Evaluation method of the impact of AI technology application on the quality of smart teaching

Aiming at the problem of high data dimension in the evaluation of the impact of artificial intelligence technology on the quality of wisdom teaching, an evaluation method based on principal component analysis (PCA) was proposed. First, the evaluation index is selected and the evaluation index system is constructed. Then the principal component analysis method is improved by using covariance and sensitivity to reduce the data dimension. Finally, BP neural network (BPNN) is improved by adjusting the steepness factor and weight value, and the evaluation model of the influence of wisdom teaching quality is established. The experimental results show that the evaluation error of this method can reach 0.02, the satisfaction of the respondents is 96%, the goal realisation coefficient is 0.99, and the evaluation effect is good.

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Publication Details

Journal
International Journal of Continuing Engineering Education and Life-Long Learning
Published
2025-01-01
DOI
https://doi.org/10.1504/ijceell.2025.143794
Citations
1
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
2.76
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article

Evaluation method of the impact of AI technology application on the quality of smart teaching

Lin Zeng
1 citations
International Journal of Continuing Engineering Education and Life-Long Learning
AI and HR Technologies
2.76
article

Evaluation method of the impact of AI technology application on the quality of smart teaching

Lin Zeng
article en
1 citations

Abstract

Aiming at the problem of high data dimension in the evaluation of the impact of artificial intelligence technology on the quality of wisdom teaching, an evaluation method based on principal component analysis (PCA) was proposed. First, the evaluation index is selected and the evaluation index system is constructed. Then the principal component analysis method is improved by using covariance and sensitivity to reduce the data dimension. Finally, BP neural network (BPNN) is improved by adjusting the steepness factor and weight value, and the evaluation model of the influence of wisdom teaching quality is established. The experimental results show that the evaluation error of this method can reach 0.02, the satisfaction of the respondents is 96%, the goal realisation coefficient is 0.99, and the evaluation effect is good.

International Journal of Continuing Engineering Education and Life-Long LearningVol. 35(1/2)
Nanchang Hangkong University (CN)
Openalex Percentile: Top 13%
AI and HR Technologies
2.76
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