RavenGrader is an AI-powered grading platform that grades entire classes of handwritten exams at once while providing personalized feedback to each student. It reads messy handwriting, diagrams, and partial-credit work, then integrates directly with Canvas to deliver question-level analytics and common-mistake insights. The platform helps instructors save time while giving students detailed, actionable feedback on every problem.
Funding
Funding not disclosed
Founders
Product
Problem
Instructors of large classes face the time-consuming burden of manually grading stacks of handwritten exams, often sacrificing the depth and personalization of feedback due to time constraints. This leads to inconsistent grading across submissions and delayed feedback that reduces its learning value for students.
Solution
RavenGrader is an AI-powered grading platform that processes an entire class's handwritten exams in parallel, using a single click to evaluate every submission against the instructor's rubric. The system is designed to understand real student work, including messy handwriting, hand-drawn diagrams, circuits, and non-linear scratch-work reasoning, ensuring that partial credit is awarded for correct steps even when the final answer is wrong. It provides each student with clear, problem-specific feedback on what they did well, where they went wrong, and how to improve, delivered while the exam is still fresh in their minds. For instructors, RavenGrader offers question-by-question analytics, score distributions, and identification of the most common mistakes, enabling targeted teaching interventions. The platform is FERPA-compliant and integrates directly with Canvas, allowing for a seamless workflow from exam upload to gradebook sync.
Target Audience
Primary users are university and college instructors teaching large courses, particularly in STEM fields like mathematics, engineering, and the physical sciences, who grade handwritten exams and want to provide consistent, detailed feedback without sacrificing their evenings.
Features
- Batch upload and parallel grading of an entire class's exam stack in one action
- AI recognition of messy handwriting, hand-drawn diagrams, circuits, and non-linear scratch-work reasoning
- Rubric-based evaluation that awards partial credit for correct steps in incorrect final answers
- Automated per-student feedback on every problem, including strengths, errors, and improvement suggestions
- Question-level analytics showing average scores, score distributions, and difficulty metrics
- Common-mistake identification per problem to highlight class-wide learning gaps
- FERPA-compliant architecture with native Canvas integration for gradebook synchronization