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Rizwaan Malik; Dorna Abdi; Rose Wang; Dorottya Demszky – Annenberg Institute for School Reform at Brown University, 2024
Despite well-designed curriculum materials, teachers often face challenges in their implementation due to diverse classroom needs. This paper investigates whether Large Language Models (LLMs) can support middle-school math teachers by helping create high-quality curriculum scaffolds, which we define as the adaptations and supplements teachers…
Descriptors: Middle School Mathematics, Scaffolding (Teaching Technique), Models, Mathematics Teachers
Lauren Covelli; Julia Kaufman; Umut Özek – Annenberg Institute for School Reform at Brown University, 2024
In this study, we highlight the differences in classroom-, teacher-, and school-level factors in 8th and 9th grade algebra experiences along socioeconomic and racial/ethnic lines using nationally representative survey data from the American Mathematics Educator Study. Several takeaways emerge from our analysis. First, we show that highest-poverty…
Descriptors: Algebra, Access to Education, Socioeconomic Influences, Racial Factors
Rose E. Wang; Ana T. Ribeiro; Carly D. Robinson; Susanna Loeb; Dorottya Demszky – Annenberg Institute for School Reform at Brown University, 2024
Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Tutors, Elementary School Students