Analysis on Academic Benchmark Design and Teaching Method Improvement under Artificial Intelligence Robot Technology

Authors

  • Xiaojuan Cao Hunan Mechanical&electrical Polytechnic
  • Zheng Li Hunan Post and Telecommunication College
  • Ruyi Zhang Hunan Aerospace Yuanwang Science&Technology Co., Ltd

DOI:

https://doi.org/10.3991/ijet.v16i05.20295

Keywords:

artificial intelligence, robot education, academic benchmark design, educational teaching methods

Abstract


To allow robots to better complete teaching tasks in the education field, based on artificial intelligence robot technology, the academic benchmark is designed for information technology teaching. First, LEGO MINDSTORMS EV3 is introduced for statement, which is developed through information technology courses based on robot education. Then, according to the sensor teaching in robot education, the teaching contents are designed for the information technology course. After that, the teaching method is improved based on sensor teaching for the information technology course. Finally, the feasibility is evaluated of the existing of teaching resources, and the effect of information technology teaching is analyzed through a questionnaire survey. The results show that more than 80% of students are interested in robot teaching, and more than 70% of students can master relevant theoretical knowledge in practical operation, which proves that robot education can arouse students' interest in information technology courses. In conclusion, the learning methods proposed in this study can enable students to better master related theories, and improve students' operation and innovation abilities. The study provides a reference for the application of artificial intelligence robot technology in the field of education.

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Published

2021-03-16

How to Cite

Cao, X., Li, Z., & Zhang, R. (2021). Analysis on Academic Benchmark Design and Teaching Method Improvement under Artificial Intelligence Robot Technology. International Journal of Emerging Technologies in Learning (iJET), 16(05), pp. 58–72. https://doi.org/10.3991/ijet.v16i05.20295

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Section

Papers