arXiv cs.AI· Ethan Mollick, Lilach Mollick, Ben Przystanski, LJ Ciccarelli, Natalie Bach, Laura Zarrow·· 1 小时前AI 评分31
Primer：一个以教师为中心的 AI 教育模拟创建框架
Primer: An AI Framework for Instructor-Centered Educational Simulation Creation
AI 导读
Primer 是一个开源、与具体 LLM 无关的 AI 系统，帮助教育者创建自适应教育模拟。它采用以执行教师教学意图为核心的多智能体架构，教师提供学科、课程和学生的知识，生成式 AI 为学生产出响应式练习变体。系统连同提示词和 GitHub 仓库开源，支持本地和开源模型，便于在不同治理体系下落地。
正文 · 原文
Abstract:Educational simulations are a well-established pedagogical technique that gives learners opportunities to apply knowledge, respond to challenges, and reflect on their performance. High-quality simulations, however, are difficult to build, requiring instructor input, scenario design, character development, scaffolding, technical implementation, and repeated revision. This paper presents Primer, an open-source, LLM-agnostic AI-supported system that helps educators create adaptive educational simulations. Educators contribute their knowledge of the subject, lesson, and students, while generative AI produces responsive variations of practice for students. Primer uses a multi-agent system centered around executing educator's instructional intent through the entire learning experience. We hope that by releasing Primer as an open-source system, including its prompts and GitHub repository, we provide a foundation for development and research on educator-in-the-loop AI authoring, multi-agent educational workflows, and simulation-based pedagogy. Further, by allowing the use of various AI models, including local and open source models, we provide the opportunity for implementation under varied governance regimes. Primer points toward a model of educational AI in which instructors can create locally grounded practice opportunities based on their own materials, goals, examples, and knowledge of students.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.16986 [cs.AI] |
| (or arXiv:2611.16986v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16986 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ethan Mollick [view email]
[v1]
Thu, 12 Nov 2026 15:50:46 UTC (1,106 KB)
来源：arXiv cs.AI · arxiv.org