Edhivo platform scales college assessments with fast AI feedback
Edhivo: An AI-Native Platform for Scalable Assessment and Instant Feedback in Higher Education
Computers and Society
Summary
Testing students in college can be hard because making good questions and grading detailed answers takes lots of work and time. The authors created Edhivo, a computer system that uses two AI models: one that helps teachers make questions from their materials, and another that grades students' written answers quickly using detailed rubrics. Their system also lets instructors review AI feedback to stay in control. When tested in a real college class, Edhivo helped make and grade many assessments faster, giving students quicker and more useful feedback.
What this means in practice
- •For higher education administrators: Use Edhivo to reduce instructor workload and speed up grading across full courses with both multiple-choice and written assessments.
- •For corporate training teams: Implement Edhivo to automatically generate training assessments and provide rapid, detailed feedback on employee open-ended responses.
Authors
Moraish Kapoor, Jiawei Zhou
Abstract
Scaling assessment in higher education faces two intertwined bottlenecks: the cognitive burden of authoring assessments and the inability to semantically evaluate open-ended responses, which together create a persistent feedback gap that hinders student learning outcomes. To address these challenges, we present Edhivo, an AI-native pedagogical platform that addresses both bottlenecks through a vertically integrated system for content delivery, assessment, and reporting. Edhivo employs a Dual-LLM pipeline: one model generates context-aware assessments for both multiple-choice and open-ended questions, from instructor-provided materials, while a second model performs multi-criteria, rubric-based evaluation of student responses, enabling near-instant qualitative feedback at multiple hierarchical levels. All AI interactions are source-grounded to reduce hallucination risk, and a Human-in-the-Loop review process preserves instructor authority. We demonstrate Edhivo's efficacy through a pilot deployment at Stony Brook University in a real classroom environment in 2026 spring, where it supports high-frequency assessment creation and evaluation for a cohort of 36 students throughout a full semester, significantly reducing authoring effort and feedback latency while enabling novel pedagogical designs centered on active learning.