United States

Constructive, personalized, and timely feedback is essential to student learning. However, providing such feedback in large classes remains a major challenge. Large language models (LLMs) offer alternatives to support instructors by generating relevant feedback that highlights both student strengths and areas for improvement. Nevertheless, most existing LLM-based feedback systems rely on proprietary APIs and are often tailored to specific tasks, which limits their accessibility, flexibility, and applicability in resource-constrained educational settings. In this study, we investigate the potential of two open-source LLMs to support automated feedback generation for varied forms of student assignments across three disciplines (e.g., programming assignments, essays, and mathematics problems). We evaluate two prompting strategies (unified and multi-agent) across these domains and use human judgment criteria to assess feedback quality. Through this analysis, we examine whether open-source models can serve as practical, scalable alternatives to proprietary API-based systems for developing freely accessible feedback-generation tools. Our results provide initial evidence for cross-domain applicability, rather than establishing full generalizability. We conclude that open-source models feedback quality varies significantly across domains, and that the effectiveness of prompting strategies depends on the nature of the task.

ACL 2026

Toward Cross-Domain Automated Feedback: A Comparative Evaluation of Open-Source Models across Diverse Student Assessment Types

multi-agent prompting

automated feedback generation

open-source llms

educational nlp

large language models

**Welcome to EMNLP 2023!**

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EMNLP 2023

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workshop paper

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

We propose a two-stage framework for vocabulary difficulty prediction for L2 learners, addressing representation-dependent variation and learner-specific effects such as L1 transfer. Stage 1 constructs diverse representations using multiple pretrained encoders with different pooling and prediction strategies, capturing complementary aspects of lexical and contextual complexity. Stage 2 models systematic residual errors using psycholinguistic and cross-lingual features, enabling explicit correction of learner-dependent biases. Experiments on the BEA 2026 Shared Task (KVL dataset) show that the proposed approach outperforms strong baselines, achieving substantial improvements in both RMSE and correlation. These results demonstrate the effectiveness of combining representation diversity with structured residual calibration for accurate difficulty prediction.

A Two-Stage Framework for L1-Aware Vocabulary Difficulty Prediction with Representation Diversity and Residual Calibration

What makes a word difficult to learn, and how does the difficulty depend on the learner's native language? We computationally model vocabulary difficulty for English learners whose first language is Spanish, German, or Chinese with gradient-boosted models trained on familiarity, meaning, surface-form, and cross-linguistic transfer features. Using Shapley values, we identify the importance of feature groups. Word familiarity is the dominant feature group shared by all three languages. However, Spanish- and German-L1 predictions rely additionally on orthographic transfer and predictions for easy words generally rely on one strong clue, either familiarity or transfer. This transfer mechanism is unavailable to Chinese learners, whose difficulty is shaped by a combination of familiarity and surface features alone. Our models provide interpretable L1-tailored difficulty estimates applicable to the design of vocabulary curricula and readability measures.

What Makes a Word Hard to Learn? Modeling L1 Influence on English Vocabulary Difficulty

In this paper, we present our team submissions to the BEA 2026 Shared Task 2:
Rubric-based Short Answer Scoring for German. We systematically explored how fine-tuned language models can be reliably employed for short answer scoring, for which three aspects turn out to be particularly beneficial: supplementing the finetuning process with generated domain expertise, restructured rubrics, and thinking traces. To increase the robustness of the scoring, we combine distinct approaches in an ensemble. Our best submissions finished in first place across all tracks, indicating promise for the further application of these strategies in automatic scoring.

IWM-DKM at BEA 2026 Shared Task 2: Supplementing Supervised Fine-Tuning for Rubric-Based Short Answer Scoring

In this paper, we present the RETUYT-INCO participation at the BEA 2026 shared task "Rubric-based Short Answer Scoring for German". Our team participated in track 1 (Unseen answers three-way), track 3 (Unseen answers two-way) and track 4 (Unseen questions two-way). Since these tracks required scoring short student answers using specific rubrics, we looked for ways to handle the changing nature of the task. We created a method called Meta-prompting. In this approach, an LLM creates a custom prompt based on examples from the Train set. This prompt is then used to grade new student answers. Along with this method, we also describe other approaches we used, such as classic machine learning, fine-tuning open-source LLMs, and different prompting techniques. According to the official results, our team placed 6th out of 8 participants in Track 1 with a QWK of 0.729. In Track 3, we secured 4th place out of 9 with a QWK of 0.674, and we also placed 4th out of 8 in Track 4 with a QWK of 0.49.

RETUYT-INCO at BEA 2026 Shared Task 2: Meta-prompting in Rubric-based Scoring for German

We describe two types of models for vocabulary difficulty prediction: a high-accuracy blackbox model, which achieved the top shared task result in the open track, and an explainable model, which outperforms a fine-tuned encoder baseline. As the blackbox model, we fine-tuned an LLM using a soft-target loss function for effective application to the rating task, achieving r > 0.91. The explainable model provides insights into what impacts the difficulty of each item while maintaining a strong correlation (r > 0.77). We further analyze the results, demonstrating that the difficulty of items in the British Council's Knowledge-based Vocabulary Lists (KVL) is often affected by spelling difficulty or the construction of the test items, in addition to the genuine production difficulty of the words.

What Makes Words Hard? Sakura at BEA 2026 Shared Task on Vocabulary Difficulty Prediction

This paper reports findings from the BEA 2026 Shared Task on Vocabulary Difficulty Prediction for English Learners across three L1s (Spanish, German and Mandarin). The task featured open and closed tracks, using data from the British Council's Knowledge-based Vocabulary Lists (KVL). Submissions were received from 23 teams employing diverse modelling approaches, including transformers, Large Language Models, feature-based approaches and ensembles. Results were evaluated using RMSE, with winning systems significantly exceeding the baseline and establishing new state-of-the-art benchmarks. This paper offers an examination of the participating systems, performance across tracks and L1s, and the factors that can affect prediction accuracy.

Findings of the BEA 2026 Shared Task on Vocabulary Difficulty Prediction for English Learners

This paper presents a Closed Track system for the BEA 2026 Shared Task on Vocabulary Difficulty Prediction for English Learners. The task requires predicting GLMM-calibrated difficulty scores for English vocabulary items across Spanish, German, and Mandarin Chinese L1 backgrounds. Our approach studies how engineered linguistic features can complement contextual multilingual transformer representations under the Closed Track setting. We evaluate 33 phonological, morphological, semantic, contextual, and cross-lingual features through feature-only regressors, Solo transformer models, Hybrid transformer models, and prediction-level ensembling. The official Closed Track submissions used XLM-RoBERTa-large Solo and Hybrid models and improved over the official baseline across all three L1 groups. We also report an extended post-submission Closed Track analysis using mDeBERTa-v3-base components and a Ridge stacking ensemble, which achieved the lowest RMSE among our evaluated systems: 1.037 for Spanish, 0.997 for German, and 0.913 for Mandarin Chinese, with a mean RMSE of 0.982. The results suggest that engineered features provide useful complementary linguistic signal, while prediction-level ensembling offers the most robust improvement for L1-aware vocabulary difficulty prediction in the Closed Track setting.

A Hybrid Transformer–Feature Ensemble for L1-Aware English Vocabulary Difficulty Prediction: System Description for the BEA 2026 Shared Task

Large language models are increasingly used to provide automated feedback on open-ended student responses. However, most current systems generate content-level corrections without explicitly modeling learners' cognitive states. This may limit pedagogical effectiveness, particularly in domains where personalized feedback is scarce. We propose a cognitively-aware generative feedback framework that integrates lightweight learner state modeling with LLM-based response generation. The system infers latent cognitive signals from student responses and conditions feedback generation on these signals. We evaluate our approach on three publicly available datasets spanning science, computing, and technical domains, comprising 2,847 responses. Compared to a static LLM baseline, cognitively-aware feedback receives higher expert ratings (+0.38 on a 5-point scale). Ablation results indicate that multiple cognitive dimensions contribute to feedback quality. The approach maintains competitive performance with reduced training data, achieving 94% of full-model performance with 200 training examples. These findings suggest that integrating cognitive signals into generative feedback is a promising direction for adaptive automated assessment.

Cognitively-Aware Generative Feedback: Integrating Learner State Modeling with Large Language Models for Adaptive Educational Support

We introduce a thermal–visual fusion approach to improve the robustness of
non-invasive Voice Activity Detection (VAD) for classroom engagement
monitoring. In noisy multi-speaker classrooms using a single microphone, acoustic-only methods fail to reliably isolate individual speakers. Our method integrates facial thermal signatures—capturing
respiratory and speech-related heat patterns—with visual lip-motion
cues to provide an acoustic-independent speech signal. This provides a localized, privacy-preserving, and
acoustic-independent indicator of speech activity.

This system acts as a visual-diarization front-end,
informing Automatic Speech Recognition (ASR) and Natural Language
Processing (NLP) systems not only when speech occurs, but precisely
which student is speaking. Using 19 engineered features, our
Thermal-Only Random Forest classifier achieved a Recall of 0.9234 and an
F1-score of 0.8105 in subject-independent evaluations, outperforming
visual-only baselines. The system was successfully deployed on a
Raspberry Pi 5, demonstrating real-time performance. These results
demonstrate that thermal–visual fusion enables more reliable linguistic
analysis of collaborative learning and provide critical input for AI
agents to facilitate group participation in real-world educational
settings that lead to more successful learning outcomes.

Inferring Student Engagement via Real-Time Thermal–Visual Voice Activity Detection

With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become increasingly prominent. Prior works on LLM-based tutoring typically learn a single tutor policy and do not capture the diversity of tutoring styles. In real-world tutor–student interactions, pedagogical intent is realized through adaptive instructional strategies, with tutors varying the level of scaffolding, instructional directiveness, feedback, and affective support in response to learners' needs. These differences can all impact dialogue dynamics and student engagement. In this paper, we explore how tutor personas embedded in human tutor-student dialogues can be used to guide LLM behavior without relying on explicitly prompted instructions. We modify Bidirectional Preference Optimization (BiPO) to learn a steering vector, an activation-space direction that steers model responses towards certain tutor personas. We find that this steering vector captures tutor-specific variation across dialogue contexts, improving semantic alignment with ground-truth tutor utterances and increasing preference-based evaluations, while largely preserving lexical similarity. Analysis of the learned directional coefficients further reveals interpretable structure across tutors, corresponding to consistent differences in tutoring behavior. These results demonstrate that activation steering offers an effective and interpretable way for controlling tutor-specific variation in LLMs using signals derived directly from human dialogue data.

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A Two-Stage Framework for L1-Aware Vocabulary Difficulty Prediction with Representation Diversity and Residual Calibration

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