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Productive struggle is a critical component of mathematics education, requiring students to actively work through ideas rather than just making errors. However, identifying this struggle from text transcripts is challenging because students often mask confusion with epistemic hedging rather than direct statements. Zero-shot large language models exhibit a conservative bias, systematically under-detecting struggle in classroom discourse. We introduce a two-stage NLP pipeline comprising a lexical heuristic gate and an LLM subtype classifier. Our model achieves 90.0% binary accuracy and 84.0% 4-category accuracy. We demonstrate the pedagogical value of this tool by showing that struggle is uniquely concentrated during explicit mathematical reasoning, offering educators a scalable method for root-cause analysis.

ACL 2026

Classification of Student Struggle in Mathematics

pedagogical feedback

epistemic hedging

productive struggle detection

classroom discourse analysis

mathematics education

natural language processing

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. <br>
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workshop paper

Recent advances in the capabilities of conversational agents based on large language models make them a very promising tool for role playing K-12 students in order to train educators in conversational teaching practices, such as eliciting student thinking, explaining disciplinary content, and facilitating a classroom discussion. In fact, such simulations can and have been developed relatively quickly and without data to machine-learn from -- neither classroom data nor human-simulated data. To enhance the usefulness and effectiveness of such teaching simulations, it is necessary to provide pedagogically sound, timely, and personalized feedback to the educator about their simulation performance. In this study, we present experiments on fine-tuning models to evaluate educator performance in an elicitation teaching simulation. The models are developed with data collected during usability testing of the simulation and evaluated on real user data. We show that even with relatively little fine-tuning data, robust performance can be obtained

Data-lean fine-tuning of models for evaluating teacher performance in a GenAI-led elicitation simulation

Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring systems to provide personalized support, it is essential to assess student performance at each turn, motivating knowledge tracing (KT) in dialogue settings. However, existing dialogue-based KT approaches often ignore question difficulty modeling and rely on opaque latent representations from LLMs, hindering accurate and interpretable prediction. In this work, we propose an interpretable difficulty-aware conversational KT framework built upon LLMs, which explicitly models students' abilities and the tutor-posed tasks at each turn. The framework incorporates the original textual question and the next tutor-posed task to estimate the student's knowledge state and the difficulty of the upcoming turn. Furthermore, it integrates Item Response Theory to map LLM's outputs into student ability and question difficulty parameters, enabling interpretable prediction of student performance grounded in cognitive theories of learning. We evaluate the framework on two tutor-student dialogue datasets. Both quantitative and qualitative results show that our framework outperforms existing KT baselines, meanwhile generating interpretable outputs consistent with cognitive theory.

Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues

Rubrics are the primary reference for manual scoring of constructed responses, and there is growing interest in their use in automated scoring methodologies. In this work, we propose Aspect-Grounded Rubric–Answer Alignment (AGRAA), a rubric-based end-to-end scoring framework that models rubric descriptors as latent aspect spaces. Concretely, rubric descriptors are represented as low-dimensional subspaces derived from contextualised transformer embeddings, and student responses are scored according to how strongly their representations align with these rubric-induced spaces relative to the residual space outside them. This formulation provides a geometrically grounded interpretation of rubric-based scoring while enabling end-to-end training with standard transformer encoders. We introduce three distinct architectural variants and evaluate them on multiple short-answer and essay scoring datasets. Across these tasks, AGRAA achieves predictive performance highly competitive with strong neural and feature-based baselines. In addition, the framework yields interpretable intermediate representations that expose which rubric-defined aspects contribute to scoring decisions, enabling decision-aligned explanations grounded in rubric descriptors.

Rubrics as Semantic Subspaces: A Unified Approach to Rubric-based Constructed Response Scoring across Short Answers and Essays

This paper proposes a computational framework to operationalize optimal challenge based on Krashen's Input Hypothesis for open-ended conversational English as a Foreign Language (EFL) tutoring. Because conversational EFL tutoring relies on dynamically generated exercises rather than static item banks, traditional difficulty estimation methods cannot verify if a task is appropriately calibrated to a learner. Rather than treating difficulty as an intrinsic item property, we measure it directly from real-time interactional behavior, classifying exercises into three alignment states: Under-Challenged, Optimally Challenged, and Over-Challenged. Using 1,566 exercises from the Teacher-Student Chatroom Corpus, we first demonstrate that a learner's history of these states highly predicts their success on subsequent tasks. We then leverage this micro-level predictive model to construct an in-situ learning gain proxy, tracking shifts in a student's expected success probability across a session. Analyzing these macro-level trajectories, we find that sessions with higher proportions of over-challenging exercises systematically yield lower estimated gains. Crucially, increasing the proportion of optimally challenging interactions drives statistically significantly greater capability improvement than providing under-challenging tasks, providing quantitative evidence consistent with the Input Hypothesis in open-ended tutoring.

Measuring Optimal Challenge: Trajectory-Based Difficulty Alignment in Open-Ended Language Tutoring

Investigating the degree to which large language models (LLMs) affect teaching and learning in universities can help identify strategies for integrating LLMs in a way that supports, rather than undermines, student learning outcomes. This study examined how varying levels of LLM assistance affect writing performance, engagement, and perceived authorship. We report a pilot study in which 24 college students were randomly assigned to write a short essay with no LLM access, limited access (<=3 prompts, responses capped at 100 words), or unlimited access. Overall essay quality was statistically indistinguishable across groups. Yet writing behavior and perceived authorship diverged sharply: students with limited access reported higher ownership (62.5% would submit the essay as independent work, vs. 25% in the unlimited group), stronger organizational gains, and more strategic, revision-focused prompting. The unlimited group spent more time writing, produced essays more similar to LLM output, and reported reduced creative expression. Our findings suggest that constraining, rather than banning, LLM access may preserve authorship confidence while retaining the scaffolding benefits of AI assistance.

Effects of Varying LLM Access on Essay Writing Behavior

Improvements in computing resources and advances made in artificial intelligence have accelerated a systemic shift toward automating educational infrastructure. While the Natural Language Processing community has dedicated significant resources to developing educational technologies (EdTech) to address these pressures, it remains unclear whose interests are being best served among the many stakeholders involved. In this paper, we present a systematic literature review of papers published at venues within the Association for Computational Linguistics' Special Interest Group on Building Educational Applications (ACL SIGEDU) in 2024 and 2025, validated against EdTech papers from the wider ACL Anthology. By examining stakeholder inclusion and the prioritisation of research tasks, our findings reveal a critical tension: a push and pull between private-sector incentives and the foundational needs of educational infrastructure. Our analysis reveals that teachers are systematically under-represented as beneficiaries, that real-world deployment remains rare, and that ethical engagement tends toward acknowledgement rather than action. We identify exemplary research that successfully balances technical innovation with a responsible commitment to stakeholder agency and welfare, and derive from it actionable recommendations to ensure that EduNLP research continues to align with the theoretical goals of teaching and learning.

Incentives Of EdTech: A Systematic Review Of EduNLP Research

Large language models can generate feedback on free-form student writing, but it is unclear whether such feedback is correct and pedagogically useful. We evaluate LLM-generated feedback on 65 undergraduate proof-writing exercises using Hattie and Timperley's feedback framework and a grade agreement metric, comparing two models (GPT-4.1, GPT-5) under two workflow configurations graded by two independent LLM evaluators. A mark-scheme-augmented workflow improves grade correlation with human experts for both models, and its precomputed mark schemes allow instructors to audit the system before deployment. GPT-5 produces higher-quality feedback across all dimensions. The metrics we collect give some evidence that in the setting studied, feedback quality is high, and several sanity checks on our experiments support this finding. However, providing meaningful self-regulation support and controlled tests with students remain to be done. The results in this contribution show that feedback theory provides a useful lens for evaluating automated mathematical feedback.

Evaluating LLM-Generated Formative Feedback for Undergraduate Mathematics Through the Lens of Feedback Theory

Effective tutoring requires distinguishing optimal (expert-path), valid-alternative (valid but suboptimal), and incorrect student solutions, a distinction central to intelligent tutoring systems (ITS) yet untested for LLM-based tutors. As LLMs are increasingly explored as conversational complements to ITS for scalable feedback, evaluating their diagnostic precision is essential. We benchmark seven LLMs as feedback agents in propositional logic using knowledge-graph-derived ground truth across 10,836 solution-feedback pairs \footnote{\url{https://github.com/anonymouspaper64-debug/anonymous_paper.git}} and three prompt-level feedback conditions. Our analysis show that models achieve near-ceiling performance on optimal steps but systematically over-reject valid-alternative reasoning and over-validate incorrect solutions, precisely where adaptive tutoring matters most. These failure modes persist regardless of solution context or model capability, indicating architectural rather than informational limits. Accurate classification does not reliably translate into pedagogically actionable feedback, exposing a gap between diagnostic judgment and instructional effectiveness. LLMs thus cannot resolve the assistance dilemma without ITS-grounded mechanisms; they are better positioned within hybrid systems that combine LLM flexibility with ITS student modeling.

Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most

Algorithmic problem-solving is often taught through course-specific representations, conventions, and examples, making it important to provide instructional support that remains closely aligned with the curriculum while also being timely and available when students need help. In this paper, we present KITE (Knowledge-Informed Tutoring Engine), a Retrieval-Augmented Generation (RAG)-based intelligent tutoring system designed to serve as a classroom teaching assistant by supporting learning through dialogue in university AI courses. KITE uses an intent-aware Socratic response strategy to tailor its support to different student needs, responding with targeted hints, guiding questions, and progressive scaffolding intended to support independent reasoning. To keep responses aligned with course content, KITE uses a multimodal RAG pipeline that retrieves relevant information from lecture slides and course PDFs. We evaluate KITE using three forms of assessment: RAGAS-based metrics for response grounding and quality, human evaluation of pedagogical quality, and a simulated student pipeline in which a weaker language model interacts with KITE across two-turn dialogues and produces revised answers after receiving feedback. Preliminary results suggest that KITE produces contextually grounded and pedagogically appropriate responses, with especially promising performance on algorithm validation, debugging, and tracing tasks. This work contributes a replicable architecture and evaluation framework for intent-aware, curriculum-grounded tutoring systems in STEM education.

Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education

Grammatical error correction using large language models often suffers from the over-correction issue. To mitigate this, we propose a training-free inference method that performs edit-level majority voting over multiple candidates generated by a single model, without requiring model modifications or additional training. Across nine benchmarks covering English, Czech, German, Ukrainian, Korean, Hindi, and Romanian, the proposed method outperforms both greedy and MBR decoding in most cases. Moreover, it yields stable correction quality regardless of the instruction prompts used. The code will be released upon acceptance.

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Data-lean fine-tuning of models for evaluating teacher performance in a GenAI-led elicitation simulation

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Identifying Where Large Language Models Struggle in Answering Complex Questions

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