一个混合模型,结合环境分析和机器学习,用于预测AI教育质量
1Business With Financial Management, Northumbria University, Newcastle, NE18ST, England, UK. Cyndibabyr@gmail.com.
Scientific reports
|April 12, 2025
概括
人工智能 (AI) 在大学的整合需要战略管理. 本研究提出了人工智能教育的纠正措施和基于人工智能的模型来评估人工智能培训质量,显示了显著的准确性改进.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 管理科学 管理科学
背景情况:
- 在大学管理系统中对人工智能 (AI) 工具的需求日益增加.
- 为教育机构快速开发基于人工智能的平台.
研究的目的:
- 在大学教学和学习管理中提出人工智能教育的纠正措施.
- 引入一种新的方法来评估高等教育中人工智能培训计划的质量.
主要方法:
- 宏观环境分析 (外部,中间,内部) 用于采取纠正措施.
- 开发一个多层感知子 (MLP) 算法用于质量评估.
- 使用卡普奇人搜索算法 (CapSA) 进行神经网络重量调整.
主要成果:
- 拟议的人工智能模型在预测教育质量方面表现出高准确度 (CCC: 0.9611,SROCC: 0.9805,PLCC: 0.9731,R2: 0.9803).
- 对所有环境因素的纠正措施都会对大学人工智能教育发展产生积极影响.
- 提出的方法在评估人工智能培训计划方面表现优于其他模型.
结论:
- 拟议的人工智能驱动方法有效评估高等教育中人工智能培训计划的质量.
- 对校正措施的战略实施对于推动大学人工智能教育至关重要.
- 人工智能集成增强了大学管理和教育成果.
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