Verifying complex real-world claims against diverse and potentially unreliable open-web sources requires balancing evidence comprehensiveness with rigorous source reliability. Current automated fact-checking approaches often fail to address this holistically, losing contextual dependencies and applying trust signals monolithically at the document level.We introduce ClaimCLAIRE, a multi-component fact-checking agent that integrates four key innovations: (1) iterative component-aware decomposition with exhaustiveness validation, (2) holistic evidence gathering using a ReAct agent that maintains cross-component semantic awareness, (3) trust-modulated retrieval that weights evidence by source credibility to mitigate the influence of misinformation, and (4) adaptive gap-filling to address recall bottlenecks in under-supported sub-claims.Evaluated on the AVeriTeC benchmark, ClaimCLAIRE achieves 84.27% accuracy and a macro-F1 of 0.806. Our systematic ablations demonstrate that while decomposition alone can degrade performance, its integration with trust-aware retrieval and adaptive gap-filling yields a pipeline where component-level verdicts, source trust ratings, and deterministic AND-logic synthesis together support transparent, accountable fact verification.

Prediction of item difficulty from its text content is of substantial interest for automated generation of test items. In this paper, we focus on the related problem of recovering IRT-based difficulty when the data originally reported item p-value (percent correct responses). We model this item difficulty using a repository of reading passages and student data from US standardized tests from New York and Texas for grades 3-8 spanning the years 2018-23. This repository is annotated with meta-data on (1) linguistic features of the reading items, (2) test features of the passage, and (3) context features. Using a penalized regression model, we achieve an RMSE of 0.59 (compared to a 0.92 baseline) and a 0.77 correlation between true and predicted difficulty. We further evaluated the impact of LLM embeddings (ModernBERT, BERT, and LLaMA), finding that they marginally improve performance but function effectively as standalone alternatives to traditional linguistic features. Finally, we demonstrate how this difficulty prediction model powers a publicly available, human-in-the-loop tool for generating reading comprehension items.

Most existing math benchmarks for LLMs focus on evaluating whether models produce correct solutions. In educational settings, however, it is equally important to understand whether LLMs grasp the pedagogical intent behind math problems, beyond simply arriving at the right answer. Tagging curriculum standards is challenging for the same reason: distinguishing fine-grained standards requires understanding subtle pedagogical distinctions. In this paper, we use the MathFish benchmark, which frames curriculum alignment as a multi-label prediction task over 385 Common Core State Standards, to evaluate a three-stage pipeline inspired by observed failure modes in retrieval and structural reasoning: curriculum-informed hard negatives (M1), a cross-encoder reranker (M2), and a ReAct agent paired with an LLM-as-a-judge critic (M3). We additionally evaluate a training-free alternative (A1) that combines hybrid sparse-dense retrieval with curriculum-graph reranking. M3 achieves 31.3% exact-match accuracy, approximately 6.5× higher than the three-shot GPT-4-Turbo baseline. Error analysis shows that, despite these improvements, the pipeline still struggles with missing predictions, grade-level misalignment, and sibling-standard confusion, reinforcing that precise curriculum alignment remains a fundamentally difficult problem in educational NLP.

This study presents a computational analysis to classify actionability in teacher feedback. We fine-tuned a RoBERTa model on 662 manually annotated feedback examples from West African classrooms, achieving strong classification performance (accuracy = 0.94, precision = 0.90, recall = 0.96, f1 = 0.93). This enabled classification of over 12,000 feedback instances. A comparison of linguistic features indicated that actionable feedback was associated with lower word count but higher readability, greater lexical diversity, and more modifier usage. These findings suggest that concise, accessible language with precise descriptive terms may be more actionable for teachers. Our results support focusing on clarity in teacher observation protocols while demonstrating the potential of computational approaches in analyzing educational feedback at scale.