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.

Globally, the incidence of depression and anxiety continues to rise, and the importance of mental health assessment scales as diagnostic tools has grown accordingly. Researchers are increasingly employing generative AI to produce large volumes of items and entire scales, which in turn elevates the costs of validating their reliability and validity. In this study, we used four open-weight LLMs to complete the GAD-7 and PHQ-9, varying prompts, sampling temperature, and dynamic contextual scenarios to emulate realistic human response patterns. Using multi-group confirmatory factor analysis, differential item functioning analyses, and other psychometric methods, we evaluate the factor structure of LLM-generated responses and assess measurement invariance relative to human responses. Our findings reveal a critical paradox: although open-weight LLMs exhibit exceptionally high internal consistency, they demonstrate severe structural mismatch and fail to achieve scalar measurement invariance against human baselines. Furthermore, pervasive differential item functioning and extreme prompt fragility indicate that these models rely on superficial, stereotype-driven semantic matching rather than simulating stable latent psychological dynamics.

Large Language Models (LLMs) are widely applied in educational practices, such as for generating children’s stories. However, the generated stories are often too difficult for children to read, and the operational cost of LLMs hinders their widespread adoption in educational settings. We used an existing expert-designed children’s reading curriculum and its corresponding generated stories from GPT-4o and Llama 3.3 70B to design different experiments for fine-tuning three 8B-parameter LLMs, which then generated new English reading stories that were subjected to quantitative and qualitative evaluation. Our method prioritizes controllability over scale, enabling educators to target reading levels and error patterns with a compact, affordable model. Our evaluation results show that with appropriate fine-tuning designs, children’s English reading stories generated by 8B LLMs perform better on difficulty-related metrics than those from zero-shot GPT-4o and Llama 3.3 70B, with almost no discernible safety issues. Such fine-tuned LLMs could be more broadly used by teachers, parents, and children in classrooms and at home to generate engaging English reading stories with children’s interests, controllable difficulty and safety.

In this study, we propose a novel paradigm for multi-modal low resource language dataset generation that eliminates dependency on existing parallel multi-modal datasets. Leveraging advances in large image-generation models, we introduce a systematic pipeline that transforms text-only parallel corpora into rich multi-modal translation datasets. We then validate the generated content through human evaluation. We design and implement a new MMT model framework suitable for our new generated dataset. The model contains a verification mechanism with a large language model to ensure consistency between visual content and textual translations. Experimental results across four African low-resource languages with less than 10k training corpus demonstrate significant improvements over NLLB baselines, with average gains of up to 9.8% in BLEU score and 4.3% in METEOR score. Our method shows particular effectiveness in correctly translating concrete objects and contextual elements, suggesting its potential for improving low-resource machine translation through visual grounding.