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This paper proposes a computational framework to operationalize optimal challenge based on Krashen&#39;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&#39;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&#39;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.

ACL 2026

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

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.

workshop paper

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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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.

Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction

Phonics stories are essential for early literacy, requiring controlled repetition of grapheme-phoneme (GP) patterns while maintaining simplicity, suitability, and quality. Generating such texts poses a challenge for large language models (LLMs), which must balance multiple phonological and pedagogical constraints. We evaluate six LLMs in a zero-shot setting across 16 prompt configurations, producing 8,688 outputs and 39,096 stories. Outputs are assessed using a multi-dimensional framework covering phonological alignment, developmental lexical appropriateness, readability, and narrative quality. Results show that while LLMs generate highly readable and age-appropriate text, they exhibit variability in phoneme control and narrative coherence. Prompt design significantly affects performance, revealing trade-offs between focusing on multiple phonological, linguistic, and pedagogical constraints, while model choice also leads to significant differences. These findings highlight the challenges of controllable educational text generation and the importance of prompt design in balancing instructional objectives. We release our prompts, generated stories, and evaluation framework to support future work in phonics-based story generation for early readers.

Zero Shot Phonics: Evaluating Constraint-Adherent Phonics Story Generation in Large Language Models

We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. Unlike typical intelligent tutoring systems that adapt questions or feedback, we treat reading as the primary intervention and use question answering only as an observation channel for Bayesian Knowledge Tracing (BKT). This enables controlled comparison of LLM-powered adaptive and non-adaptive reading policies before classroom deployment.
The framework links open educational content to a shared ontology of learning objectives and knowledge components, which is used to generate aligned reading--assessment pairs targeting one objective at a time. Simulated learners update their knowledge through a comprehension-and-memory process that models encoding, integration with prior knowledge, and misconception revision.
The learner model combines established theories of reading with constrained answer selection, ensuring responses are generated only from information the learner has plausibly retained. Together, these components provide an interpretable offline testbed for studying whether adaptive reading improves learning outcomes.

Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

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.

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

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.

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Effects of Varying LLM Access on Essay Writing Behavior

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