United States

Large Language Models (LLMs) are widely applied in educational practices, such as for generating children&#39;s stories. However, the generated stories are often too difficult for children to read, and the operational cost of LLMs hinders their widespread adoption in educational settings. We used an existing expert-designed children&#39;s reading curriculum and its corresponding generated stories from GPT-4o and Llama 3.3 70B to design different experiments for fine-tuning three 8B-parameter LLMs, which then generated new English reading stories that were subjected to quantitative and qualitative evaluation. Our method prioritizes controllability over scale, enabling educators to target reading levels and error patterns with a compact, affordable model. Our evaluation results show that with appropriate fine-tuning designs, children&#39;s English reading stories generated by 8B LLMs perform better on difficulty-related metrics than those from GPT-4o and Llama 3.3 70B, with almost no discernible safety issues. Such fine-tuned LLMs could be more broadly used by teachers, parents, and children in classrooms and at home to generate engaging English reading stories with children&#39;s interests, controllable difficulty and safety.

ACL 2026

Children&#39;s English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

llms

story

education

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories are often too difficult for children to read, and the operational cost of LLMs hinders their widespread adoption in educational settings. We used an existing expert-designed children's reading curriculum and its corresponding generated stories from GPT-4o and Llama 3.3 70B to design different experiments for fine-tuning three 8B-parameter LLMs, which then generated new English reading stories that were subjected to quantitative and qualitative evaluation. Our method prioritizes controllability over scale, enabling educators to target reading levels and error patterns with a compact, affordable model. Our evaluation results show that with appropriate fine-tuning designs, children's English reading stories generated by 8B LLMs perform better on difficulty-related metrics than those from GPT-4o and Llama 3.3 70B, with almost no discernible safety issues. Such fine-tuned LLMs could be more broadly used by teachers, parents, and children in classrooms and at home to generate engaging English reading stories with children's interests, controllable difficulty and safety.

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

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. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Automatic readability assessment for Arabic remains under-explored compared to English, and existing models are typically evaluated only within the training domain. We introduce the Jordanian School Textbook Corpus (JSTC), comprising approximately 87,000 text segments extracted from 240 official textbooks spanning grades 1–12, annotated with 127 handcrafted linguistic features. We compare XGBoost models trained on linguistic features, fine-tuned CAMeLBERT transformers, and hybrid architectures under both in-domain and cross-domain evaluation, using the BAREC benchmark for out-of-domain testing. While CAMeLBERT achieves strong in-domain performance (QWK = 0.830), its cross-domain QWK drops to 0.085—a 90% relative decrease. In contrast, XGBoost with linguistic features alone maintains the highest cross-domain QWK (0.240), outperforming all transformer and hybrid configurations. Notably, adding contextual [CLS] embeddings to linguistic features actively harms cross-domain transfer. These findings suggest that domain-robust readability models for educational deployment should prioritize linguistically-grounded features over contextual embeddings.

Transformer-based readability classifiers are worse than you think: Evidence from cross-domain Arabic readability assessment

Prediction of item difficulty from its text content is of substantial interest for automated generation of test items. In this paper, we focus on the related problem of recovering IRT-based difficulty when the data originally reported item p-value (percent correct responses). We model this item difficulty using a repository of reading passages and student data from US standardized tests from New York and Texas for grades 3-8 spanning the years 2018-23. This repository is annotated with meta-data on (1) linguistic features of the reading items, (2) test features of the passage, and (3) context features. Using a penalized regression model, we achieve an RMSE of 0.59 (compared to a 0.92 baseline) and a 0.77 correlation between true and predicted difficulty. We further evaluated the impact of LLM embeddings (ModernBERT, BERT, and LLaMA), finding that they marginally improve performance but function effectively as standalone alternatives to traditional linguistic features. Finally, we demonstrate how this difficulty prediction model powers a publicly available, human-in-the-loop tool for generating reading comprehension items.

Predicting Item Difficulty and Generating Reading Comprehension Items via an Annotated Repository

When the same LLM generates assessment items, simulates student responses, and scores them, the validation loop is self-referential. We introduce \textbf{Generative-Evaluative Agreement (GEA)}, a validity criterion measuring whether an LLM's scoring function recovers the skill levels its generative function was instructed to produce. In the first direct measurement of GEA on a two-stage adaptive assessment, the model recovers roughly half the intended variance ($r = 0.698$) with systematic positive bias. GEA is strong ($r > 0.7$) for syntactically verifiable skills but near zero for design-level skills, and low-skill overestimation inflates scores near the routing threshold. We show that granular, skill-decomposed rubrics are the primary mechanism for strengthening GEA and outline complementary mitigations.

Generative-Evaluative Agreement: A Necessary Validity Criterion for LLM-Enabled Adaptive Assessment

Grammatical error correction approaches rarely characterize the pedagogical value of corrected errors. We propose a framework that combines LLM-based second-language writing correction with a rule-based characterization module to identify pedagogically relevant, fine-grained grammatical properties in learner texts. The characterization module targets 252 European Portuguese properties, categorized by the CEFR level at which they are taught according to an authoritative curriculum, and property accuracy is inferred from contrasts between the learner and corrected texts. We evaluate the framework extrinsically by training interpretable automatic proficiency assessment models on accuracy features extracted from characterized errors in a Portuguese learner corpus. Across prompting and alignment strategies, we show that models trained on features derived from LLM-corrected texts perform similarly to those trained on features derived from annotator-corrected texts and comparably to models trained on linguistic complexity features. Feature importance overlap is likewise high, and similar predictive patterns are observed in both annotator-based and LLM-based models, further supporting the validity of the proposed framework.

LLM-Powered but Rule-Grounded: Pedagogically Relevant Grammatical Error Characterization for Learner Model Construction

We present a modular pipeline for educational analogy generation, decomposed into four stages -- source finding, sub-concept generation, explanation generation, and evaluation -- grounded in Structure Mapping Theory. Evaluating 12 LLMs across six model families on SCAR and ParallelPARC, we find that sub-concept grounding substantially improves retrieval precision and explanation quality but offers limited benefit in open-ended generation. We further validate an LLM-as-a-judge methodology against human annotations, finding that Claude Sonnet 4.6 aligns more reliably with human rankings than with absolute scores. Our results highlight cross-stage interactions that isolated studies cannot capture, and position sub-concept grounding as a key driver of analogy quality.

Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation

Adaptive language-learning systems often rely on correctness in constrained exercise formats, but dialogue-based tutors require richer evidence derived from learners' own language use. This raises a learner-modeling problem: which aspects of learner production in dialogue can serve as useful signals for adaptive progression? We address this question using pilot data from an LLM-based English grammar tutor, comprising 40 pre/post tasks, treatment interactions, and 2,406 learner messages. We report an exploratory analysis of candidate productive-use signals for future adaptive decisions. Results suggest that different adaptive goals require different evidence: short-term prediction of independent target use can be supported by a simple trace of prior target use, whereas broader productive control is more strongly associated with lexical diversity within the construction. Finer-grained typed evidence adds relatively little predictive value, but is relevant for interpreting how different forms of learner behavior relate to later target use. We discuss implications for adaptive dialogue systems that aim to move beyond fixed sequencing.

Tracing Evidence of Productive Use for Learner Modeling in an LLM-Based Grammar Tutor

This paper presents team SAAKTH's system for the BEA 2026 Shared Task on Vocabulary Difficulty Prediction (Closed Track). We address the key challenge that English word difficulty is not fixed but varies with English learners' native language. Our approach combines a fine-tuned XLM-RoBERTa-large encoder with handcrafted psycholinguistic features engineered separately for each L1 group. These features are integrated via a shallow multilayer perceptron and optimized separately per L1, with five-seed ensembling and XGBoost-based blending for stability. Our system achieves RMSEs of 0.997 (es), 1.002 (de), and 0.932 (cn) on the development set, improving 20–25% over the baseline. Results highlight the effectiveness of L1-aware modeling under limited data.

L1-Aware English Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features

Open-source LLMs with simple, zero-shot prompts are at best middling graders on the BEA 2026 Automated Grading Shared Task -- blunt-edge models, in fact. However, they are good enough to support human graders and save them time. We demonstrate the application of a hybrid grading approach that first transparently defines the success criteria and then pairs a zero-shot LLM grader with human review. The hybrid approach outperforms the LLM grader on its own and has the added advantage of keeping the human in the loop.

HFT at BEA 2026 Shared Task 2: Blunt-Edge Models for Hybrid Grading

This system description paper presents a submission to the BEA 2026 Shared Task on Rubric-based Short Answer Scoring for German. It describes a multi-strategy scoring pipeline combining rubric-conditioned prompting, LoRA fine-tuning of Qwen-family models, and prediction aggregation. The system ranks second on three official tracks and third on the remaining track.

Multi-Strategy Rubric-Based Short Answer Scoring for German

This paper describes our submission to the BEA 2026 shared task on vocabulary item difficulty prediction in multilingual settings. We investigated whether transformer-based representations learned directly from item content can support the prediction of vocabulary item difficulty across different L1 groups. Our approach adopted a multilingual BERT-based architecture, specifically the mmBERT, with representation augmentation at both the layer and token levels, followed by a multi-task cascade learning that incorporates part-of-speech information as an auxiliary structural signal. Results showed that multi-task mmBERT consistently outperforms the shared-task XLM-RoBERTa baseline across languages, while gains from more complex aggregation are not uniform. The findings showed that strong multilingual representations provide a competitive foundation for vocabulary item difficulty prediction, while the benefits of additional architectural complexity depend on the language and training setting.

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Transformer-based readability classifiers are worse than you think: Evidence from cross-domain Arabic readability assessment

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