Among English child speech corpora, very few focus on oral reading. Existing resources such as the CMU Kids Corpus (Ellis Weismer et al., 2013) face limitations in the lack of grade-appropriate, curriculum-aligned reading texts, the annotation scope and quality, and most crucially, comprehensive annotation scheme for characterization of children’s reading errors. This study presents a multi-layered, fully manually annotated corpus of oral reading from 63 1st-3rd grade students residing in the U.S. who grow up hearing and speaking English. Additionally, we contribute methodologically rigorous annotation guidelines that categorize 10 reading error categories and 26 sublevel error labels. Using a digital reading platform supported by GPT-4o-mini (OpenAI, 2024), children read stories on topics of their own interest, while the system records their speech and logs their interactions with embedded digital supports. Each recording is paired with detailed demographic and educational metadata and subjected to linguistic annotations, including: (1) sentence- and word-level time alignment; (2) phonemic transcription; (3) reading errors.

This work proposes automated generation and psychometric scoring of Maze comprehension assessments using large language models (LLMs) and a multilevel item response theory (multilevel IRT) framework. Findings show reliable ability estimates across passages and items, offering scalable, curriculum-aligned formative assessment that reduces teacher workload and supports targeted reading instruction.