Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scaffold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.

IBM's Watson Education, an artificial intelligence platform that uses data trends to provide insights to teachers and students, is partnering with Edmodo and Scholastic in an effort meant to personalize learning. With Edmodo, a K-12 network for students, teachers, administrators and parents, IBM is collaborating to develop a personalized content recommendation engine that can be integrated within Edmodo's existing social education platform. For Scholastic, a children's publishing, education and media company, the plan is to use the Watson platform to recommend nonfiction content that aligns with curriculum standards and has multiple articles and media for students' skill and interest levels. "Our goal is to use AI to improve learning outcomes," and to personalize content for learners, said Chalapathy Neti, vice president of IBM Watson Education, in an interview. He explained that he refers to AI as "augmented intelligence" rather than the more typical "artificial intelligence," because the way people are thinking about the abbreviated "AI" has produced "a little bit of angst in terms of machines replacing humans."