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Most existing math benchmarks for LLMs focus on evaluating correct solutions. In educational settings, however, it is equally important to understand whether LLMs grasp the pedagogical intent behind math problems, beyond producing the right solution. Tagging curriculum standards is challenging for the same reason: distinguishing fine-grained standards requires understanding subtle pedagogical distinctions. In this paper, we use the MathFish benchmark, which frames alignment as multi-label prediction over 385 Common Core State Standards, to evaluate a three-stage pipeline inspired by baseline failure modes in retrieval and structural reasoning: curriculum-informed hard negatives (M1), a cross-encoder re-ranker (M2), and a ReAct agent paired with an LLM-as-a-judge critic (M3). We additionally evaluate a training-free alternative (A1) combining hybrid sparse-dense retrieval with curriculum graph reranking. M3 achieves 31.3% exact match, roughly 6.5 times the three-shot GPT-4-Turbo baseline, with the largest gains from deliberative multi-step reasoning. Error analysis shows that even with these improvements, the pipeline struggles with missing predictions, grade-level misalignment, and sibling confusion, reinforcing that precise curriculum alignment is a fundamentally hard problem in educational NLP.

ACL 2026

A Bigger Catch: Fine-Grained Curriculum Standards Alignment on the MathFish Benchmark

curriculum alignment

large language models (llms)

educational nlp

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Beyond performance, model transparency is a crucial factor in Automated Essay Scoring, yet current systems often lack explainability, limiting their pedagogical value and users' trust. Existing explainability methods, such as gradient-based attribution or feature-importance approaches, either produce counterintuitive explanations or are too complex for classroom use. To address this limitation, we make use of fine-grained prediction at the sentence level as a way to enhance explainability. We propose ablation strategies to derive sentence-level pseudo scores from essay-level gold scores and use them to train sentence-level models. We evaluate their performance against essay-level baselines on two datasets (ASAP and MEWS), and compare their sentence-level output to a human baseline. Results indicate a trade-off between essay-level performance and sentence-level granularity. For the language quality trait, most sentence-level models achieve performance comparable to the essay-level baseline, whereas for content, the approach yields more positive results on prompts with shorter

Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions

Grammatical Error Correction (GEC) requires models to make edit decisions under competing objectives: correcting errors while either minimizing changes or maximizing fluency.
However, we lack a principled characterization of how instruction-following Large Language Models (LLMs) shift their edit decisions across such editing modes, and whether standard evaluation setups faithfully reflect these shifts.
We address this gap by defining three modes—Neutral, Minimal-Edit, and Fluency-Edit—and measuring neutral-anchored performance shifts to quantify instructional sensitivity.
We benchmark seven LLMs, including proprietary and open-weight models, in a unified zero-shot prompting schema on CoNLL-2014, BEA-2019, and JFLEG datasets.
The Minimal-Edit instruction mitigates over-editing and typically boosts precision; in some settings, strong models also improve recall, suggesting more selective and effective corrections.
In contrast, the Fluency-Edit instruction often encourages broader paraphrastic rewriting that may improve perceived fluency while lowering GLEU, suggesting both a metric-objective mismatch and a shift away from targeted local correction.
Notably, Claude-Sonnet-4.5 demonstrates superior zero-shot capabilities, outperforming previously reported scores and matching or even exceeding few-shot results across CoNLL-2014 (F_0.5: 67.05), BEA-2019 (F_0.5: 64.91), and JFLEG (GLEU: 66.09).

Instruction-Following LLMs for Grammatical Error Correction: Analyzing Neutral-Anchored Instructional Sensitivity Across Editing Modes

Automated short answer grading (ASAG) with large language models (LLMs) is commonly evaluated with aggregate metrics such as macro-F1 and Cohen's kappa. However, these metrics provide limited insight into how grading performance varies across student responses of differing grading difficulty. We introduce an evaluation framework for LLM-based ASAG based on item response theory (IRT), which models grading correctness as a function of latent grader ability and response grading difficulty. This formulation enables response-level analysis of where LLM graders succeed or fail and reveals robustness differences that are not visible from aggregate scores alone. We apply the framework to 17 open-weight LLMs on the SciEntsBank and Beetle benchmarks. The results show that even models with similar overall performance differ substantially in how sharply their grading accuracy declines as response difficulty increases. In addition, confusion patterns show that errors on difficult responses concentrate disproportionately on the \texttt{partially\_correct\_incomplete} label, indicating a tendency toward intermediate-label collapse under ambiguity. To characterize difficult responses, we further analyze semantic and linguistic correlates of estimated difficulty. Across both datasets, higher difficulty is associated with weaker semantic alignment to the reference answer, stronger contradiction signals, and greater semantic isolation in embedding space. Overall, these results show that item response theory offers a useful framework for evaluating LLM-based ASAG beyond aggregate performance measures.

Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory

This paper explores the application of a Large Language Model (LLM) using k-shot prompting with large k for automatically scoring a German Written Elicited Imitation Test (WEIT), a test for assessing literacy-dependent procedural knowledge in German as a foreign language. In this test, test-takers are briefly presented with written sentences which they then have to reproduce in writing as accurately as possible. The responses are scored on an ordinal scale which differentiates between different types of erros (e.g. lexical vs. grammatical). We find that with increasing k (in a range from 1 to 700) accuracy increases significantly but it also depends on the drawn sample and varies across different runs of the same prompt. Overall, the k-shot setting which relies on in-context learning without being provided with the scoring rubric outperforms a baseline where only the scoring rubric is provided to the model. However, the LLM does not outperform previous results based on rule-based or BERT-based models.

Using k-Shot Prompting with Large k for the Automated Scoring of a German Written Elicited Imitation Test

We discuss the development of Kelvi, an open source web-based dictionary and morphological parser designed to aid Tamil learners in developing their literacy skills. 
Tamil is an agglutinative language and heavily suffixal. Existing Tamil dictionaries only carry stems, not conjugated or inflected forms, and for a beginner learner of the language, isolating the stem in an unfamiliar word can be very challenging. 
Kelvi provides 1) the stem of any input word alongside its definition, and 2) non-technical descriptions of any suffixes that are part of this input, so that learners will gradually start to recognize these suffixes and be able to understand and produce new Tamil words themselves. 
In detailing our process of collaborative research, user interviews, suffix database creation, and error analysis, we also hope to show that Kelvi can be adapted for other languages and has the potential to be a useful pedagogical aid for learner literacy development, especially for agglutinative and/or polysynthetic languages which tend to be otherwise underserved in the mainstream.

Kelvi: A "Pedagogical" Morphological Parser to Support Tamil Literacy

Automatic question generation with large language models has advanced rapidly, yet producing assessment-ready items, complete with mark schemes and expected answers, remains challenging, especially when generation must reliably target higher-order cognitive levels in Bloom's Taxonomy. We propose a multi-agent, multi-stage framework that generates structured assessment tuples for both short-answer questions (SAQs) and scenario-based questions (SBQs), combining Bloom-specialized generation agents with staged decomposition and automated verification. We further introduce a rubric-guided LLM-as-a-judge evaluation framework with Bloom-specific alignment metrics. Experiments on university-level AI course material across five generation pipelines show that prompt-level Bloom conditioning alone is insufficient to reliably achieve cognitive control. In contrast, our structured approach yields consistent and notable improvements in alignment, mark scheme quality, and output yield, particularly for higher-order Bloom levels over baseline pipelines.

From Questions to Assessment Tuples: A Multi-Agent Framework with Bloom-Specialized Agents and Automated Verification

We investigate the predictive power of keystroke logging data for automated essay scoring using the newly collected PISA FLA writing process dataset. Based on 3,882 writing sessions, we extract a comprehensive set of keystroke-based process features, including temporal measures, pause and burst patterns, deletion behavior, production efficiency, and navigation activity and evaluate their ability to predict holistic essay scores on a 0–5 scale. We specifically compare process-feature-based models with content-based scoring approaches trained on data written with and without the help of an AI chatbot, and investigate how predictive power evolves over the course of a writing session by training models at multiple time thresholds.
Our analysis reveals that keystroke features provide genuine early predictive signal, capturing aspects of writing fluency and revision behavior that distinguish writers before their texts are long enough to score conventionally. 
Additionally, our results suggest that process-based scoring is a viable complement to product-based approaches, with promise for formative, real-time feedback during writing.

KEYSCORE — Keystroke-enhanced Automated Essay Scoring

We introduce FDE-Arg, a new dataset of argumentative student essays in German. We use two Llama models of different sizes to label sentence-level content zones both in FDE-Arg and in an existing dataset of source-dependent argumentative essays. We investigate three approaches for improving model performance: a) Incorporating targeted task information into the prompt text; b) few-shot prompting with up to 10 examples selected on the basis of similarity with the target instance; and c) parameter-efficient finetuning. While the finetuning approach has not been shown to be successful, both incorporating additional information in the prompts and similarity-based few-shot prompting have produced highly promising performance gains compared to the baseline.

Fine-Grained Content Zone Prediction in German Argumentative Essays Using LLMs

This paper addresses the problem of fine-grained error classification in stepwise algebraic problem solving, with the objective of enabling accurate and timely feedback in large-scale educational environments. Using authentic student response data, we compare a carefully engineered rule-based baseline with large language models (LLMs) in zero-shot and few-shot configurations, as well as multistep LLM-based approaches. We further consider hybrid architectures that combine symbolic computation with LLM inferential processes, with particular emphasis on enhancing the robustness and faithfulness of intermediate representations and mitigating error propagation across successive stages of the computational pipeline. Our empirical results indicate that, although the baseline model delivers strong and reliable performance for narrowly defined error categories, structured multi-step approaches improve performance relative to single-step methods by achieving superior precision, F1 scores, and overall accuracy.

Multi-step Large Language Model for Fine-Grained Feedback in Stepwise Linear Equation Solutions

Russian grammar correction models can improve
on aggregate benchmarks
while getting worse at specific grammar rules.
We show this through per-rule evaluation
on a diagnostic benchmark of 48 prescriptive rules:
finetuning on synthetic data improves overall F$_{0.5}$
while driving subordinate-clause comma accuracy
from 14\% to 1\%.
The suppression is invisible under corpus-level metrics
and undetectable with existing coarse, corpus-specific tagsets;
it is recoverable only when diagnosed at rule granularity.
To enable this analysis,
we develop a 98-category error taxonomy
grounded in Rozental's reference grammar
and SyntErr, an open-source synthetic data generator
whose per-rule distribution is an explicit parameter,
designed to support arbitrary rule sets and languages.
Finetuning eight open models (0.8B--12B)
on 39K synthetic examples
yields up to 75.3 F$_{0.5}$,
approaching frontier API models
with models small enough to run on device.
We release the taxonomy, generator,
per-rule evaluation data, and all training artifacts.

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Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions

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