Text Readability and Simplification
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Results indicate that readability metrics vary greatly across texts generated by LLMs (ChatGPT, Claude), even with consistent prompting; the use of LLMs to produce ORF passages for progress monitoring and high-stakes decision-making is not recommended at this time.
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Large Language Models (LLM) have the capacity to quickly produce passages of varying lengths, complexity, genres, and topics, which could be useful for teachers of mono- and multilingual students with reading-specific learning disabilities, specifically relating to monitoring oral reading fluency (ORF). This study addressed one primary research question with two sub-questions. Research Question 1: What is the quality of third-grade ORF passages generated by LLMs as compared to validated third-grade ORF in English and Spanish? Research Question 1a: What is the quantitative readability of LLM-generated passages? Research Question 1b: What is the conceptual diversity and co-occurrence of themes in said passages? Analysis was conducted using natural language processing tools, including Coh-Metrix, MultiAzterTest, and Leximancer. Results indicate that readability metrics vary greatly across texts generated by LLMs (ChatGPT, Claude), even with consistent prompting; the use of LLMs to produce ORF passages for progress monitoring and high-stakes decision-making is not recommended at this time.
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@article{Price2026Artificial,
title = {Artificial Intelligence to Support Oral Reading Fluency Assessment in English and Spanish for Students With Reading Disabilities},
author = {Madeline Day Price and R. Alex Smith and Alain Bengochea},
journal = {Learning Disability Quarterly},
year = {2026},
doi = {10.1177/07319487261449494},
url = {https://doi.org/10.1177/07319487261449494}
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