Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

August 5, 2024


Pages


DOI

Article

The Realistic Dilemmas and Possible Paths of Artificial Intelligence Enabling Teacher Education


Authors

Qin Zhou Affiliation:
School of Education and Music, Hezhou College, Hezhou, Guangxi, 542899, China.


Abstract

This paper explains the dilemma of artificial intelligence in relation to the development of teacher education based on the functional structure of artificial intelligence and the activity characteristics of teacher education. Then, after designing a survey questionnaire on the factors affecting the development of teacher education empowered by artificial intelligence and completing the reliability test, the paper collects initial data in the form of distributing questionnaires and analyzes in detail the least squares estimation of mean, variance, standard deviation, correlation coefficient, and regression coefficient needed in the process of analyzing the data to carry out the analysis of instances. The correlation coefficients of teacher training, professional development, policy support, resource allocation, teacher literacy, educational information technology behaviors, and AI-enabled teacher education development are 0.674 (0.003), 0.496 (0.001), 0.259 (0.009), 0.371 (0.008), 0.639 (0.004), and 0.325 (0.007). Their corresponding regression coefficients were 0.616 (t=59.852, P=0.003), 0.021 (t=0.018, P=0.007), 0.078 (t=5.668, P=0.005), 0.032 (t=3.282, P=0.009), 0.239 (t=29.734, P=0.008), 0.137 (t=5.406, P=0.001), indicating that these factors have a significant impact relationship on AI-enabled teacher education.


Keywords

Mean, Variance, Standard deviation, Regression coefficient, Teacher education, 00A35


Citation

Zhou, Q. (2024). The realistic dilemmas and possible paths of artificial intelligence enabling teacher education. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-2163

Published by: Engineering Journals

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