适应实验室导师 (ALM):一个人工智能驱动的物联网框架,用于实践工程教育中的实时个性化指导
Md Shakib Hasan1, Awais Ahmed2, Nouman Rasool1
1School of Electronic Information Engineering, China West Normal University, Nanchong 637002, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
一个创新的自适应实验室导师 (ALM) 使用人工智能和物联网在工程实验室实时提供个性化的反. 这种智能系统通过监控学生的进步和有效诊断错误来增强体验式学习.
科学领域:
- 工程教育 工程教育教育
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 在工程教育中,体验式学习至关重要.
- 目前的实验室设置在提供实时,个性化的学生反方面面临挑战.
- 需要智能系统来增强实验室体验.
研究的目的:
- 为智能和定制的工程实验室引入适应性实验室导师 (ALM) 系统.
- 开发一个实时多式联络传感器融合模型,用于自适应反.
- 为了创建一个新的智能实验室助理的蓝图.
主要方法:
- 结合人工智能 (AI),物联网 (IoT) 和传感器技术.
- 开发了一个实时多式联接传感器融合模型,使用电气测量 (电压,电流) 和符号组件值 (电阻).
- 在边缘设备上使用轻量级的双输入1D-CNN进行数据处理.
主要成果:
- 使用LTspice模拟开发了一个高保真性欧姆实验室模型.
- 经过训练的1D-CNN模型实现了93.3%的测试准确度.
- 证明了在实际环境中应用ALM系统的可行性.
结论:
- 该ALM系统可实现实时监控,快速错误诊断和工程实验室的自适应反.
- 在传统的智能辅导系统中,ALM通过物理感知和边缘AI提供了优势.
- 该ALM框架为未来的智能实验室助理提供了基础.
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