Austria

We release LORuGEC -- the first rule-annotated corpus for Russian Grammatical Error Correction. The corpus is designed for diagnostic purposes and contains 348 validation and 612 test sentences specially selected to represent complex rules of Russian writing. This makes our corpus significantly different from other Russian GEC corpora. We apply several large language models and approaches to our corpus, the best F0.5 score of 83% is achieved by 5-shot learning using YandexGPT-5 Pro model.

To move further the boundaries of few-shot learning, we are the first to apply a GECTOR-like encoder model for similar examples retrieval. GECTOR-based example selection significantly boosts few-shot performance. This result is true not only for LORuGEC but for other Russian GEC corpora as well. On LORuGEC, the GECTOR-based retriever might be further improved using contrastive tuning on the task of rule label prediction. All these results hold for a broad class of large language models.

ACL 2025

LLMs in alliance with Edit-based models: advancing In-Context Learning for Grammatical Error Correction by Specific Example Selection

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workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Comparative judgment (CJ) is an assessment method in which multiple assessors determine the holistic quality of essays through pairwise comparisons.
While CJ is recognized for generating reliable and valid scores, it falls short in providing transparency about the specific quality aspects these holistic scores represent.
Our study addresses this limitation by predicting scores on a set of rubrics that measure text quality, thereby explaining the holistic scores derived from CJ.
We developed feature-based machine learning models that leveraged complexity and genre features extracted from a collection of Dutch essays.
We evaluated the predictability of rubric scores for text quality based on linguistic features.
Subsequently, we evaluated the validity of the predicted rubric scores by examining their ability to explain the holistic scores derived from CJ.
Our findings indicate that feature-based prediction models can predict relevant rubric scores moderately well. Furthermore, the predictions can be used to explain holistic scores from CJ, despite certain biases. This automated approach to explain holistic quality scores from CJ can enhance the transparency of CJ assessments and simplify the evaluation of their validity.

Explaining Holistic Essay Scores in Comparative Judgment Assessments by Predicting Scores on Rubrics

Automated Essay Scoring (AES) plays a crucial role in assessing language learners' writing quality, reducing grading workload, and providing real-time feedback. The lack of annotated essay datasets inhibits the development of Arabic AES systems. This paper leverages Large Language Models (LLMs) and Transformer models to generate synthetic Arabic essays for AES. We prompt an LLM to generate essays across the Common European Framework of Reference (CEFR) proficiency levels and introduce and compare two approaches to error injection. We create a dataset of 3,040 annotated essays with errors injected using our two methods. Additionally, we develop a BERTbased Arabic AES system calibrated to CEFR levels. Our experimental results demonstrate the effectiveness of our synthetic dataset in improving Arabic AES performance. We make our code and data publicly available

Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection

Interviews are central to the Empathy phase of Design Thinking, helping designers uncover user needs and experience. Although interviews are widely used to support human centered innovation, evaluating their quality, especially from a cognitive perspective, remains underexplored. This study introduces a structured framework for evaluating interview quality in the context of Design Thinking, using Bloom's Taxonomy as a foundation. We propose the Cognitive Interview Quality Score, a composite metric that integrates three dimensions: Effectiveness Score, Bloom Coverage Score, and Distribution Balance Score. Using human-annotations, we assessed 15 interviews across three domains to measure cognitive diversity and structure. We compared CIQS-based rankings with human experts and found that the Bloom Coverage Score aligned more closely with expert judgments. We evaluated the performance of LMA-3-8B-Instruct and GPT-4o-mini, using zero-shot, few-shot, and chain-of-thought prompting, finding GPT-4o-mini, especially in zero-shot mode, showed the highest correlation with human annotations in all domains. Error analysis revealed that models struggled more with mid-level cognitive tasks (e.g., Apply, Analyze) and performed better on Create, likely due to clearer linguistic cues. These findings highlight both the promise and limitations of using NLP models for automated cognitive classification and underscore the importance of combining cognitive metrics with qualitative insights to comprehensively assess interview quality.

Analyzing Interview Questions via Bloom's Taxonomy to Enhance the Design Thinking Process

Large language models (LLMs) are increasingly being adopted in educational settings. These applications expand beyond English, though current LLMs remain primarily English-centric. In this work, we ascertain if their use in education settings in non-English languages is warranted. We evaluated the performance of popular LLMs on four educational tasks: identifying student misconceptions, providing targeted feedback, interactive tutoring, and grading translations in eight languages (Mandarin, Hindi, Arabic, German, Farsi, Telugu, Ukrainian, Czech) in addition to English. We find that the performance on these tasks somewhat corresponds to the amount of language represented in training data, with lower-resource languages having poorer task performance. However, at least some models are able to more or less maintain their levels of performance across all languages. Thus, we recommend that practitioners first verify that the LLM works well in the target language for their educational task before deployment.

Are Large Language Models for Education Reliable Across Languages?

Vocabulary use is a fundamental aspect of second language (L2) proficiency. To date, its assessment by automated systems has typically examined the context-independent, or part-of-speech (PoS) related use of words. This paper introduces a novel approach to enable fine-grained vocabulary evaluation exploiting the precise use of words within a sentence. The scheme combines large language models (LLMs) with the English Vocabulary Profile (EVP). The EVP is a standard lexical resource that enables in-context vocabulary use to be linked with proficiency level. We evaluate the ability of LLMs to assign proficiency levels to individual words as they appear in L2 learner writing, addressing key challenges such as polysemy, contextual variation, and multi-word expressions. We compare LLMs to a PoS-based baseline. LLMs appear to exploit additional semantic information that yields improved performance.
We also explore correlations between word-level proficiency and essay-level proficiency. Finally, the approach is applied to examine the consistency of the EVP proficiency levels. Results show that LLMs are well-suited for the task of vocabulary assessment.

Exploiting the English Vocabulary Profile for L2 word-level vocabulary assessment with LLMs

Question Generation (QG), the task of automatically generating questions from a source input, has seen significant progress in recent years. Difficulty-controllable QG (DCQG) enables control over the difficulty level of generated questions while considering the learner's ability. Additionally, narrative-controllable QG (NCQG) allows control over the narrative aspects embedded in the questions. However, research in QG lacks a focus on combining these two types of control, which is important for generating questions tailored to educational purposes. To address this gap, we propose a strategy for Joint Narrative and Difficulty Control, enabling simultaneous control over these two attributes in the generation of reading comprehension questions. Our evaluation provides preliminary evidence that this approach is feasible, though it is not effective across all instances. Our findings highlight the conditions under which the strategy performs well and discuss the trade-offs associated with its application.

Advancing Question Generation with Joint Narrative and Difficulty Control

Advances in Large Language Models (LLMs) have sparked growing interest in their potential as explainable text evaluators. While LLMs have shown promise in assessing machine-generated texts in tasks such as summarization and machine translation, their effectiveness in evaluating human-written content—such as student writing in classroom settings—remains underexplored. In this paper, we investigate LLM-based specificity assessment of student reflections written in response to prompts, using three instruction-tuned models. Our findings indicate that although LLMs may underperform compared to simpler supervised baselines in terms of scoring accuracy, they offer a valuable interpretability advantage. Specifically, LLMs can generate user-friendly explanations that enhance the transparency and usability of automated specificity scoring systems.

Lessons Learned in Assessing Student Reflections with LLMs

Identifying instances of first language (L1) transfer -- the application of the linguistics structures of a speaker's first language to their second language(s) -- can facilitate second language (L2) learning as it can inform learning and teaching resources, especially when instances of negative transfer (that is, interference) can be identified. While studies of transfer between two languages A and B require a priori linguistic structures to be analyzed with three datasets (data from L1 speakers of language A, L1 speakers of language B, and L2 speakers of A or B), native language identification (NLI) -- a machine learning task to predict one's L1 based on one's L2 production -- has the advantage to detect instances of subtle and unpredicted transfer, casting a "wide net" to capture patterns of transfer that were missed before (Jarvis and Crossley, 2018). This study aims to apply NLI tasks to find potential instances of transfer of collocations. Our results, compared to previous transfer studies, indicate that NLI can be used to reveal collocation transfer, also in understudied L2 languages.

Using NLI to Identify Potential Collocation Transfer in L2 English

Across cultures, names tell a lot about their bearers as they carry deep personal, historical, and cultural significance. Names have also been found to serve as powerful signals of gender, race, and status in the social hierarchy--a pecking order in which individual positions shape others' expectations on their perceived competence and worth (Podolny, 2005). With the widespread adoption of Large Language Models (LLMs) in education and given that names are often an input for LLMs, it is crucial to evaluate whether LLMs may sort students into status positions based on first and last names and, if so, whether it is in an unfair, biased fashion. While prior work has primarily investigated biases in first names, little attention has been paid to last names and even less to the combined effects of first and last names. In this study, we conduct a large-scale analysis with bootstrap standard errors of 45,000 name variations across 5 ethnicities to examine how AI-generated responses exhibit systemic name biases. Our study investigates three key characteristics of inequality and finds that LLMs reflect, construct, and reinforce status hierarchies based on names that signal gender and ethnicity as they encode differential expectations of competence, leadership, and economic potential. Contrary to the common assumption that AI tends to favor Whites, we show that East and, in some contexts, South Asian names receive higher rankings. We also disaggregate Asians, a population projected to be the largest immigrant group in the U.S. by 2055. Our results challenge the monolithic Asian model minority assumption, illustrating a more complex and stratified model of bias. Additionally, spanning cultural categories by adopting Western first names improves AI-perceived status for East and Southeast Asian students, particularly for girls. Our findings underscore the importance of intersectional and more nuanced understandings of race, gender, and mixed identities in the evaluation of LLMs, rather than relying on broad, monolithic, and mutually exclusive categories. By examining LLM bias and discrimination in our multicultural contexts, our study illustrates potential harms of using LLMs in education as they do not merely reflect implicit biases but also actively construct new social hierarchies that can unfairly shape long-term life trajectories. An LLM that systematically assigns lower grades or subtly less favorable evaluations to students with certain name signals reinforces a tiered system of privilege and opportunity. Some groups may face structural disadvantages, while others encounter undue pressure from inflated expectations.

Name of Thrones: How Do LLMs Rank Student Names in Status Hierarchies Based on Race and Gender?

This study investigates the use of ChatGPT for Automated Essay Scoring (AES) in assessing Italian middle school students' written texts. Using rubrics targeting grammar, coherence and argumentation, we compare AI-generated feedback with that of a human teacher on a newly collected corpus of students' essays. Despite some differences, ChatGPT provided detailed and timely feedback that complements the teacher's role. These findings underscore the potential of generative AI to improve the assessment of writing, providing useful insights for educators and supporting students in developing their writing skills.

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Explaining Holistic Essay Scores in Comparative Judgment Assessments by Predicting Scores on Rubrics

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Explaining Holistic Essay Scores in Comparative Judgment Assessments by Predicting Scores on Rubrics

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Improved grammatical error correction by ranking elementary edits

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