cs.AIMar 24, 2026
SaveLLMs Do Not Grade Essays Like Humans
Organizations: University of Alberta, 116 St & 85 Ave, Edmonton, AB, T6G 2R3, Canada
Abstract
Large language models have recently been proposed as tools for automated essay scoring, but their agreement with human grading remains unclear. In this work, we evaluate how LLM-generated scores compare with human grades and analyze the grading behavior of several models from the GPT and Llama families in an out-of-the-box setting, without task-specific training. Our results show that agreement between LLM and human scores remains relatively weak and varies with essay characteristics. In particular, compared to human raters, LLMs tend to assign higher scores to short or underdeveloped essays, while assigning lower scores to longer essays that contain minor grammatical or spelling errors. We also find that the scores generated by LLMs are generally consistent with the feedback they generate: essays receiving more praise tend to receive higher scores, while essays receiving more criticism tend to receive lower scores. These results suggest that LLM-generated scores and feedback follow coherent patterns but rely on signals that differ from those used by human raters, resulting in limited alignment with human grading practices. Nevertheless, our work shows that LLMs produce feedback that is consistent with their grading and that they can be reliably used in supporting essay scoring.
Figures & tables
Figure 1: Overview of the proposed analysis framework. Essays are first evaluated by LLMs to produce both predicted scores and textual feedback. Essay-level features and feedback-derived signals are then extracted and analyzed to compare LLM scores with human ratings and to examine how feedback sentiment and essay characteristics relate to the scores assigned by the models.
Figure 2: Distribution of human-assigned essay scores across datasets.
| Name | Subset | Num essays | Essay length (words) | Num prompts | Num raters | Score range | Score step | Num traits | NER tags |
|---|---|---|---|---|---|---|---|---|---|
| ASAP | Task 1 | 1,783 | 410.30 136.18 | 1 | 2 | 1–6 | 1 | 5 | Yes |
| Task 7 | 1,569 | 191.67 100.12 | 1 | 2 | 0–12 | 1 | 4 | Yes | |
| DREsS | New | 1,917 | 360.65 107.01 | 59 | 1 | 3–15 | 0.5 | 3 | No |
Table 1: Summary of the datasets used in the experiments.
Figure 3: Distribution of essay scores assigned by Llama and GPT models on the ASAP Task 7 dataset. Most models produce scores concentrated around the middle of the scale, while GPT-3.5 assigns comparatively lower scores with predictions concentrated toward the lower end.
Figure 4: Quadratic Weighted Kappa (QWK) scores between LLM-generated scores and human ratings across datasets. Lighter cells indicate greater agreement. While human raters exhibit relatively strong agreement with each other, agreement between LLM predictions and human scores remains generally lower across models and datasets.
Figure 5: Pearson correlation between LLM-generated scores and human ratings across datasets. Lighter cells indicate greater agreement. Although some models show moderate correlations with human scores, the overall agreement remains lower than the correlation observed between human raters.
Figure 6: Mean Absolute Error (MAE) between LLM-generated scores and human ratings across datasets. Lighter cells indicate greater agreement (smaller differences between scores). Across datasets and models, LLM predictions consistently exhibit larger errors than the differences observed between human raters.
Figure 7: Differences between Llama-assigned scores and human ratings grouped by human score across datasets. Columns correspond to Llama models and rows to datasets. Positive values indicate higher model scores than human ratings. Llama models assign relatively higher scores to low-scoring essays and lower scores to high-scoring essays, indicating compression of the score range.
Figure 8: Differences between GPT-assigned scores and human ratings grouped by human score across datasets. Columns correspond to GPT models and rows to datasets. Positive values indicate higher model scores than human ratings. Similar to Llama models, GPT models assign higher scores to low-scoring essays and lower scores to high-scoring essays, indicating compression of the score range.
Figure 9: Readability scores (Flesch Reading Ease, top) and essay lengths (number of tokens, bottom) for the ASAP Task 1 dataset grouped by human score. Essays receiving higher scores tend to be substantially longer, while readability varies less across score levels.
Figure 10: Average distribution of LanguageTool-detected error types in essays that received the maximum possible score from human raters. All such essays contain at least one detected error, most commonly spelling mistakes, suggesting that minor language issues are often tolerated by human raters.
Figure 11: Proportion of praise (bottom) and criticism (top) associated with different rubric traits across essays with different LLM-assigned scores on the DREsS dataset. As scores increase, praise becomes more frequent while criticism decreases across most traits, indicating that feedback sentiment generally corresponds to the assigned scores.
Figure 12: Proportion of criticism (top) and praise (bottom) associated with rubric traits across essays with different LLM-assigned scores on ASAP Task 1. Clear score-dependent trends are observed for traits such as content, organization, and awareness of audience, while other traits show more irregular patterns across score levels.
Figure 13: SHAP beeswarm plots showing the contribution of trait-specific praise and criticism mentions to predicted essay scores on ASAP Task 7. Features sorted in decreasing order of importance (mean absolute SHAP value). Points represent individual essays, and horizontal position indicates the impact on the predicted score (SHAP value). Praise-related features generally increase predicted scores, while criticism-related features decrease them.
Figure 14: Distribution of trait-specific praise (left) and criticism (right) across models for essays in ASAP Task 1. Praise is concentrated on a small number of traits, particularly organization and content, while criticism is distributed more broadly across traits.
Figure 15: Distribution of trait-specific praise (left) and criticism (right) across models for essays in ASAP Task 7. Positive mentions are concentrated mainly on ideas , with some models also placing substantial emphasis on style , whereas negative mentions are distributed more broadly across the rubric traits.
Figure 16: Distribution of trait-specific praise (left) and criticism (right) across models for essays in the DREsS dataset. Trait mentions are uneven: praise is most frequently associated with content , while criticism places greater emphasis on language , with organization playing a secondary role.
Figure 17: Average absolute SHAP values for trait-specific praise and criticism features for GPT models on ASAP Task 7. Higher values indicate stronger influence on predicted scores, highlighting differences in trait importance across models.
Appendix figures & tables17 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 24: Distribution of essay scores assigned by Llama and GPT models on the ASAP Task 1 dataset. Llama models tend to assign mid-range scores (around 2-4), while GPT models show stronger variation across versions: GPT-3.5 concentrates on very low scores (1-2), whereas GPT-5 produces mostly mid-range scores.
Figure 25: Distribution of essay scores assigned by Llama and GPT models on the DREsS dataset. Most models concentrate scores in the mid-range (7-10), though the distributions vary across models, with Llama 2 assigning many high scores and GPT-5 showing a smoother, centered distribution..
| Dataset | Trait | Extracted Terms |
| ASAP Task 1 | Content | position, claim, support, reason, detail, evidence, argument, causal reasoning, counterarguments, reasoning, thesis, elaboration, development, explanation |
| Organization | organization, structure, paragraph, coherence, logical flow, progression, topic sentence | |
| Word Choice | language, word, phrase, vocabulary, word choice, choice of words, use of words, command of words | |
| Sentence Fluency | sentence, sentence fluency, quality of sentences, sentence structure, sentence variety, flow of sentences, sentence flow, clarity | |
| Conventions | conventions, spelling, punctuations, writing conventions, capitalization, grammar, grammatical, mechanics | |
| Transitions | transition, transitional language, transitional phrases, transitional words, connective, linking word |
Table 2: Trait-specific lexical cues used to identify praise and criticism mentions in model-generated feedback across datasets.
Figure 26: Agreement between LLM-generated scores and human grades under different prompt configurations on ASAP Task 1, measured using QWK, MAE, and Pearson correlation. Lighter cells indicate higher agreement (and lower error for MAE). Human raters show substantially stronger agreement with each other than with LLM predictions, while prompt variants yield only modest and inconsistent improvements across models.
Figure 27: Agreement between LLM-generated scores and human grades under different prompt configurations on ASAP Task 7, measured using QWK, MAE, and Pearson correlation. Lighter cells indicate higher agreement (and lower error for MAE). Human-human agreement remains substantially stronger than LLM-human agreement, while prompt variants produce only limited and inconsistent improvements.
Figure 28: Agreement between LLM-generated scores and human grades under different prompt configurations on the DREsS dataset, measured using QWK, MAE, and Pearson correlation. Lighter cells indicate higher agreement (and lower error for MAE). Agreement levels vary across models, and prompt variants lead to only modest and inconsistent changes.
Figure 29: Pairwise agreement between LLM-generated scores on the ASAP Task 1 dataset, measured using QWK (top) and Pearson correlation (bottom). Lighter cells indicate higher agreement. LLMs from the same family and prompt configuration tend to show stronger agreement with each other, while agreement between different model families is generally lower.
Figure 30: Pairwise agreement between LLM-generated scores on the ASAP Task 7 dataset, measured using QWK (top) and Pearson correlation (bottom). Lighter cells indicate higher agreement. Agreement is generally stronger between variants of the same model family and prompt configuration than across different models.
Figure 31: Pairwise agreement between LLM-generated scores on the DREsS dataset, measured using QWK (top) and Pearson correlation (bottom). Lighter cells indicate higher agreement. Agreement is generally stronger between variants of the same model family and prompt configuration than across different models.
Figure 35: Readability scores (Flesch Reading Ease, top) and essay lengths (number of tokens, bottom) for the ASAP Task 7 dataset. Each boxplot represents essays within a specific score range. Essay length increases consistently with score, while readability varies less systematically across score levels.
Figure 36: Readability scores (Flesch Reading Ease, top) and essay lengths (number of tokens, bottom) for the DREsS dataset. Each boxplot represents essays within a specific score range. Similar to ASAP Task 7, essay length increases with score, while readability shows weaker variation across score levels.
Figure 37: SHAP beeswarm plots showing the impact of trait-specific praise and criticism mentions on predicted essay scores for GPT models on ASAP Task 7. Features are sorted in decreasing order of importance (mean absolute SHAP value). Points represent individual essays, and horizontal position indicates the contribution to the predicted score. Praise-related features tend to increase predicted scores, while criticism-related features decrease them.
Figure 38: Average absolute SHAP values for trait-specific praise and criticism features for Llama models on ASAP Task 7. Higher values indicate features that contribute more strongly to predicted scores, revealing differences in how models prioritize rubric traits.
Figure 39: SHAP beeswarm plots showing the impact of trait-specific praise and criticism mentions on predicted essay scores for Llama models on DREsS. Features are sorted in decreasing order of importance (mean absolute SHAP value). Points represent individual essays, and horizontal position indicates the contribution to the predicted score. Praise-related features tend to increase predicted scores, while criticism-related features decrease them.
Figure 40: SHAP beeswarm plots showing the impact of trait-specific praise and criticism mentions on predicted essay scores for GPT models on DREsS. Features are sorted in decreasing order of importance (mean absolute SHAP value). Points represent individual essays, and horizontal position indicates the contribution to the predicted score. Praise-related features tend to increase predicted scores, while criticism-related features decrease them.
Figure 41: Average absolute SHAP values for trait-specific praise and criticism features for Llama models on DREsS. Higher values indicate features that contribute more strongly to predicted scores, revealing differences in how models prioritize rubric traits.
Figure 42: Average absolute SHAP values for trait-specific praise and criticism features for GPT models on DREsS. Higher values indicate features that contribute more strongly to predicted scores, revealing differences in how models prioritize rubric traits.