini’s positive bias toward Grok and Command R+’s negative bias toward Gemini. These patterns provide a framework for selecting appropriate LLMs for specific educational tasks while highlighting the importance of understanding their individual evaluation tendencies.
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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 | 0.074 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".