Ednius provides an AI‑driven grading platform that lets educators upload rubrics and automatically evaluates student submissions, generating personalized feedback and draft grades in minutes. The system supports both objective and subjective answers across humanities and STEM, with a human‑in‑the‑loop review step to ensure accuracy and consistency, and integrates with existing LMS workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Higher education instructors spend dozens of hours grading large classes, often delivering delayed, generic feedback that fails to support timely student learning. Inconsistent grading standards across multiple teaching assistants further reduce fairness and effectiveness.
Solution
Ednius offers an AI-powered grading platform that automatically evaluates student submissions in minutes while generating personalized, context-aware feedback for each response. Educators upload their rubrics, and the system applies natural‑language understanding to assess both objective and subjective answers across disciplines, from essays to equations. The AI highlights specific errors, suggests improvements, and produces a draft grade that educators review and approve before release, ensuring human oversight. By standardizing rubric application, the platform delivers consistent grading, reduces grading time by up to 70%, and provides students with actionable feedback while the material is still fresh.
Target Audience
Primary customers are university professors, teaching assistants, and academic departments that manage large undergraduate or graduate courses requiring frequent, detailed assessment of written or numerical assignments.
Features
- AI engine that interprets rubric criteria and grades both handwritten and typed submissions across humanities and STEM subjects
- Generation of unique, constructive feedback for each student, pinpointing mistakes and offering improvement suggestions
- Human‑in‑the‑loop workflow: educators validate AI grades and feedback before they are sent to students
- Consistency enforcement to apply the same grading standards to every submission, eliminating grader fatigue bias
- Secure handling of student work with strict privacy controls; data is never used to train models or sold
- Integration-ready output that can be exported to LMS platforms or used in existing grading workflows