In the United States, English learners (EL) often do not have the academic language proficiency, literacy skills, cultural background, and content knowledge necessary to succeed in kindergarten through 12th grade classrooms. This leads to large achievement gaps. Also, classroom texts are often riddled with linguistically unfamiliar elements, including: unfamiliar vocabulary, idioms, complex phrases or sentences, morphologically complex words, and unfamiliar discourse relations. Lack of familiarity with linguistic elements may result in gaps in a learner’s comprehension of key content. It is not feasible for teachers to develop additional curriculum for the needs of all ELs in a classroom (who often come from culturally and linguistically diverse backgrounds.) However, it is feasible for teachers to develop instructional scaffolding (support) that helps ELs and can be used with all students. To develop effective scaffolding, teachers need to be able to reliably identify linguistic features in texts that could interfere with content comprehension. Language MuseSM is a web-based application designed to support teachers in the identification of linguistic features in texts and in the development of linguistically focused instructional scaffolding. With regard to system itself, we will discuss (a) the system’s motivation, (b) the system’s linguistic feedback and instructional authoring components, which are driven by natural language processing, and (c) the system’s infrastructure for capturing teachers’ system use. In addition, we will also discuss preliminary pilot study findings with three teacher professional development programs. These findings suggest that exposure to Language Muse’s linguistic feedback can support teachers in the development of lesson plan scaffolds designed to address language learning needs.