Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

Authors

  • Jian Huang Nanjing Institute of Technology
  • Gang Shen Changzhou University
  • Xiping Ren Zhejiang Normal University University of Rostock

DOI:

https://doi.org/10.3991/ijet.v16i05.20287

Keywords:

artificial intelligence technology, paradigm shift, teaching design, teaching efficiency

Abstract


The influence of artificial intelligence technology on teaching design is explored to improve teaching efficiency. First, artificial intelligence is introduced and its impacts on teaching design are analyzed. Second, the connotation of the paradigm of teaching design and the paradigm shift for teaching design are explored using the paradigm shift analysis framework. Finally, the changes in teaching design under artificial intelligence are analyzed, and the impacts of artificial intelligence on teaching activities are investigated. The results show that the application of artificial intelligence technology has led to different levels of change in the six elements of teaching design, including teaching objectives, service objects (teachers and students), teaching content, teaching media, teaching environment, and teaching evaluation. The connotation and paradigm shift of the teaching design are introduced from the four elements based on the artificial intelligence technology. It is found that artificial intelligence technology can enhance the learning ability and cognitive ability of students to a certain extent while improving the teaching efficiency and learning efficiency. The investigation proves that the teaching design based on artificial intelligence technology can be applied to teaching activities, thereby improving the learning efficiency of students and the teaching efficiency of teachers.

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Published

2021-03-16

How to Cite

Huang, J., Shen, G., & Ren, X. (2021). Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology. International Journal of Emerging Technologies in Learning (iJET), 16(05), pp. 73–86. https://doi.org/10.3991/ijet.v16i05.20287

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Section

Papers