Austria

This paper presents a data-driven analysis of Romanian secondary school textbooks through the lens of Bloom&#39;s Taxonomy, focusing on the promotion of critical thinking in instructional design. Using the ROTEX corpus, we extract and annotate almost 2 million words of Romanian Language and Literature textbooks (grades 5-8) with Bloom-aligned labels for verbs associated with pedagogical tasks. Our annotation pipeline combines automatic verb extraction, human filtering based on syntactic form and task relevance, and manual assignment of Bloom labels supported by in-text concordance checks. The resulting dataset enables fine-grained analysis of task complexity both across and within textbooks and grade levels. Our findings reveal a general lack of structured cognitive progression across most textbook series. We also propose a multi-dimensional framework combining cognitive-level and linguistic evaluation to assess instructional design quality. This work contributes annotated resources and reproducible methods for NLP-based educational content analysis in low-resource languages.

ACL 2025

Assessing Critical Thinking Components in Romanian Secondary School Textbooks: A Data Mining Approach to the ROTEX Corpus

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

This paper presents a data-driven analysis of Romanian secondary school textbooks through the lens of Bloom's Taxonomy, focusing on the promotion of critical thinking in instructional design. Using the ROTEX corpus, we extract and annotate almost 2 million words of Romanian Language and Literature textbooks (grades 5-8) with Bloom-aligned labels for verbs associated with pedagogical tasks. Our annotation pipeline combines automatic verb extraction, human filtering based on syntactic form and task relevance, and manual assignment of Bloom labels supported by in-text concordance checks. The resulting dataset enables fine-grained analysis of task complexity both across and within textbooks and grade levels. Our findings reveal a general lack of structured cognitive progression across most textbook series. We also propose a multi-dimensional framework combining cognitive-level and linguistic evaluation to assess instructional design quality. This work contributes annotated resources and reproducible methods for NLP-based educational content analysis in low-resource 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 paper presents a strategy for improving AI assistants embedded in short e-learning courses. The proposed method is implemented within a Retrieval-Augmented Generation (RAG) architecture and evaluated using several retrieval variants. The results show that query quality improves when the knowledge base is enriched with definitions of key concepts discussed in the course. Our main contribution is a lightweight enhancement approach that increases response quality without overloading the course with additional instructional content.

Improving AI assistants embedded in short e-learning courses with limited textual content

This paper presents an LLM-powered approach for generating concept maps to enhance digital reading comprehension in higher education. While particularly focused on supporting neurodivergent students with their distinct information processing patterns, this approach benefits all learners facing the cognitive challenges of digital text. We use GPT-4o-mini to extract concepts and relationships from educational texts across ten diverse disciplines using open-domain prompts without predefined categories or relation types, enabling discipline-agnostic extraction. Section-level processing achieved higher precision (83.62%) in concept extraction, while paragraph-level processing demonstrated superior recall (74.51%) in identifying educationally relevant concepts. We implemented an interactive web-based visualization tool https://simplified-cognitext.streamlit.app that transforms extracted concepts into navigable concept maps. User evaluation (n=14) showed that participants experienced a 31.5% reduction in perceived cognitive load when using concept maps, despite spending more time with the visualization (22.6% increase). They also completed comprehension assessments more efficiently (14.1% faster) with comparable accuracy. This work demonstrates that LLM-based concept mapping can significantly reduce cognitive demands while supporting non-linear exploration.

Beyond Linear Digital Reading: An LLM-Powered Concept Mapping Approach for Reducing Cognitive Load

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.

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

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.

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