United States

Generative AI is increasingly used to accelerate assessment content development, yet its effectiveness for generating content used in complex assessment tasks for knowledge-rich domains such as medical education is unclear. This study evaluates automated LLM-supported workflows for generating patient-centered communication assessment items that allow students to practice their communication skills. We compared two content generation approaches—constrained linear and exploratory branching—each implemented with and without anchoring in vetted multiple-choice questions (MCQs). Ten subject-matter experts (SMEs) evaluated 80 communication items across six quality dimensions using structured rubrics. The constrained linear approach yielded better ratings than exploratory branching approaches, particularly for medical accuracy and alignment with learning objectives and patient-centered behaviors. MCQ anchoring did not improve medical accuracy. Only a minority of items met all criteria without requiring revision, and no items were unanimously approved by all SMEs. These findings underscore the importance of workflow design in LLM-supported assessment content generation, the continued need for human oversight, and the current limitations of automated content generation in medical education.

ACL 2026

Evaluating LLM Workflows for Generating Clinical Communication Assessment Items: A Comparative Study with Subject-Matter Experts

workflow design

medical education

assessment

human-in-the-loop

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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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.

Automated essay scoring (AES) research often relies on rank-based correlation metrics to validate analytic assessment. However, such metrics obscure both intrinsic intercorrelations among analytic dimensions that arise from the structure of writing proficiency itself and halo effects, whereby holistic impressions bleed into fine-grained component scores. As a result, high correlations may mask a system's true diagnostic behaviour. In this study, we propose a novel self-referential assessment evaluation framework that focuses on identifying intra-learner strengths and weaknesses rather than assessing inter-learner rankings. We conduct experiments on the publicly available ICNALE GRA, a uniquely dense second-language writing dataset annotated holistically and analytically by up to 80 trained raters. To obtain reliable reference scores, we apply two-facet Rasch modelling to calibrate rater severity and derive fair average scores across ten analytic aspects and holistic proficiency. We compare the analytic scoring performance of human operational raters and three large language models (LLMs) in a zero-shot setting. Our results show that LLMs tend to outperform single human raters in identifying relative weaknesses (negative feedback) across several proficiency aspects, while human raters remain stronger at identifying relative strengths (positive feedback). Overall, our findings highlight the limitations of rank-based evaluation for analytic assessment and demonstrate the value of intra-learner, profile-based methods for assessing and deploying LLMs in AES.

Towards Self-Referential Analytic Assessment: A Profile-Based Approach to L2 Writing Evaluation with LLMs

Lecture-style instruction is one of the most prevalent forms of learning in postsecondary education in the United States. Despite the factors that make lectures a convenient format, they tend to present few opportunities for meaningful engagement between students and the course materials being presented due to factors such as the overhead associated with interacting with large numbers of students. By utilizing large language models, we have created a pipeline built upon the ANONYMIZED classroom response system for processing student self-explanations produced during lectures using automatically generated knowledge components. While previous work using proprietary large language models has examined the basic functionality of this pipeline, this work more closely examines the consistency and quality of this pipeline using both a large closed-weight model and a smaller open-weight model, with or without RAG. We find that while there are small statistically significant differences in performance between the RAG conditions of each LLM, they are effectively comparable at this task. Additionally, the LLM-generated knowledge components are of higher quality when relevant course material is provided for RAG, although consistency is not improved. This indicates that both large closed-weight and smaller open-weight models show promise in this task, but fine-tuning may be necessary to improve performance further.

Assessing the Quality and Consistency of Automated Knowledge Component Generation using Instructor-generated Questions and LLMs

Pronunciation feedback in language learning depends on accurate detection of learner errors, but it is unclear whether modern ASR systems are suitable for this purpose. Their language models recover intended words rather than what was actually pronounced, systematically masking mispronunciations. This is a tendency we call \emph{intent bias}. By evaluating eight ASR systems spanning three architectures on two L2 English corpora, we find that overcorrection rate correlates inversely with word error rate. In other words, ASR systems with lower WER tend to mask more pronunciation errors. We propose surface-faithful reranking, an inference-time method that uses phoneme-level acoustic similarity to select N-best hypotheses closer to what the learner actually said. Without retraining or access to model internals, the method reduces the false acceptance rate of mispronunciations by 6.0 percentage points on L2-ARCTIC and 4.8 on Speechocean762. The improvement is consistent across age groups and first-language backgrounds, though substantial overcorrection remains, pointing to the need for pronunciation-aware ASR objectives.

Intent vs. Surface: Recovering Acoustic Realization from Modern ASR for Pronunciation Training

Research in AI applied to education increasingly relies on large-scale, high-quality datasets to support the development and evaluation of learning analytics and intelligent educational systems. Open educational resources provide a promising foundation, yet few datasets integrate structured instructional content with assessment materials in a multimodal form. In this study, we introduce a large-scale multimodal educational dataset (EduMUSE - \underline{Educ}ational \underline{M}ultimodal \underline{U}nderstanding \& \underline{S}olution Datas\underline{e}t) constructed from OpenStax undergraduate textbooks across multiple domains. The dataset integrates hierarchically structured instructional text, figures, exercises, and, when available, official solutions. For exercises with solutions, we introduce an automatic method that associates each exercise with a focused instructional subsection rather than entire textbook chapters, estimating subsection relevance via solution likelihood under candidate contexts using a vision–language model. We analyze the impact of contextualization on the behavior of vision–language models across different contexts. Results indicate that subsection-level instructional context has a measurable impact on model performance, with variation across model scales and task formulations. The dataset and code are released as open source at \url{https://anonymous.4open.science/r/EduMUSE-1EF8/} to support reproducible research in multimodal educational modeling and to facilitate generating similar datasets using our approach.

EduMUSE: A Multimodal Educational Dataset with Automatically Extracted Instructional Context

Automated short answer scoring (ASAS) is shifting from discriminative, fine-tuned models to large language models (LLMs) used in few-shot settings. This paradigm leverages LLMs' broad world knowledge and ease of deployment, but limited task-specific data may reduce alignment on complex scoring tasks. In particular, its impact on scoring partially correct responses that require nuanced interpretation remains underexplored.
We investigate the relationship between the degree of task-specific adaptation of different models and quality-conditioned scoring agreement. We compare three LLMs (GPT-5.2, GPT-4o, Claude Opus 4.5) in few-shot mode, a fine-tuned BERT-based encoder, and a human expert on two open-ended biology items, using several hundred student responses and ground truth scores provided by a biology education expert.
The results show that human–human agreement is highest and stable across the full quality spectrum. All AI models perform well on fully correct and fully incorrect responses, but exhibit substantial degradation on mid-range responses. This mid-range degradation is conditioned on task-specific adaptation: It is most severe in few-shot LLMs with few examples and decreases as task-specific data increases, with fine-tuned encoder models performing best.
This mid-range degradation may lead to inequitable evaluation of responses produced by students with developing understanding. Our findings highlight the importance of quality-conditioned fairness, with particular attention to mid-range responses.

Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation

Various aspects of the Elicited Imitation Test (EIT), a sentence repetition task for language assessment, can be automated, for example in terms of test administration or automatic scoring. It is potentially also possible to generate test items with Large Language Models (LLMs). This study investigates the potential of GPT-4o for item creation in the context of EIT, creating a parallel form to two popular and validated tests. We analysed the tests in terms of their linguistic and psychometric properties. While the items created by the LLM show some difference in grammatical structures when compared to human-written items, linguistic complexity results did not differ significantly between tests. Psychometric properties showed only minor differences. These findings lend support to the potential of Automatic Item Generation with LLMs in the context of sentence repetition tasks and might support the process of standardisation in SLA research and testing by enabling parallel test creation.

Using LLMs for item creation: Validating the potential of automatically generated sentence repetition test items for language assessment

Young athletes, parents and coaches are increasingly encountering training metrics derived from wearable technology, yet such metrics rarely provide sufficient information on their own. We present a rule-based data-to-text framework designed to promote data literacy in youth football, generating concise, stakeholder-specific summaries of training sessions. A rule layer maps duration-normalised indicators to structured facts about the session profile, internal intensity, speed exposure and movement dynamics. A large language model then verbalises these facts for coaches, parents or players. We compare direct generation from raw metrics, strictly rule-based generation and an extended RULES+CONTEXT implementation that combines validated facts with raw metrics and explicit threshold definitions. In the open-weight setting examined here, the selected models are also adapted using LoRA. When evaluated on 122 player-session records from a U15 environment, it was found that rule grounding improved reliability and audience adaptation compared to direct generation. A school-based expert evaluation also provided preliminary evidence that player-facing explanations in the extended setting can remain accurate, comprehensible, and useful in practice.

From Metrics to Meaning: Rule-Grounded LLM Explanations for Data Literacy in Youth Football

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.

Classification of Student Struggle in Mathematics

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.

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