Austria

Correct verb placement is difficult to acquire for second-language learners of Germanic languages. However, word order errors and, consequently, verb placement errors, are heavily underrepresented in benchmark datasets of NLP tasks such as grammatical error detection/correction and linguistic acceptability assessment. If they are present, they are most often naively introduced, or classification occurs at the sentence level, preventing the precise identification of individual errors and the provision of appropriate feedback to learners. To remedy this, we present GermDetect: Universal Dependencies-based, linguistically informed verb placement error detection datasets for learners of Germanic languages, designed as a token classification task. As our datasets are UD-based, we are able to provide them in most major Germanic languages: Afrikaans, German, Dutch, Faroese, Icelandic, Danish, Norwegian (Bokmål and Nynorsk), and Swedish.

We train multilingual BERT models on GermDetect and show that linguistically informed, UD-based error induction results in more effective models for verb placement error detection than models trained on naively introduced errors. Finally, we conduct ablation studies on multilingual training and find that lower-resource languages benefit from the inclusion of structurally related languages in training.

ACL 2025

GermDetect: Verb Placement Error Detection Datasets for Learners of Germanic Languages

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)

This study examines vulnerabilities in transformer-based automated short-answer grading systems used in medical education, with a focus on how these systems can be manipulated through adversarial gaming strategies. Our research identifies three main types of gaming strategies that exploit the system's weaknesses, potentially leading to false positives. To counteract these vulnerabilities, we implement several adversarial training methods designed to enhance the system's robustness. Our results indicate that these methods significantly reduce the susceptibility of grading systems to such manipulations, especially when combined with ensemble techniques like majority voting and Ridge regression, which further improve the system's defense against sophisticated adversarial inputs. Additionally, employing large language models suchasGPT-4with varied prompting techniques has shown promise in recognizing and scoring gaming strategies effectively. The findings underscore the importance of continuous improvements in AI-driven educational tools to ensure their reliability and fairness in high-stakes settings.

Enhancing Security and Strengthening Defenses in Automated Short-Answer Grading Systems

Recent advances in LLMs offer new opportunities for supporting student writing, particularly through real-time, composition-level feedback. However, for such support to be effective, LLMs need to generate text completions that align with the writer's internal representation of their developing message, a representation that is often implicit and difficult to observe. This paper investigates the use of eye-tracking data, specifically lookback fixations during pauses in text production, as a cue to this internal representation. Using eye movement data from students composing texts, we compare human-generated completions with LLM-generated completions based on prompts that either include or exclude words and sentences fixated during pauses. We find that incorporating lookback fixations enhances human-LLM alignment in generating text completions. These results provide empirical support for generating fixation-aware LLM feedback and lay the foundation for future educational tools that deliver real-time, composition-level feedback grounded in writers' attention and cognitive processes.

EyeLLM: Using Lookback Fixations to Enhance Human-LLM Alignment for Text Completion

We present a method for labeling spans of text with large language models (LLMs) and apply it to the task of identifying shell language, language which plays a structural or connective role without constituting the main content of a text. We compare several recent LLMs by evaluating their "annotations" against a small human-curated test set, and train a smaller supervised model on thousands of LLM-annotated examples. The described method enables workflows that can learn complex or nuanced linguistic phenomena without tedious, large-scale hand-annotations of training data or specialized feature engineering.

Span Labeling with Large Language Models: Shell vs. Meat

Large language models (LLMs) hold great promise for educational applications, particularly in intelligent tutoring systems. However, effective tutoring requires alignment with pedagogical strategies – something current LLMs lack without task-specific adaptation. In this work, we explore whether fine-grained annotation of teacher intents can improve the quality of LLM-generated tutoring responses. We focus on MathDial, a dialog dataset for math instruction, and apply an automated annotation framework to re-annotate a portion of the dataset using a detailed taxonomy of eleven pedagogical intents. We then fine-tune an LLM using these new annotations and compare its performance to models trained on the original four-category taxonomy. Both automatic and qualitative evaluations show that the fine-grained model produces more pedagogically aligned and effective responses. Our findings highlight the value of intent specificity for controlled text generation in educational settings, and we release our annotated data and code to facilitate further research.

Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation

Large Language Models (LLMs) offer exciting potential as educational tutors, and much research explores this potential. Unfortunately, there's little research in understanding the baseline behavioral pattern differences that LLM tutors exhibit, in contrast to human tutors. We conduct a preliminary study of these differences with the CIMA dataset and three state-of-the-art LLMs (GPT-4o, Gemini Pro 1.5, and LLaMA 3.1 450B). Our results reveal systematic deviations in these baseline patterns, particulary in the tutoring actions selected, complexity of responses, and even within different LLMs. 

This research brings forward some early results in understanding how LLMs when deployed as tutors exhibit systematic differences, which has implications for educational technology design and deployment. We note that while LLMs enable more powerful and fluid interaction than previous systems, they simultaneously develop characteristic patterns distinct from human teaching. Understanding these differences can inform better integration of AI in educational settings.

Comparing Behavioral Patterns of LLM and Human Tutors: A Population-level Analysis with the CIMA Dataset

This paper presents EduCSW, a novel pipeline for generating Mandarin-English code-switched text to support AI-powered educational tools that adapt computer science instruction to learners' language proficiency through mixed-language delivery. To address the scarcity of code-mixed datasets, we propose an encoder-decoder architecture that generates natural code-switched text using only minimal existing code-mixed examples and parallel corpora. Evaluated on a corpus curated for computer science education, human annotators rated 60--64% of our model's outputs as natural, significantly outperforming both a baseline fine-tuned neural machine translation (NMT) model (22--24%) and the DeepSeek-R1 model (34--44%). The generated text achieves a Code-Mixing Index (CMI) of 25.28%, aligning with patterns observed in spontaneous Mandarin-English code-switching. Designed to be generalizable across language pairs and domains, this pipeline lays the groundwork for generating training data to support the development of educational tools with dynamic code-switching capabilities.

EduCSW: Building a Mandarin-English Code-Switched Generation Pipeline for Computer Science Learning

The advent of artificial intelligence (AI) has marked a transformative era in educational measurement and evaluation, particularly in the development of assessment items. Large language models (LLMs) have emerged as promising tools for scalable automatic item generation (AIG), yet concerns remain about the validity of AI-generated items in various domains. To address this issue, we propose STAIR-AIG (Systematic Tool for Assessment Item Review in Automatic Item Generation), a human-in-the-loop framework that integrates expert judgment to optimize the quality of AIG items. To explore the functionality of the tool, AIG items were generated in the domain of critical thinking. Subsequently, the human expert and four OpenAI LLMs conducted a review of the AIG items. The results show that while the LLMs demonstrated high consistency in their rating of the AIG items, they exhibited a tendency towards leniency. In contrast, the human expert provided more variable and strict evaluations, identifying issues such as the irrelevance of the construct and cultural insensitivity. These findings highlight the viability of STAIR-AIG as a structured human-AI collaboration approach that facilitates rigorous item review, thus optimizing the quality of AIG items. Furthermore, STAIR-AIG enables iterative review processes and accumulates human feedback, facilitating the refinement of models and prompts. This, in turn, would establish a more reliable and comprehensive pipeline to improve AIG practices.

STAIR-AIG: Optimizing the Automated Item Generation Process through Human-AI Collaboration for Critical Thinking Assessment

We present UPSC2M, a large-scale dataset comprising two million multiple-choice question attempts from over 46,000 students, spanning nearly 9,000 questions across seven subject areas. The questions are drawn from the Union Public Service Commission (UPSC) examination, one of India's most competitive and high-stakes assessments. Each attempt includes both response correctness and time taken, enabling fine-grained analysis of learner behavior and question characteristics. Over this dataset, we define two core benchmark tasks: question difficulty estimation and student performance prediction. The first task involves predicting empirical correctness rates using only question text. The second task focuses on predicting the likelihood of a correct response based on prior interactions. We evaluate simple baseline models on both tasks to demonstrate feasibility and establish reference points. Together, the dataset and benchmarks offer a strong foundation for building scalable, personalized educational systems. We release the dataset and code to support further research at the intersection of content understanding, learner modeling, and adaptive assessment.

UPSC2M: Benchmarking Adaptive Learning from Two Million MCQ Attempts

Despite recent advances in AI detection methods, their practical application, especially in education, remains limited. Educators need functional tools pointing to AI indicators within texts, rather than merely estimating whether AI was used. 
GPTZero's new AI Vocabulary feature, which highlights parts of a text likely to be AI-generated based on frequent words and phrases from LLM-generated texts, offers a potential solution. However, its effectiveness has not yet been empirically validated.
In this study, we examine whether GPTZero's AI Vocabulary can effectively distinguish between LLM-generated and student-written essays. We analyze the AI Vocabulary lists published from October 2024 to March 2025 and evaluate them on a subset of the Ghostbuster dataset, which includes student and LLM essays. We train multiple Bag-of-Words classifiers using GPTZero's AI Vocabulary terms as features and examine their individual contributions to classification.
Our findings show that simply checking for the presence, not the frequency, of specific AI terms yields the best results, particularly with ChatGPT-generated essays. However, performance drops to near-random when applied to Claude-generated essays, indicating that GPTZero's AI Vocabulary may not generalize well to texts generated by LLMs other than ChatGPT. Additionally, all classifiers based on GPTZero's AI Vocabulary significantly underperform compared to Bag-of-Words classifiers trained directly on the full dataset vocabulary. These findings suggest that fixed vocabularies based solely on lexical features, despite their interpretability, have limited effectiveness across different LLMs and educational writing contexts.

Can GPTZero's AI Vocabulary Distinguish Between LLM-Generated and Student-Written Essays?

We present a case study on building task-specific models for grammatical error correction and explanation generation tailored to learners of Estonian. Our approach handles whole paragraphs instead of sentences and leverages prompting proprietary large language models for generating synthetic training data, addressing the limited availability of error correction data and the complete absence of correction justification/explanation data in Estonian. We describe the chosen approach and pipeline and provide technical details for the experimental part. The final outcome is a set of open-weight models, which are released with a permissive license along with the generated synthetic error correction and explanation data.

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Enhancing Security and Strengthening Defenses in Automated Short-Answer Grading Systems

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