# 5G and Artificial Intelligence Interactive Technology Applied in Preschool Education Courses

Authors: Lijuan Liao; Fanjun Gu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

To explore the application of artificial intelligence interaction technology in preschool education practice courses, preschool education, 5G wireless network, artificial intelligence interaction technology, and other relevant theories are integrated, questionnaire survey is adopted as the research method, and six-year-old kindergarten students in a city of China are taken as the research objects. Adaptive optimization suggestions are put forward from the preschool children practice curriculum learning situation, cooperation and exchange situation, learning effect, and learning attitude research. The results show that in terms of learning of practical courses, students’ cognition of learning has changed after the implementation of wisdom teaching, but the average value of someP values < 0.05 is low in the real classroom adaptation stage. In the exchange and cooperation of practical courses, it is found that wisdom teaching has the greatest influence on helping other students solve problems, with a specific P of 0.009. In terms of learning effect and learning attitude, the effect is significant after the introduction of artificial intelligence technology, with an average of 3.56 before the introduction and 4.01 after the introduction. It means that to a certain extent, preschool children can realize the role and significance of wisdom classroom teaching, which plays a certain role in teaching practice. Therefore, research on the application of artificial intelligence interaction technology in preschool education practice courses under 5G wireless network environment provides a new theoretical basis and optimization direction for preschool education in the future.


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## Body

## 1. Introduction

With the continuous improvement of scientific level, the emergence of a 5G wireless network environment, and the maturity of artificial intelligence interactive technology, more and more researchers are associating it with education. In his congratulatory message to the International Conference on Artificial Intelligence and Education held from May 16 to 18, 2019, President Jinping Xi stressed that China attaches great importance to AI education. Artificial intelligence has a profound impact on education. The deep integration of artificial intelligence and education is promoting educational innovation. The advantages of artificial intelligence are fully brought into play, and the development is accelerated with equal education for everyone, education for everyone’s life, and more open and flexible education [1].Couch, vice president of Education of Apple, introduced his research experience, explained the reasons for the comprehensive transformation of traditional teaching, and introduced how to apply new teaching concepts and practices and high-tech achievements to higher-level learning. In the future, artificial intelligence should be applied to education to enable students to experience and feel the new technology, to liberate their minds and stimulate their potential for innovation [2]. David conducted research and analysis on the teaching effectiveness of smart classroom, aiming to investigate primary school students’ cognition of smart classroom teaching. The researchers analyzed the cognitive scores of learners in a random sample of 100 students from two schools in India. The results showed that traditional teaching strategies, teachers’ knowledge, presentation skills, use of blackboard, explanation of examples, questioning, consolidation, and feedback were much better than smart classroom teaching [3]. The theory of multiple intelligences proposed by Gardner, a famous American educational psychologist, has exerted a profound influence on the field of early childhood education in China from educational concept to program design and implementation [4]. In terms of future classroom design and development research, Chen proposed the integrated teaching activity design mode of preclass, in-class, and after-class under the multidisciplinary theory from the aspects of environmental design and activities. This model promoted the learning and development of learners as the center and effectively improved the teaching effect [5].In this context, starting from the 5G wireless network environment, the research on artificial intelligence interaction technology is carried out. Then, questionnaire survey is implemented; the study situation, cooperation and communication, learning effect, and learning attitude of preschool children practice courses are discussed; and the corresponding opinions and suggestions are put forward. This work is aimed at providing reference for future research on the application of artificial intelligence interaction technology in preschool education practice courses. The organizational structure of the article is shown in Figure1.Figure 1
Article organization structure.


## 2. Methods

### 2.1. Preschool Education

Preschool education refers to the education carried out by parents and preschool teachers to stimulate children’s brain systematically and scientifically by using various methods and objects, so as to gradually improve the functions of various parts of the brain. Preschool education has broad and narrow senses. The broad sense of preschool education referred to the care and education implemented by children from birth to the age of 6 [6]. In a narrow sense, preschool education referred to the care and education of children aged 3 ~ 6. In addition, preschool special education is also an important part of preschool education, teaching children with disabilities (sensory disabilities, physical disabilities, language disabilities, infirmity and multiple disabilities, and intellectual disabilities) from birth to 6 years old, children with excellent qualifications, and students with a weak academic foundation [7]. The specific contents involved in preschool education are shown in Figure 2.Figure 2
Main contents of preschool education.Figure2 shows that the main content of preschool education currently consists of five parts, namely, language, health, science, society, and art. In the field of language, parents and teachers are required to create education suitable for children’s age and individual development as early as possible. The latter four parts are aimed at promoting the good development of preschool children’s morality, intelligence, physique, beauty, and labor.Preschool education is one of the important contents of preschool pedagogy and a part of the scientific system of preschool pedagogy. Childhood is not only the basic stage of intellectual development in life but also the fastest developing period. Appropriate and correct preschool education played a great role in children’s intelligence and its future development [8]. The formation and development of gifted children are all related to appropriate and correct preschool education, especially in intelligence. Preschool education is a multifaceted training process [9]. Attention to the mental health of preschool children should not be underestimated. The mental development of children in this age group is extremely immature and needs the active guidance of parents and preschool teachers [10]. Figure 3 illustrates the basic principles of preschool education.Figure 3
Basic principles of preschool education.


### 2.2. Artificial Intelligence Interaction Technology

Artificial intelligence technology mainly includes robots, language recognition, image recognition, natural language processing, and expert systems. Artificial neural networks are used to connect people and machines, so that machines can respond to various instructions like people [11]. Artificial neural network simulates neuron nodes through perceptron, thus achieving the ability to simulate the human brain neural network and store various information [12, 13]. Figure 4 illustrates the specific workflow of perceptron simulating neurons.Figure 4
Perceptron simulation neuron workflow.From Figure4, the artificial neural network can perceive effective information through various perceptrons and store and collect information data first by using its own corresponding algorithms or technologies. Then, the neural network can simulate or transform it similar to the human brain and finally present it vividly.The perceptron inputs the featurea1,a2,⋯,an into the calculation unit, w represents the weight, and the weighted sum obtains the following equation:
(1)sum=a1w1+a2w2+⋯+anwn.The summed value is input into the activate functionf, and the resulting output is the result of the perceptron.
(2)t=fsum.A nonlinear function is selected as the activation function of the perceptron, such asSigmoid function and tanh function. x represents the sample vector, w represents the weight, and e is a constant.
(3)Sigmoidw,x=11+e−wx,tanhx=ex−e−xex+e−x.The nonlinear activation function,Sigmoid function, and tanh function make the perceptron a nonlinear model. The neural network model is generally composed of multiple perceptrons. Neurons enter from the input layer and output layer after passing through the hidden layer. Figure 5 reveals the specific feedforward neural network structure.Figure 5
Feedforward neural network structure.In daily practical operations, there may be multiple hidden layers existing between the input layer and the output layer,i=1,2,⋯,nq,j=1,2,⋯,nq−1, q represents the number of layers of the network, the qth layer can form a hyperplane of nq−1 dimension, and the transformation relationship between the input layer and the output layer is shown in
(4)Oiq=∑j=0nq−1wijqxjq−1,xjq−1=eijq,wiOq=1.Equation (5) can be obtained from the above equation.
(5)xiq−1=fOiq=1Oiq≥0,−1,Oiq<0.In the neural network, the eigenvalues of the input layer are passed forward layer by layer through the nonlinear transformation of the weighted sum activation function. Eventually, the output layer is obtained. The algorithm of back calculating the parameters corresponding to the eigenvalues of each previous layer by comparing the output with the training label is called back propagation algorithm. The derivation process of back propagation algorithm is mainly the process of minimizing the loss function by using gradient descent algorithm. Now, it is supposed that the model output isO, the real result is t, and k represents the coefficient. The loss function is expressed as the following equation:
(6)Ew=12∑k∈outputst−o2.For each weightwji in the network, its derivative is calculated and expressed in
(7)∂E∂wji=∂E∂netj∂netj∂wji=∂E∂netj∂∂wji∑i=0nwjixji=∂E∂netjxji.Ifj is the output layer unit of the network, equation (7) is the derivative of netj.
(8)∂E∂netj=∂E∂oj∂oj∂netj.From the above equation, the following equations can be obtained.(9)∂E∂oj=∂∂oj12∑k∈outputstk−ok2=−tj−oj,(10)∂oj∂netj=∂onetj∂netj=oj1−oj.Thus, from equations (9) and (10), equation (11) can be obtained.
(11)∂E∂netj=∂E∂oj∂oj∂netj=−tj−ojoj1−oj.For convenience of expression, the above equation (11) can also be recorded as follows:
(12)ςi=−∂E∂netj=tj−ojoj1−oj.In the gradient descent algorithm, the fastest direction of parameter update is towards the negative gradient of the loss function, so the parameter change can be obtained as follows:(13)Δwji=−η∂E∂wji=−η∂E∂netjxji=ηtj−ojoj1−ojxji,whereη is called learning rate, or step size. If j is a hidden unit in the network, w represents the value in the hidden unit.Artificial intelligence interaction technology refers to the intelligent behavior reflected through dialogue and interaction. Usually, intelligent systems interact with users or the environment and realize learning and modeling in the interaction. The main research areas at present are shown in Figure6.Figure 6
Research field of artificial intelligence interaction technology.Artificial intelligence interaction technology mainly includes three aspects of research, namely, pan questioning system, task- or goal-based dialogue system, and open-domain chat system [14]. Pan question answering system is aimed at finding precise information from structured (such as knowledge base and tables) and unstructured (such as documents) to answer user questions and belongs to a single-round dialogue system [15]. Task- or goal-based dialogue systems need to achieve a specific task or goal through interaction, such as various intelligent assistants, ticket booking, and meal ordering systems [16–18]. The chat system in the open field, such as Microsoft Xiaobing, focuses on chatting with users, emotional communication, and escort, which is an important foundation and prerequisite for social robots to enter thousands of households. As a broader concept of human-computer interaction, interactive systems are not only based on natural language but also comprehensively apply multimedia information such as images and voices. It enables machines to understand their own environment and display intelligent behaviors that fit the situation [19, 20]. Through comparison of artificial intelligence interactive technology with deep learning and machine learning technology, it is found that artificial intelligence interactive technology is giving machines the intelligence of people, and machine learning is one of the methods to realize artificial intelligence interactive technology. Deep learning technology is another technology for realizing machine learning, and the three complement each other. The convolutional neural network model in deep learning can be constructed by imitating the visual perception mechanism of biology and can perform supervised learning and unsupervised learning. Figure 7 shows the specific division results of artificial intelligence interaction technology in preschool education application scenarios.Figure 7
Application scenario division of artificial intelligence interaction technology in preschool education.After analysis of the research content of neural network, three different methods are found to be involved.(I) The convolutional neural network (CNN) system based on convolutional operation, namely, CNN: this model includes convolution computation and computes feedforward neural network with deep structure. It includes four parts: the input layer, convolution layer, pooling layer, and fully connected layer, which is one of the representative algorithms of deep learning. (II) Self-coding neural networks based on multilayer neurons, including autoencoder and sparse coding which have attracted widespread attention in recent years. (III) Deep belief network (DBN), which are pretrained in the way of multilayer self-coding neural network and further optimized the weights of neural network by combining the identification information. The model can be used for both unsupervised and supervised learning. The adopted feedforward neural network model in the convolutional neural network algorithm can effectively evaluate and control the preschool teaching classroom with artificial intelligence interaction technology by using the four-layer structure, and the multilayer structure makes the analysis result more scientific.


### 2.3. Proposed Research Methods

#### 2.3.1. Action Research Method

It refers to a research mode in which educational practitioners, in accordance with certain operating procedures, comprehensively apply a variety of research methods and technologies and take solving practical problems in education as the primary goal in a natural and real education environment [21]. Therefore, the proposed educational activity design model is applied to the practical courses of preschool education specialty. According to the analysis of the practical results, children’s learning effect, learning feelings, and recognition degree of intelligent classroom are understood, and then, the teaching design is effective [22].


#### 2.3.2. Literature Method

It refers to searching-related master’s and doctoral papers and periodicals in CNKI, Baidu Library, and other databases around the keywords of artificial intelligence, artificial intelligence + education, smart classroom, and teaching design, and then collect and sort out a large number of literature, thereby summarizing the research status of artificial intelligence + education and smart classroom, finding innovation points, and determining the topic selection and research significance of this research [23, 24].


#### 2.3.3. Questionnaire Survey Method

Through the questionnaire survey method, questions of different dimensions are designed to be investigated before and after the implementation of teaching design. Through the comparative analysis of the investigation and statistics before and after, the actual situation of the application of the teaching design model of practical courses for preschool education specialty is obtained, and its advantages and disadvantages are analyzed [25–27].


### 2.4. Experimental Process and Method

(I)
Research objectivesThe application of artificial intelligence interactive technology in the practice courses of preschool education majors is explored under the 5G network environment, and suggestions are given to improve the quality of preschool teaching strategies through the data analysis of the survey results, so as to help preschool children increase their interest in learning and improve the cultivation of autonomous learning ability.(II)
Research objectSix-year-old kindergarten students in a city of China are recruited as the research object, and 200 students are randomly selected to give questionnaires to them. To ensure the scientific nature of the questionnaire survey, the questionnaire is discussed with relevant experts before the questionnaire is issued, and the unreasonable parts are revised in the questionnaire survey. To ensure the corresponding recovery rate, 163 copies are distributed and recovered on the spot, with a recovery rate of 81.5%. The effective recovery rate is 140 copies and 85.89%.To make the results of the questionnaire more accurate, the validity test of systematic error variance as shown in equation (14) is introduced.
(14)α=KK−11−∑i=1KσYi2σX2.In the above equation,α represents the coefficient, K represents the quantity, X represents the dependent variable, and Y represents the independent variable. The higher the reliability coefficient, the higher the reliability between variables, indicating the higher the degree of internal consistency between variables. When α is less than or equal to 0.3, the variable is not credible. When α is between 0.3 and 0.4, the variable is initially credible. The variables are slightly credible when α is between 0.4 and 0.5, and the variables are credible when the alpha is between 0.5 and 0.7. When α is between 0.7 and 0.9, the variables are credible. When α is greater than 0.9, the variable is very credible. Through equation (14), the reliability of the internal consistency of the responses to the questionnaire is tested, and the calculated result is 0.86, indicating that the reliability of the questionnaire is relatively high.


## 2.1. Preschool Education

Preschool education refers to the education carried out by parents and preschool teachers to stimulate children’s brain systematically and scientifically by using various methods and objects, so as to gradually improve the functions of various parts of the brain. Preschool education has broad and narrow senses. The broad sense of preschool education referred to the care and education implemented by children from birth to the age of 6 [6]. In a narrow sense, preschool education referred to the care and education of children aged 3 ~ 6. In addition, preschool special education is also an important part of preschool education, teaching children with disabilities (sensory disabilities, physical disabilities, language disabilities, infirmity and multiple disabilities, and intellectual disabilities) from birth to 6 years old, children with excellent qualifications, and students with a weak academic foundation [7]. The specific contents involved in preschool education are shown in Figure 2.Figure 2
Main contents of preschool education.Figure2 shows that the main content of preschool education currently consists of five parts, namely, language, health, science, society, and art. In the field of language, parents and teachers are required to create education suitable for children’s age and individual development as early as possible. The latter four parts are aimed at promoting the good development of preschool children’s morality, intelligence, physique, beauty, and labor.Preschool education is one of the important contents of preschool pedagogy and a part of the scientific system of preschool pedagogy. Childhood is not only the basic stage of intellectual development in life but also the fastest developing period. Appropriate and correct preschool education played a great role in children’s intelligence and its future development [8]. The formation and development of gifted children are all related to appropriate and correct preschool education, especially in intelligence. Preschool education is a multifaceted training process [9]. Attention to the mental health of preschool children should not be underestimated. The mental development of children in this age group is extremely immature and needs the active guidance of parents and preschool teachers [10]. Figure 3 illustrates the basic principles of preschool education.Figure 3
Basic principles of preschool education.


## 2.2. Artificial Intelligence Interaction Technology

Artificial intelligence technology mainly includes robots, language recognition, image recognition, natural language processing, and expert systems. Artificial neural networks are used to connect people and machines, so that machines can respond to various instructions like people [11]. Artificial neural network simulates neuron nodes through perceptron, thus achieving the ability to simulate the human brain neural network and store various information [12, 13]. Figure 4 illustrates the specific workflow of perceptron simulating neurons.Figure 4
Perceptron simulation neuron workflow.From Figure4, the artificial neural network can perceive effective information through various perceptrons and store and collect information data first by using its own corresponding algorithms or technologies. Then, the neural network can simulate or transform it similar to the human brain and finally present it vividly.The perceptron inputs the featurea1,a2,⋯,an into the calculation unit, w represents the weight, and the weighted sum obtains the following equation:
(1)sum=a1w1+a2w2+⋯+anwn.The summed value is input into the activate functionf, and the resulting output is the result of the perceptron.
(2)t=fsum.A nonlinear function is selected as the activation function of the perceptron, such asSigmoid function and tanh function. x represents the sample vector, w represents the weight, and e is a constant.
(3)Sigmoidw,x=11+e−wx,tanhx=ex−e−xex+e−x.The nonlinear activation function,Sigmoid function, and tanh function make the perceptron a nonlinear model. The neural network model is generally composed of multiple perceptrons. Neurons enter from the input layer and output layer after passing through the hidden layer. Figure 5 reveals the specific feedforward neural network structure.Figure 5
Feedforward neural network structure.In daily practical operations, there may be multiple hidden layers existing between the input layer and the output layer,i=1,2,⋯,nq,j=1,2,⋯,nq−1, q represents the number of layers of the network, the qth layer can form a hyperplane of nq−1 dimension, and the transformation relationship between the input layer and the output layer is shown in
(4)Oiq=∑j=0nq−1wijqxjq−1,xjq−1=eijq,wiOq=1.Equation (5) can be obtained from the above equation.
(5)xiq−1=fOiq=1Oiq≥0,−1,Oiq<0.In the neural network, the eigenvalues of the input layer are passed forward layer by layer through the nonlinear transformation of the weighted sum activation function. Eventually, the output layer is obtained. The algorithm of back calculating the parameters corresponding to the eigenvalues of each previous layer by comparing the output with the training label is called back propagation algorithm. The derivation process of back propagation algorithm is mainly the process of minimizing the loss function by using gradient descent algorithm. Now, it is supposed that the model output isO, the real result is t, and k represents the coefficient. The loss function is expressed as the following equation:
(6)Ew=12∑k∈outputst−o2.For each weightwji in the network, its derivative is calculated and expressed in
(7)∂E∂wji=∂E∂netj∂netj∂wji=∂E∂netj∂∂wji∑i=0nwjixji=∂E∂netjxji.Ifj is the output layer unit of the network, equation (7) is the derivative of netj.
(8)∂E∂netj=∂E∂oj∂oj∂netj.From the above equation, the following equations can be obtained.(9)∂E∂oj=∂∂oj12∑k∈outputstk−ok2=−tj−oj,(10)∂oj∂netj=∂onetj∂netj=oj1−oj.Thus, from equations (9) and (10), equation (11) can be obtained.
(11)∂E∂netj=∂E∂oj∂oj∂netj=−tj−ojoj1−oj.For convenience of expression, the above equation (11) can also be recorded as follows:
(12)ςi=−∂E∂netj=tj−ojoj1−oj.In the gradient descent algorithm, the fastest direction of parameter update is towards the negative gradient of the loss function, so the parameter change can be obtained as follows:(13)Δwji=−η∂E∂wji=−η∂E∂netjxji=ηtj−ojoj1−ojxji,whereη is called learning rate, or step size. If j is a hidden unit in the network, w represents the value in the hidden unit.Artificial intelligence interaction technology refers to the intelligent behavior reflected through dialogue and interaction. Usually, intelligent systems interact with users or the environment and realize learning and modeling in the interaction. The main research areas at present are shown in Figure6.Figure 6
Research field of artificial intelligence interaction technology.Artificial intelligence interaction technology mainly includes three aspects of research, namely, pan questioning system, task- or goal-based dialogue system, and open-domain chat system [14]. Pan question answering system is aimed at finding precise information from structured (such as knowledge base and tables) and unstructured (such as documents) to answer user questions and belongs to a single-round dialogue system [15]. Task- or goal-based dialogue systems need to achieve a specific task or goal through interaction, such as various intelligent assistants, ticket booking, and meal ordering systems [16–18]. The chat system in the open field, such as Microsoft Xiaobing, focuses on chatting with users, emotional communication, and escort, which is an important foundation and prerequisite for social robots to enter thousands of households. As a broader concept of human-computer interaction, interactive systems are not only based on natural language but also comprehensively apply multimedia information such as images and voices. It enables machines to understand their own environment and display intelligent behaviors that fit the situation [19, 20]. Through comparison of artificial intelligence interactive technology with deep learning and machine learning technology, it is found that artificial intelligence interactive technology is giving machines the intelligence of people, and machine learning is one of the methods to realize artificial intelligence interactive technology. Deep learning technology is another technology for realizing machine learning, and the three complement each other. The convolutional neural network model in deep learning can be constructed by imitating the visual perception mechanism of biology and can perform supervised learning and unsupervised learning. Figure 7 shows the specific division results of artificial intelligence interaction technology in preschool education application scenarios.Figure 7
Application scenario division of artificial intelligence interaction technology in preschool education.After analysis of the research content of neural network, three different methods are found to be involved.(I) The convolutional neural network (CNN) system based on convolutional operation, namely, CNN: this model includes convolution computation and computes feedforward neural network with deep structure. It includes four parts: the input layer, convolution layer, pooling layer, and fully connected layer, which is one of the representative algorithms of deep learning. (II) Self-coding neural networks based on multilayer neurons, including autoencoder and sparse coding which have attracted widespread attention in recent years. (III) Deep belief network (DBN), which are pretrained in the way of multilayer self-coding neural network and further optimized the weights of neural network by combining the identification information. The model can be used for both unsupervised and supervised learning. The adopted feedforward neural network model in the convolutional neural network algorithm can effectively evaluate and control the preschool teaching classroom with artificial intelligence interaction technology by using the four-layer structure, and the multilayer structure makes the analysis result more scientific.


## 2.3. Proposed Research Methods

### 2.3.1. Action Research Method

It refers to a research mode in which educational practitioners, in accordance with certain operating procedures, comprehensively apply a variety of research methods and technologies and take solving practical problems in education as the primary goal in a natural and real education environment [21]. Therefore, the proposed educational activity design model is applied to the practical courses of preschool education specialty. According to the analysis of the practical results, children’s learning effect, learning feelings, and recognition degree of intelligent classroom are understood, and then, the teaching design is effective [22].


### 2.3.2. Literature Method

It refers to searching-related master’s and doctoral papers and periodicals in CNKI, Baidu Library, and other databases around the keywords of artificial intelligence, artificial intelligence + education, smart classroom, and teaching design, and then collect and sort out a large number of literature, thereby summarizing the research status of artificial intelligence + education and smart classroom, finding innovation points, and determining the topic selection and research significance of this research [23, 24].


### 2.3.3. Questionnaire Survey Method

Through the questionnaire survey method, questions of different dimensions are designed to be investigated before and after the implementation of teaching design. Through the comparative analysis of the investigation and statistics before and after, the actual situation of the application of the teaching design model of practical courses for preschool education specialty is obtained, and its advantages and disadvantages are analyzed [25–27].


## 2.3.1. Action Research Method

It refers to a research mode in which educational practitioners, in accordance with certain operating procedures, comprehensively apply a variety of research methods and technologies and take solving practical problems in education as the primary goal in a natural and real education environment [21]. Therefore, the proposed educational activity design model is applied to the practical courses of preschool education specialty. According to the analysis of the practical results, children’s learning effect, learning feelings, and recognition degree of intelligent classroom are understood, and then, the teaching design is effective [22].


## 2.3.2. Literature Method

It refers to searching-related master’s and doctoral papers and periodicals in CNKI, Baidu Library, and other databases around the keywords of artificial intelligence, artificial intelligence + education, smart classroom, and teaching design, and then collect and sort out a large number of literature, thereby summarizing the research status of artificial intelligence + education and smart classroom, finding innovation points, and determining the topic selection and research significance of this research [23, 24].


## 2.3.3. Questionnaire Survey Method

Through the questionnaire survey method, questions of different dimensions are designed to be investigated before and after the implementation of teaching design. Through the comparative analysis of the investigation and statistics before and after, the actual situation of the application of the teaching design model of practical courses for preschool education specialty is obtained, and its advantages and disadvantages are analyzed [25–27].


## 2.4. Experimental Process and Method

(I)
Research objectivesThe application of artificial intelligence interactive technology in the practice courses of preschool education majors is explored under the 5G network environment, and suggestions are given to improve the quality of preschool teaching strategies through the data analysis of the survey results, so as to help preschool children increase their interest in learning and improve the cultivation of autonomous learning ability.(II)
Research objectSix-year-old kindergarten students in a city of China are recruited as the research object, and 200 students are randomly selected to give questionnaires to them. To ensure the scientific nature of the questionnaire survey, the questionnaire is discussed with relevant experts before the questionnaire is issued, and the unreasonable parts are revised in the questionnaire survey. To ensure the corresponding recovery rate, 163 copies are distributed and recovered on the spot, with a recovery rate of 81.5%. The effective recovery rate is 140 copies and 85.89%.To make the results of the questionnaire more accurate, the validity test of systematic error variance as shown in equation (14) is introduced.
(14)α=KK−11−∑i=1KσYi2σX2.In the above equation,α represents the coefficient, K represents the quantity, X represents the dependent variable, and Y represents the independent variable. The higher the reliability coefficient, the higher the reliability between variables, indicating the higher the degree of internal consistency between variables. When α is less than or equal to 0.3, the variable is not credible. When α is between 0.3 and 0.4, the variable is initially credible. The variables are slightly credible when α is between 0.4 and 0.5, and the variables are credible when the alpha is between 0.5 and 0.7. When α is between 0.7 and 0.9, the variables are credible. When α is greater than 0.9, the variable is very credible. Through equation (14), the reliability of the internal consistency of the responses to the questionnaire is tested, and the calculated result is 0.86, indicating that the reliability of the questionnaire is relatively high.


## 3. Results

### 3.1. Application of Artificial Intelligence Technology in Preschool Education Curriculum

To explore the practical courses of preschool education, the artificial intelligence interaction technology in a 5G wireless network environment is adopted and analyzed. The specific results are shown in Figure8 and Table 1.Figure 8
Survey results of preschool children’s learning of practical courses.Table 1
The results of the survey on the learning situation of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueBetter adapt to teaching methods2.882.980.072It is not difficult to adapt3.123.350.032Interesting teaching methods3.013.510.014Teaching methods are necessary2.853.220.021Figure8 reveals that the learning situation of preschool children before and after the introduction of artificial intelligence technology is mainly carried out from four aspects, namely, “it can better adapt to the current teaching method,” “it is not difficult to adapt,” “this teaching method is interesting,” and “this teaching method is very necessary.” The average value of “can better adapt to the current teaching methods” before the introduction of artificial intelligence technology is 2.88, and the average value after the introduction is 2.98. The average value of “little adaptation difficulty” before the introduction of artificial intelligence technology is 3.12, and the average value after the introduction is 3.35. The average value of “this teaching method is interesting” before the introduction of artificial intelligence technology is 3.01, and the average value after the introduction is 3.51. The average value of “this teaching method is very necessary” before the introduction of artificial intelligence technology is 2.85, and the average value after the introduction is 3.22. In addition, the P values of the above four aspects are 0.072, 0.032, 0.014, and 0.021, and the P value < 0.05 of the latter three items shows that there is a significant difference between the results before and after the investigation. In other words, students’ cognition of learning has changed after the implementation of teaching, but the average value of preschool children in the real adaptation stage is low. The reason for this phenomenon is related to the characteristics of preschool children. Although they show great enthusiasm for various things, it still takes some time to adapt.In addition, the exchanges of classroom cooperation are shown in Figure9 and Table 2.Figure 9
Survey on cooperation and exchange of preschool children’s practical courses.Table 2
Survey on cooperation and exchange of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueParticipate in group communication3.383.550.069Share your thoughts4.124.450.059Help other students solve problems4.224.890.009Harmonious group atmosphere3.854.050.013Figure9 shows that the average value of “can participate in group communication” before the introduction of artificial intelligence technology is 3.38, and the average value after the introduction is 3.55. The average value of “being able to share your ideas” before the introduction of artificial intelligence technology is 4.12, and the average value after the introduction is 4.54. The average value of “can help other students solve problems” before the introduction of artificial intelligence technology is 4.22, and the average value after the introduction is 4.89. The average value of “pleasant and harmonious group atmosphere” before the introduction of artificial intelligence technology is 3.85, and the average value after the introduction is 4.05. In these four studies, the P values are 0.069, 0.059, 0.009, and 0.013, of which the lowest P value is “can help other students solve problems,” indicating that the introduction of artificial intelligence interaction technology into practical course cooperation and communication has the greatest impact on helping other students solve problems.At the same time, the learning effect of preschool practice courses is investigated, and the specific results are shown in Figure10 and Table 3.Figure 10
Survey results of preschool children’s practical classroom learning effect.Table 3
A questionnaire on the learning effect of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueParticipate in group communication3.383.550.069Share your thoughts4.124.450.059Help other students solve problems4.224.890.009Harmonious group atmosphere3.854.050.013Figure10 indicates that the investigation on the classroom learning effect mainly includes “knowing what I have learned,” “learning all the knowledge explained by the teacher in the classroom,” “being able to tell my parents what I have learned,” and “smart classroom makes me understand knowledge faster.” Among them, the average value of “knowing what you have learned” before the introduction of artificial intelligence technology is 3.67, the average value after the introduction is 3.12, and the P value is 0.155. Before the introduction of artificial intelligence technology, the average value of “the knowledge explained by the teacher in the classroom has been learned” is 4.01. After the introduction of artificial intelligence technology, the average value is 4.48 and the P value is 0.02. The average value of “being able to tell their parents what they have learned” before the introduction of artificial intelligence technology is 3.76, the average value after the introduction is 3.98, and the P value is 0.038. The average value of “smart classroom makes me understand knowledge faster” before the introduction of artificial intelligence technology is 3.16, the average value after the introduction is 3.99, and the P value is 0.005. The P value of “knowing what you have learned” is relatively low. The effect on this item before and after the introduction of artificial intelligence technology is not significant, but there are significant differences in the latter three, indicating that students gain more in class.Finally, the study attitude of preschool children is investigated. The specific results are shown in Figure11 and Table 4.Figure 11
Survey results of preschool children’s learning attitude towards practical courses.Table 4
Learning attitude survey of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueVery interested in wisdom class3.73.810.178Like teaching activities3.543.970.028Satisfied with the way to solve the problem3.083.050.325Love learning more3.123.630.017Improve learning effect3.564.010.011Figure11 shows that the average value of “very interested in intelligent classroom teaching” before the introduction of artificial intelligence technology is 3.7, the average value after the introduction is 3.81, and the P value is 0.178. The average value of “like the current form of teaching activities” before the introduction of artificial intelligence technology is 3.54, the average value after the introduction is 3.97, and the P value is 0.028. Before the introduction of artificial intelligence technology, the average value of “very satisfied with the way of classroom group communication and cooperation to solve problems” is 3.08. After the introduction, the average value is 3.05 and P value is 0.325. The average value of “smart classroom makes me love learning more” before the introduction of artificial intelligence technology is 3.12, the average value after the introduction is 3.63, and the P value is 0.017. The average value of “intelligent classroom teaching improves my learning effect” before the introduction of artificial intelligence technology is 3.56, the average value after the introduction is 4.01, and the P value is 0.011. It can be found that after the implementation of smart classroom, obvious results have been achieved, which makes preschool children realize the role and significance of smart classroom teaching to a certain extent. Thus, it plays a certain role in teaching practice.To sum up, through the method of questionnaire survey, the learning, cooperation and communication, and the learning effect and learning attitude of preschool children’s practical courses are analyzed. It is found that artificial intelligence interactive technology can promote students’ enthusiasm and autonomous learning ability in practical courses to varying degrees. It can be seen that the application of artificial intelligence interaction technology in the practical curriculum of preschool education is the right choice. Feng et al. believed that children of families with different socioeconomic statuses have significant differences in the following aspects, including “concentration,” “imagination and creation,” “sense of purpose,” “ability to resist setbacks,” and “initiative.” Therefore, families and society should pay more attention to preschool children [28]. Sun et al. believed that parents have a high cognitive level and can reasonably arrange parenting activities. With diverse family cultural resources and peaceful family atmosphere, the family usually has an authoritative and democratic parenting style. There are various forms of family education and abundant activities. Children growing up in such an environment tend to have relatively high preschool cognitive level [29]. After comparison with the concepts of experts and scholars mentioned above, it is found that it is logical and feasible to use advanced artificial intelligence interactive technology to study the practical courses of preschool education major from different perspectives, follow the steps of theory first and method first, and gradually draw corresponding conclusions.


### 3.2. Strategy Optimization of Intelligent Classroom Teaching Activities in Preschool Education

With the continuous improvement of social and economic level, the quality of preschool education in China is also improving, the strength of teachers is growing, educational resources are becoming richer and richer, and educational methods are more reasonable and scientific. In order to combine various teaching methods with the current advanced technology, several design strategies are proposed for the intelligent teaching activities of preschool education practice courses. (I) Preschool teachers should fully apply the platform of smart cloud to the classroom. Before practical application, teachers can conduct research through their own experience or cooperate with students. Teachers should actively mobilize the ability of preschool children to obtain external information, so as to truly and effectively cultivate students’ skills. (II) Children from birth to the age of six are full of infinite curiosity about real life. At this time, both parents and teachers should actively encourage children to contact all kinds of things. On the one hand, they can cultivate their ability of autonomous learning and cognition. On the other hand, they can timely realize their real interests and advantages through this process. In this way, students can start from an early age. (III) Teachers’ reasonable planning of class time is also very important. Each class needs to be divided into several parts. How much time is left for students to think independently, how much time is left for students to cooperate to complete tasks, how much time is left for students to share, communicate, and discuss. Only by reasonably allocating class time can teaching achieve the preset effect. (IV) Interest is the best teacher. Therefore, how to mobilize students’ learning enthusiasm and interest is a problem that all teachers should think and choose. Teachers of preschool education can show in class with “physical object + image + video” instead of blindly playing animated films and videos. Although videos can mobilize children’s enthusiasm, the form is too single, which is easy to cause students’ fatigue and deviate from the real educational goal. (V) Classroom evaluation is the result analysis and summary of a class. It is a positive feedback mechanism. Classroom evaluation is not a summary made by the teacher but needs to be completed by students and teachers together. This is not only an encouragement to students themselves but also can recognize the advantages of other students, so as to be cited in the next learning process. Conversely, if the classroom evaluation mechanism is not perfect, it may hurt the young mind, such as forming a sense of inferiority. Therefore, the usual guidance of teachers and parents is equally important.


## 3.1. Application of Artificial Intelligence Technology in Preschool Education Curriculum

To explore the practical courses of preschool education, the artificial intelligence interaction technology in a 5G wireless network environment is adopted and analyzed. The specific results are shown in Figure8 and Table 1.Figure 8
Survey results of preschool children’s learning of practical courses.Table 1
The results of the survey on the learning situation of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueBetter adapt to teaching methods2.882.980.072It is not difficult to adapt3.123.350.032Interesting teaching methods3.013.510.014Teaching methods are necessary2.853.220.021Figure8 reveals that the learning situation of preschool children before and after the introduction of artificial intelligence technology is mainly carried out from four aspects, namely, “it can better adapt to the current teaching method,” “it is not difficult to adapt,” “this teaching method is interesting,” and “this teaching method is very necessary.” The average value of “can better adapt to the current teaching methods” before the introduction of artificial intelligence technology is 2.88, and the average value after the introduction is 2.98. The average value of “little adaptation difficulty” before the introduction of artificial intelligence technology is 3.12, and the average value after the introduction is 3.35. The average value of “this teaching method is interesting” before the introduction of artificial intelligence technology is 3.01, and the average value after the introduction is 3.51. The average value of “this teaching method is very necessary” before the introduction of artificial intelligence technology is 2.85, and the average value after the introduction is 3.22. In addition, the P values of the above four aspects are 0.072, 0.032, 0.014, and 0.021, and the P value < 0.05 of the latter three items shows that there is a significant difference between the results before and after the investigation. In other words, students’ cognition of learning has changed after the implementation of teaching, but the average value of preschool children in the real adaptation stage is low. The reason for this phenomenon is related to the characteristics of preschool children. Although they show great enthusiasm for various things, it still takes some time to adapt.In addition, the exchanges of classroom cooperation are shown in Figure9 and Table 2.Figure 9
Survey on cooperation and exchange of preschool children’s practical courses.Table 2
Survey on cooperation and exchange of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueParticipate in group communication3.383.550.069Share your thoughts4.124.450.059Help other students solve problems4.224.890.009Harmonious group atmosphere3.854.050.013Figure9 shows that the average value of “can participate in group communication” before the introduction of artificial intelligence technology is 3.38, and the average value after the introduction is 3.55. The average value of “being able to share your ideas” before the introduction of artificial intelligence technology is 4.12, and the average value after the introduction is 4.54. The average value of “can help other students solve problems” before the introduction of artificial intelligence technology is 4.22, and the average value after the introduction is 4.89. The average value of “pleasant and harmonious group atmosphere” before the introduction of artificial intelligence technology is 3.85, and the average value after the introduction is 4.05. In these four studies, the P values are 0.069, 0.059, 0.009, and 0.013, of which the lowest P value is “can help other students solve problems,” indicating that the introduction of artificial intelligence interaction technology into practical course cooperation and communication has the greatest impact on helping other students solve problems.At the same time, the learning effect of preschool practice courses is investigated, and the specific results are shown in Figure10 and Table 3.Figure 10
Survey results of preschool children’s practical classroom learning effect.Table 3
A questionnaire on the learning effect of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueParticipate in group communication3.383.550.069Share your thoughts4.124.450.059Help other students solve problems4.224.890.009Harmonious group atmosphere3.854.050.013Figure10 indicates that the investigation on the classroom learning effect mainly includes “knowing what I have learned,” “learning all the knowledge explained by the teacher in the classroom,” “being able to tell my parents what I have learned,” and “smart classroom makes me understand knowledge faster.” Among them, the average value of “knowing what you have learned” before the introduction of artificial intelligence technology is 3.67, the average value after the introduction is 3.12, and the P value is 0.155. Before the introduction of artificial intelligence technology, the average value of “the knowledge explained by the teacher in the classroom has been learned” is 4.01. After the introduction of artificial intelligence technology, the average value is 4.48 and the P value is 0.02. The average value of “being able to tell their parents what they have learned” before the introduction of artificial intelligence technology is 3.76, the average value after the introduction is 3.98, and the P value is 0.038. The average value of “smart classroom makes me understand knowledge faster” before the introduction of artificial intelligence technology is 3.16, the average value after the introduction is 3.99, and the P value is 0.005. The P value of “knowing what you have learned” is relatively low. The effect on this item before and after the introduction of artificial intelligence technology is not significant, but there are significant differences in the latter three, indicating that students gain more in class.Finally, the study attitude of preschool children is investigated. The specific results are shown in Figure11 and Table 4.Figure 11
Survey results of preschool children’s learning attitude towards practical courses.Table 4
Learning attitude survey of preschool children’s practical courses.
TypeBefore introductionAfter introductionP valueVery interested in wisdom class3.73.810.178Like teaching activities3.543.970.028Satisfied with the way to solve the problem3.083.050.325Love learning more3.123.630.017Improve learning effect3.564.010.011Figure11 shows that the average value of “very interested in intelligent classroom teaching” before the introduction of artificial intelligence technology is 3.7, the average value after the introduction is 3.81, and the P value is 0.178. The average value of “like the current form of teaching activities” before the introduction of artificial intelligence technology is 3.54, the average value after the introduction is 3.97, and the P value is 0.028. Before the introduction of artificial intelligence technology, the average value of “very satisfied with the way of classroom group communication and cooperation to solve problems” is 3.08. After the introduction, the average value is 3.05 and P value is 0.325. The average value of “smart classroom makes me love learning more” before the introduction of artificial intelligence technology is 3.12, the average value after the introduction is 3.63, and the P value is 0.017. The average value of “intelligent classroom teaching improves my learning effect” before the introduction of artificial intelligence technology is 3.56, the average value after the introduction is 4.01, and the P value is 0.011. It can be found that after the implementation of smart classroom, obvious results have been achieved, which makes preschool children realize the role and significance of smart classroom teaching to a certain extent. Thus, it plays a certain role in teaching practice.To sum up, through the method of questionnaire survey, the learning, cooperation and communication, and the learning effect and learning attitude of preschool children’s practical courses are analyzed. It is found that artificial intelligence interactive technology can promote students’ enthusiasm and autonomous learning ability in practical courses to varying degrees. It can be seen that the application of artificial intelligence interaction technology in the practical curriculum of preschool education is the right choice. Feng et al. believed that children of families with different socioeconomic statuses have significant differences in the following aspects, including “concentration,” “imagination and creation,” “sense of purpose,” “ability to resist setbacks,” and “initiative.” Therefore, families and society should pay more attention to preschool children [28]. Sun et al. believed that parents have a high cognitive level and can reasonably arrange parenting activities. With diverse family cultural resources and peaceful family atmosphere, the family usually has an authoritative and democratic parenting style. There are various forms of family education and abundant activities. Children growing up in such an environment tend to have relatively high preschool cognitive level [29]. After comparison with the concepts of experts and scholars mentioned above, it is found that it is logical and feasible to use advanced artificial intelligence interactive technology to study the practical courses of preschool education major from different perspectives, follow the steps of theory first and method first, and gradually draw corresponding conclusions.


## 3.2. Strategy Optimization of Intelligent Classroom Teaching Activities in Preschool Education

With the continuous improvement of social and economic level, the quality of preschool education in China is also improving, the strength of teachers is growing, educational resources are becoming richer and richer, and educational methods are more reasonable and scientific. In order to combine various teaching methods with the current advanced technology, several design strategies are proposed for the intelligent teaching activities of preschool education practice courses. (I) Preschool teachers should fully apply the platform of smart cloud to the classroom. Before practical application, teachers can conduct research through their own experience or cooperate with students. Teachers should actively mobilize the ability of preschool children to obtain external information, so as to truly and effectively cultivate students’ skills. (II) Children from birth to the age of six are full of infinite curiosity about real life. At this time, both parents and teachers should actively encourage children to contact all kinds of things. On the one hand, they can cultivate their ability of autonomous learning and cognition. On the other hand, they can timely realize their real interests and advantages through this process. In this way, students can start from an early age. (III) Teachers’ reasonable planning of class time is also very important. Each class needs to be divided into several parts. How much time is left for students to think independently, how much time is left for students to cooperate to complete tasks, how much time is left for students to share, communicate, and discuss. Only by reasonably allocating class time can teaching achieve the preset effect. (IV) Interest is the best teacher. Therefore, how to mobilize students’ learning enthusiasm and interest is a problem that all teachers should think and choose. Teachers of preschool education can show in class with “physical object + image + video” instead of blindly playing animated films and videos. Although videos can mobilize children’s enthusiasm, the form is too single, which is easy to cause students’ fatigue and deviate from the real educational goal. (V) Classroom evaluation is the result analysis and summary of a class. It is a positive feedback mechanism. Classroom evaluation is not a summary made by the teacher but needs to be completed by students and teachers together. This is not only an encouragement to students themselves but also can recognize the advantages of other students, so as to be cited in the next learning process. Conversely, if the classroom evaluation mechanism is not perfect, it may hurt the young mind, such as forming a sense of inferiority. Therefore, the usual guidance of teachers and parents is equally important.


## 4. Conclusions

The combination of artificial intelligence technology and education is a hot topic in educational research. Starting with the 5G wireless network environment, and the application of artificial intelligence interaction technology in preschool education practice courses is explored. Questionnaire survey is implemented. In the aspect of students’ learning situation, the results of the survey before and after the research are significantly different. After the implementation of teaching, students’ cognition of learning has changed somewhat than before. However, the average value of preschool children in the real adaptation stage is relatively low, and it still takes some time to adapt. In the practice course communication and cooperation, it is found that the introduction of artificial intelligence interaction technology into the practice course cooperation and communication has the greatest impact on helping other students solve problems. In terms of learning effect, the effect of introducing artificial intelligence technology is relatively significant, which indicates that students gain more in class. In terms of learning attitude, the implementation of smart classroom has achieved obvious results, which makes preschool children realize the role and significance of smart classroom teaching to a certain extent and plays a certain role in teaching practice.The current mainstream artificial intelligence interaction technology is applied, and the questionnaire survey method is used to study the learning effect, learning attitude, and other aspects of the six-year-old kindergarten class in a city of China, with a certain degree of rigor and effectiveness. However, due to the limited energy, this work has certain limitations in data acquisition, leading to some deviations in the inspection of relevant data. Moreover, the application of artificial intelligence interaction technology in the practice courses of preschool education majors under the 5G wireless network environment has not been discussed in terms of economic cost input. Subsequent benefit evaluation can be carried out according to the specific situation, to provide a new theoretical basis and optimization direction for preschool education practice courses, so as to help students establish a correct learning concept from childhood.



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*Source: 7629354-2022-05-09.xml*