Aligning test items to content standards is a critical step in test development to collect validity evidence 3 based on content. Item alignment has typically been conducted by human experts, but this judgmental process can be subjective and time-consuming. This study investigated the performance of fine-tuned small language models (SLMs) for automated item alignment using data from a large-scale standardized reading and writing test for college admissions. Different SLMs were trained for both domain and skill alignment. The model performance was evaluated using precision, recall, accuracy, weighted F1 score, and Cohen’s kappa on two test sets. The impact of input data types and training sample sizes was also explored. Results showed that including more textual inputs led to better performance gains than increasing sample size. For comparison, classic supervised machine learning classifiers were trained on multilingual-E5 embedding. Fine-tuned SLMs consistently outperformed these models, particularly for fine-grained skill alignment. To better understand model classifications, semantic similarity analyses including cosine similarity, Kullback-Leibler divergence of embedding distributions, and two-dimension projections of item embedding revealed that certain skills in the two test datasets were semantically too close, providing evidence for the observed misclassification patterns.

This study investigates methods for item difficulty modeling in large-scale assessments using both small and large language models. We introduce novel data augmentation strategies, including on-the-fly augmentation and distribution balancing, that surpass benchmark performances, demonstrating their effectiveness in mitigating data imbalance and improving model performance. Our results showed that fine-tuned small language models such as BERT and RoBERTa yielded lower root mean squared error than the first-place winning model in the BEA 2024 Shared Task competition, whereas domain-specific models like BioClinicalBERT and PubMedBERT did not provide significant improvements due to distributional gaps. Majority voting among small language models enhanced prediction accuracy, reinforcing the benefits of ensemble learning. Large language models (LLMs), such as GPT-4, exhibited strong generalization capabilities but struggled with item difficulty prediction, likely due to limited training data and the absence of explicit difficulty-related context. Chain-of-thought prompting and rationale generation approaches were explored but did not yield substantial improvements, suggesting that additional training data or more sophisticated reasoning techniques may be necessary. Embedding-based methods, particularly using NV-Embed-v2, showed promise but did not outperform our best augmentation strategies, indicating that capturing nuanced difficulty-related features remains a challenge.