arXiv cs.CY· Moraish Kapoor, Jiawei Zhou·· 11 小时前AI 评分33
Edhivo：面向高等教育规模化评估与即时反馈的 AI 原生平台
Edhivo: An AI-Native Platform for Scalable Assessment and Instant Feedback in Higher Education
AI 导读
Edhivo 是一个面向高等教育的 AI 原生教学平台，采用 Dual-LLM 流水线：一个模型基于教师提供的材料生成选择题与开放式题目，另一个模型按评分标准对学生的开放式回答做多维度评估，提供近乎即时的定性反馈。
正文
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.
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.36654 [cs.CY] |
| (or arXiv:2610.36654v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36654 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiawei Zhou [view email]
[v1]
Tue, 27 Oct 2026 05:17:06 UTC (1,537 KB)
来源：arXiv cs.CY · arxiv.org