Frizzle

Frizzle is an essential operating system for math classrooms that uses computer vision and LLMs to transform how teachers interact with student work. It reads handwritten math from paper with 97% accuracy, turning physical assignments into real-time, actionable data. The product is built for K-12 math teachers, instructional coaches, and school districts who need granular insights into student understanding without changing the fundamental workflow of students writing on paper. Teachers simply snap a photograph of a stack of papers or run them through a copier, and within hours, Frizzle returns standards-level formative analytics aligned to CCSS and other frameworks. This eliminates the 10-15 hours per week teachers spend grading by hand. The confidence-interval system flags uncertain grades for human review, ensuring accuracy. Frizzle is already live in over 30 schools and districts, including a college math pilot at Vanderbilt and ASU. It provides a live nervous system for math classrooms, showing exactly which standards each class and student has mastered, allowing for immediate intervention instead of waiting on spring assessments. For districts, this means cutting math screen time without losing the classroom-level data they need for informed decision-making.

Computer Vision Handwriting Recognition

Frizzle reads any handwriting style including print, cursive, scribbled, and sideways text. Its model was trained on 1.4 million pages of real K-12 student work, allowing it to recognize the messy, partial, and unconventional ways students actually write math. It understands multiple solution paths simultaneously, meaning three students using three different approaches to solve the same problem all receive appropriate credit. This feature is critical for teachers who need accurate grading without forcing students into rigid answer formats.

Step-Level Misconception Detection

Frizzle does not simply mark answers right or wrong. It parses every step of student work to identify exactly where thinking went off track. The system has 147 named misconceptions across K-12 math, each mapped to specific standards. It includes prerequisite tracing, which means it can identify when a 7th-grade error is actually rooted in a 4th-grade gap. Every flag links back to the exact stroke on the page, showing teachers precisely what needs reteaching.

Real-Time Class and District Dashboards

Live dashboards update automatically as papers are read, showing who is stuck, which mistakes are spreading across the classroom, and what to teach tomorrow. For schools and districts, Frizzle aggregates anonymized signal across periods, grades, and buildings. This allows coaches and administrators to see where to invest resources, what curricula are actually working, and which concepts need reteaching at scale. Equity dashboards spot performance gaps the moment they emerge.

Privacy-First Infrastructure

Student work never trains Frizzle's model. Data remains the property of the school or district. The platform is fully FERPA and COPPA compliant with SOC 2 Type II annual audits. All data is encrypted end-to-end using AES-256 at rest and TLS in transit. Privacy is not an afterthought but the foundation of the system, ensuring schools can adopt Frizzle without compromising their legal and ethical obligations to protect student information.

Eliminating Weekend Grading for Math Teachers

Math teachers spend 10-15 hours per week grading handwritten assignments. Frizzle eliminates this burden entirely. A teacher photographs a stack of 28 papers and within approximately 8 minutes, every page is read, graded, and analyzed. The system recognizes partial credit, multiple solution paths, and step-level errors. Teachers reclaim their weekends and focus their energy on instruction and intervention rather than mechanical grading.

Standards-Level Formative Assessment for Instructional Coaches

Instructional coaches need specific, actionable data to guide conversations with teachers. Frizzle provides granular standards-level analytics showing exactly which CCSS standards each class and student has mastered. Instead of generic coaching conversations about broad topics, coaches can identify specific misconceptions spreading through a classroom and work with teachers on targeted reteaching strategies. This transforms coaching from subjective observation to data-driven collaboration.

Early Intervention for At-Risk Students

Prerequisite tracing in Frizzle identifies when a student's current error is actually a gap from previous grade levels. For example, a 7th-grade student struggling with distributive property may have an underlying 4th-grade multiplication gap. Frizzle surfaces these connections immediately, allowing teachers and intervention specialists to address root causes rather than symptoms. This prevents small gaps from compounding into major learning deficits over time.

District-Wide Curriculum and Equity Analysis

School districts need to know which curricula are working and where performance gaps exist across different student populations. Frizzle aggregates anonymized data across all classrooms, buildings, and grades. Equity dashboards spot performance gaps the moment they emerge, allowing districts to allocate resources effectively. The system is curriculum-agnostic, working with Eureka, Illustrative, Saxon, and other frameworks, so districts can compare effectiveness without changing their instructional materials.

How does Frizzle handle different handwriting styles and multiple solution paths?

Frizzle's model was trained on 1.4 million pages of real K-12 student work, covering print, cursive, scribbled, and sideways handwriting. It recognizes multiple solution paths simultaneously. For example, if three students solve a quadratic equation using factoring, square roots, and the quadratic formula respectively, Frizzle credits all three approaches as valid. The system understands that different brains arrive at answers differently and accommodates that diversity.

Is Frizzle compliant with student privacy regulations like FERPA and COPPA?

Yes, absolutely. Frizzle is built with privacy as its foundation, not an afterthought. Student work never trains the model. All data remains the property of the school or district. The platform is fully FERPA and COPPA compliant with SOC 2 Type II audits conducted annually. All data is encrypted end-to-end using AES-256 at rest and TLS in transit, ensuring complete protection of student information.

What does the confidence-interval system mean for grading accuracy?

Frizzle grades handwritten math at 97% accuracy. When the system encounters work it is less certain about, it flags those papers for human review using a confidence-interval system. This ensures that teachers only need to personally review the small percentage of papers that fall below the confidence threshold. The result is that teachers trust the automated grades while maintaining quality control over edge cases.

How long does it take to process a stack of papers?

The entire workflow takes approximately 8 minutes per class. Snapping a photograph of a stack of papers takes about 30 seconds. Frizzle then processes the images, reads every page, identifies misconceptions, and updates live dashboards within the remaining time. Teachers can photograph papers during a passing period and have actionable data before their next class begins.