Austria

Establishing the difficulty of test items is an essential part of the language assessment development process. However, traditional item calibration methods are often time-consuming and difficult to scale. To address this, recent research has explored natural language processing (NLP) approaches for automatically predicting item difficulty from text. This paper investigates the use of transformer models to predict the difficulty of second language (L2) English vocabulary test items that have multilingual prompts. We introduce an extended versions of the British Council&#39;s Knowledge-based Vocabulary Lists (KVL), containing 6,768 English words paired with difficulty scores and question prompts written in Spanish, German, and Mandarin Chinese. Using this dataset for fine-tuning, we explore various transformer-based architectures. Our findings show that a multilingual model jointly trained on all L1 subsets of the KVL achieve the best results, with insights suggesting that the model is able to learn global patterns of cross-linguistic influence on target word difficulty. This study establishes a foundation for NLP-based item difficulty estimation using the KVL dataset, providing actionable insights for developing multilingual test items.

ACL 2025

Transformer Architectures for Vocabulary Test Item Difficulty Prediction

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

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

Establishing the difficulty of test items is an essential part of the language assessment development process. However, traditional item calibration methods are often time-consuming and difficult to scale. To address this, recent research has explored natural language processing (NLP) approaches for automatically predicting item difficulty from text. This paper investigates the use of transformer models to predict the difficulty of second language (L2) English vocabulary test items that have multilingual prompts. We introduce an extended versions of the British Council's Knowledge-based Vocabulary Lists (KVL), containing 6,768 English words paired with difficulty scores and question prompts written in Spanish, German, and Mandarin Chinese. Using this dataset for fine-tuning, we explore various transformer-based architectures. Our findings show that a multilingual model jointly trained on all L1 subsets of the KVL achieve the best results, with insights suggesting that the model is able to learn global patterns of cross-linguistic influence on target word difficulty. This study establishes a foundation for NLP-based item difficulty estimation using the KVL dataset, providing actionable insights for developing multilingual test items.

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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Heterogeneity in student populations poses a challenge in formal education, with adaptive textbooks offering a potential solution by taioring content based on individual learner models. However, creating domain models for textbooks typically demands significant manual effort. Recent work by Chau et al. (2021) demonstrated automated concept extraction from digital textbooks, but relied on costly domain-specific manual annotations. This paper introduces a novel, scalable method that minimizes manual effort by combining contextualized word embeddings with weakly supervised machine learning. Our approach clusters word embeddings from textbooks and identifies domain-specific concepts using a machine learner trained on concept seeds automatically extracted from Wikipedia. We evaluate this method using 28 economics textbooks, comparing its performance against a tf-idf baseline, a supervised machine learning baseline, the RAKE keyword extraction method, and human domain experts. Results demonstrate that our weakly supervised method effectively balances accuracy with reduced annotation effort, offering a practical solution for automated concept extraction in adaptive learning environments.

Automatic concept extraction for learning domain modeling: A weakly supervised approach using contextualized word embeddings

This paper presents an automatic speech assessment system designed for Swedish language learners. We introduce a novel hybrid approach that integrates Microsoft Azure speech services with open-source Large Language Models (LLMs). Our system is implemented as a web-based application that provides real-time quick assessment with a game-like experience. Through testing against COREFL English corpus data and Swedish L2 speech data, our system demonstrates effectiveness in distinguishing different language proficiencies, closely aligning with CEFR levels. This ongoing work addresses the gap in current low-resource language assessment technologies with a pilot system developed for automated speech analysis.

Towards a Real-time Swedish Speech Analyzer for Language Learning Games: A Hybrid AI Approach to Language Assessment

Evaluation of Grammatical Error Correction (GEC) systems is becoming increasingly challenging as the quality of such systems increases and traditional automatic metrics fail to adequately capture such nuances as fluency versus minimal edits, alternative valid corrections compared to the `ground truth', and the difference between corrections that are useful in a language learning scenario versus those preferred by native readers. Previous work has suggested using human post-editing of GEC system outputs, but this is very labor-intensive. We investigate the use of Large Language Models (LLMs) as post-editors of English and Swedish texts, and perform a meta-analysis of a range of different evaluation setups using a set of recent GEC systems. We find that for the two languages studied in our work, automatic evaluation based on post-editing agrees well with both human post-editing and direct human rating of GEC systems. Furthermore, we find that a simple n-gram overlap metric is sufficient to measure post-editing distance, and that including human references when prompting the LLMs generally does not improve agreement with human ratings. The resulting evaluation metric is reference-free and requires no language-specific training or additional resources beyond an LLM capable of handling the given language.
Evaluation of Grammatical Error Correction (GEC) systems is becoming increasingly challenging as the quality of such systems increases and traditional automatic metrics fail to adequately capture such nuances as fluency versus minimal edits, alternative valid corrections compared to the `ground truth', and the difference between corrections that are useful in a language learning scenario versus those preferred by native readers. Previous work has suggested using human post-editing of GEC system outputs, but this is very labor-intensive. We investigate the use of Large Language Models (LLMs) as post-editors of English and Swedish texts, and perform a meta-analysis of a range of different evaluation setups using a set of recent GEC systems. We find that for the two languages studied in our work, automatic evaluation based on post-editing agrees well with both human post-editing and direct human rating of GEC systems. Furthermore, we find that a simple n-gram overlap metric is sufficient to measure post-editing distance, and that including human references when prompting the LLMs generally does not improve agreement with human ratings. The resulting evaluation metric is reference-free and requires no language-specific training or additional resources beyond an LLM capable of handling the given language.

LLM-based post-editing as reference-free GEC evaluation

We present an approach to the automated scoring of a German Written Elicited Imitation Test, designed to assess literacy-dependent procedural knowledge in German as a foreign language. In this test, sentences are briefly displayed on a screen and, after a short pause, test-takers are asked to reproduce the sentence in writing as accurately as possible. Responses are rated on a 5-point ordinal scale, with grammatical errors typically penalized more heavily than lexical deviations. We compare a rule-based model that implements the categories of the scoring rubric through hand-crafted rules, and a deep learning model trained on pairs of stimulus sentences and written responses. Both models achieve promising performance with quadratically weighted kappa (QWK) values around .87. However, their strengths differ – the rule-based model performs better on previously unseen stimulus sentences and at the extremes of the rating scale, while the deep learning model shows advantages in scoring mid-range responses, for which explicit rules are harder to define.

Automated Scoring of a German Written Elicited Imitation Test

Knowing how test takers answer items in educational assessments is essential for test development, to evaluate item quality, and to improve test validity. However, this process usually requires extensive pilot studies with human participants. If large language models (LLMs) exhibit human-like response behavior to test items, this could open up the possibility of using them as pilot participants to accelerate test development. In this paper, we evaluate the human-likeness or psychometric plausibility of responses from 18 instruction-tuned LLMs with two publicly available datasets of multiple-choice test items across three subjects: reading, U.S. history, and economics. Our methodology builds on two theoretical frameworks from psychometrics which are commonly used in educational assessment, classical test theory and item response theory. The results show that while larger models are excessively confident, their response distributions can be more human-like when calibrated with temperature scaling. In addition, we find that LLMs tend to correlate better with humans in reading comprehension items compared to other subjects. However, the correlations are not very strong overall, indicating that LLMs should not be used for piloting educational assessments in a zero-shot setting.

Do LLMs Give Psychometrically Plausible Responses in Educational Assessments?

Generative AI systems have rapidly advanced, with multimodal input capabilities enabling reasoning beyond text-based tasks. In education, these advancements could influence assessment design and question answering, presenting both opportunities and challenges. To investigate these effects, we introduce a high-quality dataset of 201 university-level STEM questions, manually annotated with features such as image type, role, problem complexity, and question format. Our study analyzes how these features affect generative AI performance compared to students. We evaluate four model families with five prompting strategies, comparing results to the average of 546 student responses per question. Although the best model correctly answers on average 58.5% of the questions using majority vote aggregation, human participants consistently outperform AI on questions involving visual components. Interestingly, human performance remains stable across question features but varies by subject, whereas AI performance is susceptible to both subject matter and question features. Finally, we provide actionable insights for educators, demonstrating how question design can enhance academic integrity by leveraging features that challenge current AI systems without increasing the cognitive burden for students

Challenges for AI in Multimodal STEM Assessments: a Human-AI Comparison

Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approaches are limited in ensuring that the generated distractors are consistent with common student errors. We propose LookAlike, a method that improves error–distractor consistency via preference optimization. Our two main innovations are: (a)~mining synthetic preference pairs from model inconsistencies, and (b)~alternating supervised fine-tuning (SFT) with Direct Preference Optimization (DPO) to stabilize training. Unlike prior work that relies on heuristics or manually annotated preference data, LookAlike uses its own generation inconsistencies as dispreferred samples, thus enabling scalable and stable training. Evaluated on a real-world dataset of 1,400+ math MCQs, LookAlike achieves 51.6% accuracy in distractor generation and 57.2% in error generation under LLM-as-a-judge evaluation, outperforming an existing state-of-the-art method (45.6% / 47.7%). These improvements highlight the effectiveness of preference-based regularization and inconsistency mining for generating consistent math MCQ distractors at scale.

LookAlike: Consistent Distractor Generation in Math MCQs

Designing vocabulary learning activities for foreign/second language (L2) learners highly depends on the successful identification of difficult words. In this paper, we present a novel personalised word difficulty classifier for L2 Spanish, using the LexComSpaL2 corpus as training data and a BiLSTM model as the architecture. We train a base version (using the original LexComSpaL2 data) and a word family version of the classifier (adding word family knowledge as an extra feature). The base version obtains reasonably good performance (F1 = 0.53) and shows weak positive predictive power (ϕ = 0.32), underlining the potential of automated methods in determining vocabulary difficulty for individual L2 learners. The "word family classifier" is able to further push performance (F1 = 0.62 and ϕ = 0.45), highlighting the value of well-chosen linguistic features in developing word difficulty classifiers.

You Shall Know a Word's Difficulty by the Family It Keeps: Word Family Features in Personalised Word Difficulty Classifiers for L2 Spanish

Recent trends in NLP have shifted towards modeling lexical complexity as a continuous value, but practical implementations often remain binary. This opinion piece argues for the importance of truly graded lexical complexity prediction, particularly in language learning. We examine the evolution of lexical complexity modeling, highlighting the ``data bottleneck'' as a key obstacle. Overcoming this challenge can lead to significant benefits, such as enhanced personalization in language learning and improved text simplification. We call for a concerted effort from the research community to create high-quality, graded complexity datasets and to develop methods that fully leverage continuous complexity modeling, while addressing ethical considerations. By fully embracing the continuous nature of lexical complexity, we can develop more effective, inclusive, and personalized language technologies.

The Need for Truly Graded Lexical Complexity Prediction

This paper introduces IPPOLIS Write, an open source, web-based tool designed to provide automated feedback on the formal aspects of scientific documents. Aimed at addressing the variability in writing and language skills among scientists and the challenges faced by supervisors in providing consistent feedback on student theses, IPPOLIS Write integrates several open source tools and custom implementations to analyze documents for a range of formal issues, including grammatical errors, consistent introduction of acronyms, comparison of literature entries with several databases, referential integrity of figures and tables, and consistent link access dates.

IPPOLIS Write generates reports with statistical summaries and annotated documents that highlight specific issues and suggest improvements while also providing additional background information where appropriate. To evaluate its effectiveness, a qualitative assessment is conducted using a small but diverse dataset of bachelor's and master's theses sourced from arXiv. Our findings demonstrate the tool's potential to enhance the quality of scientific documents by providing targeted and consistent feedback, thereby aiding both students and professionals in refining their document preparation skills.

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Automatic concept extraction for learning domain modeling: A weakly supervised approach using contextualized word embeddings

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