迈向自动驾驶大学：大学能否通过代理AI实现自我驱动？
Toward Self-Driving Universities: Can Universities Drive Themselves with Agentic AI?
AI总结：
本研究提出通过代理AI实现高等教育机构的自主性框架，旨在自动化行政、学术和质量保证流程，减少教师文书工作时间，提升教育质量和研究生产力。
摘要：
代理AI和大语言模型（LLMs）的快速演进为高等教育机构带来了变革性机遇。本章引入了
英文摘要：
The rapid evolution of Agentic AI and large language models (LLMs) presents transformative opportunities for higher education institutions. This chapter introduces the concept of self-driving universities, a vision in which AI-enabled systems progressively automate administrative, academic, and quality-assurance processes through a staged autonomy model inspired by self-driving systems. We examine the current challenges facing traditional universities, including bureaucratic overload, fragmented information systems, and the disproportionate amount of time faculty spend on clerical tasks, which diverts effort away from timely feedback, curricular improvement, student mentorship, and research productivity. While prior AI-in-education research has focused primarily on learning support, tutoring, and analytics, there remains a lack of system-level frameworks for automating institutional quality assurance and accreditation workflows using agentic AI. We address this gap by presenting a framework for progressive automation, detailing how agentic AI can transform course design, assessment alignment, accreditation documentation, and institutional reporting. Through case studies of pilot deployments, we demonstrate that AI-assisted workflows can substantially reduce task completion times while enabling capabilities previously considered infeasible. The chapter's originality lies in introducing an autonomy-level framework for higher education operations grounded in agentic AI architectures rather than prompt-based LLM assistance. Finally, we discuss the critical infrastructure requirements, ethical considerations, and a strategic roadmap for universities to transition toward higher levels of academic autonomy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
