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Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
We conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers' profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet; Llama; Gemini Pro 1.5; ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, physics) to accommodate four different profiles…
Descriptors: Natural Language Processing, Profiles, Individual Differences, Semantics
Crossley, Scott A.; Skalicky, Stephen; Dascalu, Mihai; McNamara, Danielle S.; Kyle, Kristopher – Discourse Processes: A multidisciplinary journal, 2017
Research has identified a number of linguistic features that influence the reading comprehension of young readers; yet, less is known about whether and how these findings extend to adult readers. This study examines text comprehension, processing, and familiarity judgment provided by adult readers using a number of different approaches (i.e.,…
Descriptors: Reading Processes, Reading Comprehension, Readability, Adults
Sheehan, Kathleen M.; Flor, Michael; Napolitano, Diane; Ramineni, Chaitanya – ETS Research Report Series, 2015
This paper considers whether the sources of linguistic complexity presented within texts targeted at 1st-grade readers have increased, decreased, or held steady over the 52-year period from 1962 to 2013. A collection of more than 450 texts is examined. All texts were selected from Grade 1 textbooks published by Scott Foresman during the targeted…
Descriptors: Text Structure, Content Analysis, Grade 1, Elementary School Students
McNamara, Danielle S.; Crossley, Scott A.; McCarthy, Philip M. – Written Communication, 2010
In this study, a corpus of expert-graded essays, based on a standardized scoring rubric, is computationally evaluated so as to distinguish the differences between those essays that were rated as high and those rated as low. The automated tool, Coh-Metrix, is used to examine the degree to which high- and low-proficiency essays can be predicted by…
Descriptors: Essays, Undergraduate Students, Educational Quality, Computational Linguistics

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