Austria

Communicative practice is critical for second language development, yet learners often lack targeted, engaging opportunities to use new grammar structures. While large language models (LLMs) can offer coherent interactions, they are not inherently aligned with pedagogical goals or proficiency levels. In this paper, we explore how LLMs can be integrated into a structured framework for contextually-constrained, grammar-focused interaction, building on an existing goal-oriented dialogue system. Through controlled simulations, we evaluate five LLMs across 75 A2-level tasks under two conditions: (i) grammar-targeted, task-anchored prompting and (ii) the addition of a lightweight post-generation validation pipeline using a grammar annotator.
Our findings show that template-based prompting alone substantially increases target-form coverage up to 91.4% for LLaMA 3.1-70B-Instruct, while reducing overly advanced grammar usage. The validation pipeline provides an additional boost in form-focused tasks, raising coverage to 96.3% without significantly degrading appropriateness.

ACL 2025

A Framework for Proficiency-Aligned Grammar Practice in LLM-Based Dialogue Systems

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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)

Large Language Models (LLMs) are increasingly used as proxy students in the development of Intelligent Tutoring Systems (ITSs) and in piloting test questions. However, to what extent these proxy students accurately emulate the behavior and characteristics of real students remains an open question. To investigate this, we collected a dataset of 489 items from the National Assessment of Educational Progress (NAEP), covering mathematics and reading comprehension in grades 4, 8, and 12. We then apply an Item Response Theory (IRT) model to position 11 diverse and state-of-the-art LLMs on the same ability scale as real student populations. Our findings reveal that, without guidance, strong general-purpose models consistently outperform the average student at every grade, while weaker or domain-mismatched models may align incidentally. Using grade-enforcement prompts changes models' performance, but whether they align with the average grade-level student remains highly model- and prompt-specific: no evaluated model–prompt pair fits the bill across subjects and grades, underscoring the need for new training and evaluation strategies. We conclude by providing guidelines for the selection of viable proxies based on our findings. All related code and data have been made available (https://github.com/kvadityasrivatsa/IRT-for-LLMs-as-Students).

Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?

This shared task has aimed to assess pedagogical abilities of AI tutors powered by large language models (LLMs), focusing on evaluating the quality of tutor responses aimed at student's mistake remediation within educational dialogues. The task consisted of five tracks designed to automatically evaluate the AI tutor's performance across key dimensions of mistake identification, precise location of the mistake, providing guidance, and feedback actionability, grounded in learning science principles that define good and effective tutor responses, as well as the track focusing on detection of the tutor identity. The task attracted over 50 international teams across all tracks. The submitted models were evaluated against gold-standard human annotations, and the results, while promising, show that there is still significant room for improvement in this domain: the best results for the four pedagogical ability assessment tracks range between macro F1 scores of 58.34 (for providing guidance) and 71.81 (for mistake identification) on three-class problems, with the best F1 score in the tutor identification track reaching 96.98 on a 9-class task. In this paper, we overview the main findings of the shared task, discuss the approaches taken by the teams, and analyze their performance. All resources associated with this task are made publicly available to support future
research in this critical domain (https://github.com/kaushal0494/UnifyingAITutorEvaluation/tree/main/BEA_Shared_Task_2025_Datasets).

Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors

The growing use of large language models (LLMs) for AI-powered tutors in education highlights the need for reliable evaluation of their pedagogical abilities. In this work, we propose a reasoning-based evaluation methodology that leverages pedagogical domain knowledge to assess LLM-generated feedback in mathematical dialogues while providing insights into why a particular evaluation is given. We design structured prompts to invoke pedagogically-informed reasoning from LLMs and compare base model candidates selected for their strengths in reasoning, mathematics, and overall instruction-following. We employ Group Relative Policy Optimization (GRPO), a reinforcement learning method known to improve reasoning performance, to train models to perform evaluation in four pedagogically motivated dimensions, Mistake Identification, Mistake Location, Providing Guidance, and Actionability. Experimental results show that our GRPO-based models consistently outperform the base model and GPT-4.1, and surpass models trained using supervised fine-tuning in three out of four dimensions. Notably, our method achieved top-ranked performance in Actionability and competitive performance in two other dimensions in the BEA 2025 Shared Task under the team name bea-jh, underscoring the value of generating pedagogically grounded rationales for improving the quality of educational feedback evaluation.

bea-jh at BEA 2025 Shared Task: Evaluating AI-powered Tutors through Pedagogically-Informed Reasoning

This paper describes our approaches for the BEA-2025 Shared Task on assessing pedagogical ability and attributing tutor identities in AI-powered tutoring systems. We explored three methodological paradigms: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). Results indicate clear methodological strengths: SFT is highly effective for structured classification tasks such as mistake identification and feedback actionability, while ICL with advanced prompting excels at open-ended tasks involving mistake localization and instructional guidance. Additionally, fine-tuned models demonstrated strong performance in identifying tutor authorship. Our findings highlight the importance of aligning methodological strategy and task structure, providing insights toward more effective evaluations of educational AI systems.

BLCU-ICALL at BEA 2025 Shared Task: Multi-Strategy Evaluation of AI Tutors

The rapid adoption of AI in educational technology is changing learning settings, making the thorough evaluation of AI tutor pedagogical performance is quite important for promoting student success. This paper describes our solution for the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered tutors, which assesses tutor replies over several pedagogical dimensions. We developed transformer-based approaches for five diverse tracks: mistake identification, mistake location, providing guidance, actionability, and tutor identity prediction using the MRBench dataset of mathematical dialogues. We evaluated several pre-trained models including DeBERTa-V3, RoBERTa-Large, SciBERT, and EduBERT. Our approach addressed class imbalance problems by incorporating strategic fine-tuning with weighted loss functions. The findings show that, for all tracks, DeBERTa architectures have higher performances than the others, and our models have obtained in the competitive positions, including 9th of Tutor Identity (Exact F1 of 0.8621), 16th of Actionability (Exact F1 of 0.6284), 19th of Providing Guidance (Exact F1 of 0.4933), 20th of Mistake Identification (Exact F1 of 0.6617) and 22nd of Mistake Location (Exact F1 of 0.4935). The difference in performance over tracks highlights the difficulty of automatic pedagogical evaluation, especially for tasks whose solutions require a deep understanding of educational contexts. This work contributes to ongoing efforts to develop robust automated tools for assessing.

SmolLab_SEU at BEA 2025 Shared Task: A Transformer-Based Framework for Multi-Track Pedagogical Evaluation of AI-Powered Tutors

This paper presents automatic evaluation systems for assessing the pedagogical capabilities of LLM-based AI tutors. Drawing from a shared task, our systems specifically target four key dimensions of tutor responses: Mistake Identification, Mistake Location, Providing Guidance, and Actionability. These dimensions capture the educational quality of responses from multiple perspectives, including the ability to detect student mistakes, accurately identify error locations, provide effective instructional guidance, and offer actionable feedback. We propose GPT-4.1-based automatic evaluation systems, leveraging their strong capabilities in comprehending diverse linguistic expressions and complex conversational contexts to address the detailed evaluation criteria across these dimensions. Our systems were quantitatively evaluated based on the official criteria of each track. In the Mistake Location track, our evaluation systems achieved an Exact macro F1 score of 58.80\% (ranked in the top 3), and in the Providing Guidance track, they achieved 56.06\% (ranked in the top 5). While the systems showed mid-range performance in the remaining tracks, the overall results demonstrate that our proposed automatic evaluation systems can effectively assess the quality of tutor responses, highlighting their potential for evaluating AI tutor effectiveness.

K-NLPers at BEA 2025 Shared Task: Evaluating the Quality of AI Tutor Responses with GPT-4.1

As AI's presence in educational environments grows, it becomes critical to evaluate how its feedback may impact students' learning processes. Pedagogical theory, with decades of effort into understanding how human instructors give good-quality feedback to students, may provide a rich source of insight into feedback automation. In this paper, we propose a novel architecture based on pedagogical-theory feature extraction from the conversation history and tutor response to predict pedagogical guidance on MRBench. Such features are based on Brookhart's canonical work in pedagogical theory, and extracted by prompting the language model LearnLM. The features are then used to train a random-forest classifier to predict the ‘providing guidance' dimension of the MRBench dataset. Our approach ranked 8\textsuperscript{th} in the dimension's leaderboard with a test Macro F1-score of ~0.54. Our work provides some evidence in support of using pedagogical theory qualitative factors treated separately to provide clearer guidelines on how to improve low-scoring intelligent tutoring systems. Finally, we observed several inconsistencies between pedagogical theory and MRBench's inherent relaxation of the tutoring problem implied in evaluating on a single-conversation basis, calling for the development of more elaborate measures which consider student profiles to serve as true heuristics of AI tutors' usefulness.

IALab UC at BEA 2025 Shared Task: LLM-Powered Expert Pedagogical Feature Extraction

We present MSA-MathEval, our submission to the BEA 2025 Shared Task on evaluating AI tutor responses across four instructional dimensions: Mistake Identification, Mistake Location, Providing Guidance, and Actionability. Our approach uses a unified training pipeline to fine-tune a single instruction-tuned language model across all tracks, without any task-specific architectural changes. To improve prediction reliability, we introduce a disagreement-aware ensemble inference strategy that enhances coverage of minority labels. Our system achieves strong performance across all tracks, ranking 1st in Providing Guidance, 3rd in Actionability, and 4th in both Mistake Identification and Mistake Location. These results demonstrate the effectiveness of scalable instruction tuning and disagreement-driven modeling for robust, multi-dimensional evaluation of LLMs as educational tutors.

MSA at BEA 2025 Shared Task: Disagreement-Aware Instruction Tuning for Multi-Dimensional Evaluation of LLMs as Math Tutors

In light of the growing adoption of large language models (LLMs) as educational tutors, it is crucial to effectively evaluate their pedagogical capabilities across multiple dimensions. Toward this goal, we address the Mistake Identification sub-task of the BEA 2025 Shared task, aiming to assess the accuracy of tutors in detecting and identifying student errors. We experiment with several LLMs, including GPT-4o-mini, Mistral-7B, and Llama-3.1-8B, evaluating them in both zero-shot and fine-tuned settings. To address class imbalance, we augment the training data with synthetic examples, targeting underrepresented labels, generated by Command R+. Our GPT-4o model finetuned on the full development set achieves a strict macro-averaged F1 score of 71.63%, ranking second in the shared task. Our work highlights the effectiveness of fine-tuning on task-specific data and suggests that targeted data augmentation can further support LLM performance on nuanced pedagogical evaluation tasks.

TutorMind at BEA 2025 Shared Task: Leveraging Fine-Tuned LLMs and Data Augmentation for Mistake Identification

With the rapid advancement of global digitaliza-
tion, users from different countries increasingly
rely on social media for information exchange.
In this context, multilingual multi-label emo-
tion detection has emerged as a critical research
area. This study addresses SemEval-2025 Task
11: Bridging the Gap in Text-Based Emotion
Detection. Our paper focuses on two sub-tracks
of this task: (1) Track A: Multi-label emo-
tion detection, and (2) Track B: Emotion in-
tensity. To tackle multilingual challenges, we
leverage pre-trained multilingual models and
focus on two architectures: (1) a fine-tuned
BERT-based classification model and (2) an
instruction-tuned generative LLM. Addition-
ally, we propose two methods for handling
multi-label classification: the base method,
which maps an input directly to all its cor-
responding emotion labels, and the pairwise
method, which models the relationship between
the input text and each emotion category indi-
vidually. Experimental results demonstrate the
strong generalization ability of our approach in
multilingual emotion recognition. In Track A,
our method achieved Top 4 performance across
10 languages, ranking 1st in Hindi. In Track B,
our approach also secured Top 5 performance
in 7 languages, highlighting its simplicity and
effectiveness1
.

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Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?

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Grammar Control in Dialogue Response Generation for Language Learning Chatbots

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