Adaptive math learning platform with AI-powered personalization and curriculum content
Mathspace builds an adaptive learning platform that personalizes math instruction through real-time algorithmic adjustment of problem difficulty and hints. The stack reveals a content-first engineering approach: React + Python + GraphQL for the platform, paired with LaTeX, Desmos, and GeoGebra for math-specific rendering, plus recent adoption of RAG and Gemini for LLM-based feedback. Active projects show heavy investment in video content production and curriculum alignment across multiple geographies—a scaling challenge reflected in their hiring velocity (16 roles posted in 30 days, mostly senior engineers and product).
Mathspace is an EdTech platform focused on personalizing mathematics instruction for middle and secondary school students. The product uses an adaptive algorithm to adjust problem difficulty and learning pathways in real-time based on student performance, paired with instructional scaffolding (hints, video lessons) mapped to each question. Teachers assign practice work and track progress; students experience guided problem-solving support. The company operates across multiple national curricula and integrates with LMS platforms including Google Classroom, Canvas, and Schoology. Mathspace was founded in 2010 and is privately held, headquartered in Sydney with ~51–200 employees.
Frontend: JavaScript, React, TypeScript. Backend: Python, Django, GraphQL. Math rendering: LaTeX, Desmos, GeoGebra. AI/ML: RAG, Gemini. Design: Figma, Adobe Creative Cloud. LMS integrations: Google Classroom, Canvas, Schoology.
LLM-based math feedback features, new video lesson content formats, gamified learning experiences, curriculum alignment for multiple countries (including New Zealand mathematics standards and IB curricula), and design system improvements for web and mobile apps.
Mathspace's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.