Josh Fisher is a writer, curriculum designer, and educational theorist whose work explores how human understanding emerges through shared practices, testimony, and social coordination. Drawing on philosophy, psychology, cultural evolution theory, law, and the history of education, he challenges the assumption that learning is primarily an individual, internal, or constructivist process, arguing instead for a transmission-centered view of knowledge grounded in culture, trust, and collective meaning.
He has more than three decades of experience designing instructional materials and curricula across mathematics, history, behavior, and the humanities. He has co-created interdisciplinary programs and frameworks that blend rigorous conceptual mastery with visual, tactile, and narrative design. He previously served as director of digital content and content engineer, AI, at Carnegie Learning, where he led the development of curriculum-integrated learning tools, including what has been described as one of the first AI tutors fully embedded within a curriculum, improving user satisfaction and learning outcomes. Earlier in his career, Fisher worked in editorial and content development roles with major educational publishers, including Houghton Mifflin. His work emphasizes restraint, structure, and memorability—not as constraints on thinking, but as the conditions that make deep understanding possible.
Fisher is also the author of research on AI-supported learning systems and instructional design and has presented at national conferences, including the National Council of Teachers of Mathematics. His writing is aimed at teachers, parents, and scholars who suspect that something essential has been lost in modern education—and who are interested in rebuilding learning around meaning that can be shared, carried, and passed on. He holds a bachelor of science in clinical psychology and music from Loyola Marymount University.While LLM-driven educational assistance has enormous potential in general education settings, there are significant challenges to developing safe, accurate, and reliable models for K-12 students, especially for mathematics students. General LLMs perform more poorly in mathematics relative to other domains, they often confabulate information, and they are in general insensitive to local instructional contexts in which most K-12 students find themselves.
Building off of LiveHint, which has been embedded within a particular curriculum in order to provide problem-specific assistance, scaffolding, and other high-quality instructional design principles, LiveHint AI extends the capabilities of LiveHint to provide dialog-based AI assistance while taking advantage of the safety, accuracy, and reliability afforded by a technology sensitive to students' local instructional contexts.Among the critical expectations that learning stakeholders have for K12 curriculum providers during the current global pandemic are that they provide: (a) support to rectify students' learning loss, (b) resources to help parents support student learning, and (c) greater access to open educational resources. We introduce a mobile-friendly, digital support for analog learning experiences called LiveHint, which currently supports students as they work on assignments in Carnegie Learning’s physical worktexts via a chatbot with access to thousands of context-sensitive hints.
In addition to expanding the number of courses supported by LiveHint, we discuss possibilities for expanding the scope of activities supported by LiveHint within Carnegie Learning’s existing content. We also lay out possibilities for expanding the approach beyond Carnegie Learning’s content to teacher-created artifacts (e.g., custom worksheets), hand-offs between instructional modalities, and potential research use-cases for data collected from such a platform.