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With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become increasingly prominent. Prior works on LLM-based tutoring typically learn a single tutor policy and do not capture the diversity of tutoring styles. In real-world tutor–student interactions, pedagogical intent is realized through adaptive instructional strategies, with tutors varying the level of scaffolding, instructional directiveness, feedback, and affective support in response to learners&#39; needs. These differences can all impact dialogue dynamics and student engagement. In this paper, we explore how tutor personas embedded in human tutor-student dialogues can be used to guide LLM behavior without relying on explicitly prompted instructions. We modify Bidirectional Preference Optimization (BiPO) to learn a steering vector, an activation-space direction that steers model responses towards certain tutor personas. We find that this steering vector captures tutor-specific variation across dialogue contexts, improving semantic alignment with ground-truth tutor utterances and increasing preference-based evaluations, while largely preserving lexical similarity. Analysis of the learned directional coefficients further reveals interpretable structure across tutors, corresponding to consistent differences in tutoring behavior. These results demonstrate that activation steering offers an effective and interpretable way for controlling tutor-specific variation in LLMs using signals derived directly from human dialogue data.

ACL 2026

Letting Tutor Personas Speak Up for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization

With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become increasingly prominent. Prior works on LLM-based tutoring typically learn a single tutor policy and do not capture the diversity of tutoring styles. In real-world tutor–student interactions, pedagogical intent is realized through adaptive instructional strategies, with tutors varying the level of scaffolding, instructional directiveness, feedback, and affective support in response to learners' needs. These differences can all impact dialogue dynamics and student engagement. In this paper, we explore how tutor personas embedded in human tutor-student dialogues can be used to guide LLM behavior without relying on explicitly prompted instructions. We modify Bidirectional Preference Optimization (BiPO) to learn a steering vector, an activation-space direction that steers model responses towards certain tutor personas. We find that this steering vector captures tutor-specific variation across dialogue contexts, improving semantic alignment with ground-truth tutor utterances and increasing preference-based evaluations, while largely preserving lexical similarity. Analysis of the learned directional coefficients further reveals interpretable structure across tutors, corresponding to consistent differences in tutoring behavior. These results demonstrate that activation steering offers an effective and interpretable way for controlling tutor-specific variation in LLMs using signals derived directly from human dialogue data.

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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Large language models have strong potential for use in intelligent tutoring systems, but they often fail to follow effective pedagogical strategies, such as guiding students without revealing final answers. We propose a two-stage alignment pipeline for math mistake remediation, combining supervised fine-tuning on tutoring dialogs with Direct Preference Optimization on synthetic preference pairs. We construct a dataset that integrates existing tutoring corpora with synthetic data generated along pedagogical dimensions, such as scaffolding and factuality, and study different input configurations that incorporate solution correctness and gold answers. Experiments show that our approach improves both factual accuracy and pedagogical quality over base models and existing tutoring models. Human evaluation further indicates that our best model is competitive with a strong proprietary baseline, while providing additional benefits in terms of openness, transparency, and reproducibility. Our results highlight the effectiveness of preference-based pedagogical alignment, while also revealing challenges in reliably evaluating tutoring quality.

Towards Pedagogically Aligned LLM Tutors for Math Mistake Remediation

Intelligent Tutoring Systems (ITS) possess the ability to record learner interactions in fine-grained detail at scale. This opens the door to data-driven methods for investigating system performance and identifying points for improvement. 
In this paper, we draw on authentic log data from an English language ITS (N_logs = 5646, N_students = 368) to investigate the performance of its feedback algorithm.
In step 1 of our analysis, we profiled feedback accuracy by exploring how well the system provided error-specific feedback to malformed student answers in gap-filling grammar exercises using an expert-created set of feedback generation rules. We then identified frequently occurring student errors that triggered incorrect or unspecific feedback and refined the rule set to detect and respond to these errors with correct specific feedback.
In step 2, we validated the rule modifications on an unseen dataset. Comparing the performance of the initial and updated rule sets, we find significant improvement that generalizes to unseen data. Our study thus illustrates how an empirical evaluation of authentic data can complement feedback creators' expertise by informing rule refinement decisions that yield significant and generalizable performance improvements to feedback in ITS systems.

Using Interaction Log Data to Evaluate and Improve Feedback Accuracy in an Intelligent Language Tutoring System

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.

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

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.

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