Austria

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.

ACL 2025

Automated Scoring of a German Written Elicited Imitation Test

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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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.

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)

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.

Towards Automatic Formal Feedback on Scientific Documents

The rapid development of Large Language Models (LLMs) opens up the possibility of using them aspersonal tutors. This has led to the development of several intelligent tutoring systems and learning assistants that use LLMs as back-ends with various degrees of engineering. In this study, we seek to compare human tutors with LLM tutorsin terms of engagement, empathy, scaffolding, and conciseness. We ask human tutors to compare the performance of an LLM tutor with that of a human tutor in teaching grade-school math word problems on these qualities. We find that annotators with teaching experience perceive LLMs as showing higher performance than human tutors in all 4 metrics. The biggest advantage is in empathy, where 80% of our annotators prefer the LLM tutor more often than the human tutors. Our study paints a positive picture of LLMs as tutors and indicates that these models can be used to reduce the load on human teachers in the future.

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting

Real-word spelling errors (RWSEs) pose special challenges for detection methods, as they ‘hide' in the form of another existing word and in many cases even fit in syntactically. We present a modern Transformer-based implementation of earlier probabilistic methods based on confusion sets and show that RWSEs can be detected with a good balance between missing errors and raising too many false
alarms. The confusion sets are dynamically configurable, allowing teachers to easily adjust which errors trigger feedback.

Transformer-Based Real-Word Spelling Error Feedback with Configurable Confusion Sets

Weakly supervised learning (WSL) is a machine learning approach used when labeled data is scarce or expensive to obtain. In such scenarios, models are trained using weaker supervision sources instead of human-annotated data. However, these sources are often noisy and may introduce unquantified biases during training. This issue is particularly pronounced in automated scoring (AS) of second language (L2) learner output, where high variability and limited generalizability pose significant challenges.
In this paper, we investigate analytical scoring of L2 learner responses under weak and semi-supervised learning conditions, leveraging Prediction-Powered Inference (PPI) to provide statistical guarantees on score validity. We compare two approaches: (1) synthetic scoring using large language models (LLMs), and (2) a semi-supervised setting in which a machine learning model, trained on a small gold-standard set, generates predictions for a larger unlabeled corpus. In both cases, PPI is applied to construct valid confidence intervals for assessing the reliability of the predicted scores.
Our analysis, based on a dataset of L2 learner conversations with an AI agent, shows that PPI is highly informative for evaluating the quality of weakly annotated data. Moreover, we demonstrate that PPI can increase the effective sample size by over 150% relative to the original human-scored subset, enabling more robust inference in educational assessment settings where labeled data is scarce.

Automated L2 Proficiency Scoring: Weak Supervision, Large Language Models, and Statistical Guarantees

Inference making is an essential but complex skill in reading comprehension (RC). Some inferences require resolving references across sentences, and some rely on using prior knowledge to fill in the detail that is not explicitly written in the text. Diagnostic RC questions can help educators provide more effective and targeted reading instruction and interventions for school-age students. We introduce a taxonomy of inference types for RC and use it to analyze the distribution of items within a diagnostic RC item bank. Next, we present experiments using GPT-4o to generate bridging-inference RC items for given reading passages via few-shot prompting, comparing conditions with and without chain-of-thought prompts. Generated items were evaluated on three aspects: overall item quality, appropriate inference type, and LLM reasoning, achieving high inter-rater agreements above 0.90. Our results show that GPT-4o produced 93.8% good-quality questions suitable for operational use in grade 3-12 contexts; however, only 42.6% of the generated questions accurately matched the targeted inference type. We conclude that combining automatic item generation with human judgment offers a promising path toward scalable, high-quality diagnostic RC assessments.

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Do LLMs Give Psychometrically Plausible Responses in Educational Assessments?

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

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