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Classroom Design and Application of Art Design Education Based on Artificial Intelligence

Yawen Zhao & Licheng Gao

International Journal of Information Technology and Web Engineering2023https://doi.org/10.4018/ijitwe.334008article
ABDC C
Weight
0.47

What the paper says

With the development of society, the education goal of art and design education for students is constantly changing, focusing more on students' professional and practical abilities, but the traditional teaching methods cannot provide the conditions needed for teaching. In this article, the three parts of art design classroom atmosphere before, during and after class, and according to the characteristics and needs of each part into artificial intelligence technology to build a smart classroom system. The experimental results show that AI technologies can improve the efficiency of classroom management, and the information obtained from the emotional score data can help teachers understand and master the classroom teaching situation, and analyze the shortcomings of classroom teaching content based on the comprehensive classroom emotional score. In addition, AI technology can also enrich classroom content, diversify teaching modes, increase the interaction space between teachers and students and students, and improve the teaching quality of art design classrooms.

6 citations

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https://doi.org/https://doi.org/10.4018/ijitwe.334008

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@article{yawen2023,
  title        = {{Classroom Design and Application of Art Design Education Based on Artificial Intelligence}},
  author       = {Yawen Zhao & Licheng Gao},
  journal      = {International Journal of Information Technology and Web Engineering},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.4018/ijitwe.334008},
}

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Classroom Design and Application of Art Design Education Based on Artificial Intelligence

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Evidence weight

0.47

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.