Design and Implementation of IoT Data-Driven Intelligent Law Classroom Teaching System

Area of Science:

• Educational Technology
• Computer Science
• Law Education

Background:

• Traditional law classroom settings often lack interactive and personalized learning experiences.
• Integrating technology in education can enhance student engagement and learning outcomes.
• The need for data-driven insights to optimize pedagogical approaches is increasingly recognized.

Purpose of the Study:

• To design and implement an IoT data-driven intelligent law classroom teaching system.
• To create a personalized learning environment leveraging real-time data and AI.
• To empirically evaluate the impact of this system on teaching effectiveness in law courses.

Main Methods:

• Development of a comprehensive interactive system with teacher and student terminals, and a data management website.
• Utilizing various communication technologies including USB, serial, Zigbee, and WebSocket for seamless data flow.
• Integration of middleware with real-time databases and event management systems.
• Secondary development of a Turing robot for AI-assisted Q&A and personalized support.

Main Results:

• Successful deployment and testing of the IoT intelligent law classroom system in an experimental scenario.
• The system facilitates in-depth analysis of learning situations and processes.
• Empirical data from law courses demonstrate significant improvements in teaching effectiveness due to the personalized learning environment.

Conclusions:

• The developed IoT system effectively supports personalized learning in law education.
• AI-assisted teaching through personalized Q&A systems enhances the learning experience.
• Data-driven optimization of the learning environment leads to the most optimal learning effects and improved teaching outcomes.