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Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – International Journal of Artificial Intelligence in Education, 2025
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – Grantee Submission, 2024
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Wang, Zuowei; O'Reilly, Tenaha; Sabatini, John; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2021
We compared high school students' performance in a traditional comprehension assessment requiring them to identify key information and draw inferences from single texts, and a scenario-based assessment (SBA) requiring them to integrate, evaluate and apply information across multiple sources. Both assessments focused on a non-academic topic.…
Descriptors: Comparative Analysis, High School Students, Inferences, Reading Tests

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