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Session Type: Symposium
Artificial intelligence tools are transforming literacy education—simplifying texts, generating decodable readers and science text, and creating vocabulary assessments. While these applications promise to support equity and access, research on their effectiveness remains limited. This symposium presents four pioneering studies examining how AI-powered tools perform when measured against proven literacy practices. Findings reveal that while AI can offload basic tasks, such as adjusting text complexity or generating test items, these tools can also unintentionally compromise comprehension and content quality. The studies highlight both opportunities and risks, emphasizing the need for human expertise to guide AI use. Together, they offer a model for responsible integration, while underscoring the need for continued research to ensure that AI advances equity in literacy education.
Simplifying Middle-Grade Texts Using AI Extensions: Impacts on Text Vocabulary and Cohesion - Heidi Anne E. Mesmer, Virginia Tech; Elfrieda H. Hiebert, TextProject
Benchmarking Large Language Models on Generating Expository Elementary Science Text - Guy Trainin, University of Nebraska - Lincoln; Azadeh Hassani, University of Nebraska - Lincoln; Fatemeh Ashrafabadi,, University of Nebraska - Lincoln; Qizhen Deng, Boise State University
AI-Generated Decodable Texts: Problems, Promise, and Directions Supporting Beginning Reading Objectives - Laura S. Tortorelli, Michigan State University; John Z. Strong, University at Buffalo - SUNY; Tanya M. Christ, East Carolina University
Towards Scalable Vocabulary Assessment: Pilot Evidence for Reliable AI-Generated Items - Joshua Fahey Lawrence, University of Oslo; Rebecca Silverman, Stanford University; Jason D Yeatman, Stanford University; Ã…ste Hagen, University of Oslo; Jasmine Tran, University of California - Irvine