# Design and Implementation of Intelligent Teaching System Based on Artificial Intelligence and Computer Technology

Authors: Shuai Xu; Ting Wang; Jiahai Dai; DanLin Wu
Journal: Security and Communication Networks
 (2022)

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## Abstract

The purpose of this paper is to investigate and research the intelligent teaching system based on artificial intelligence (AI) and computer technology, which can provide a better education platform for the majority of education and teaching workers and also help college students improve their learning efficiency. This paper first introduces AI, system modules, computer technology, and some algorithms for establishing intelligent teaching systems, specifically introducing student modules, Bayesian algorithms, and so on, and then introduces intelligent teaching based on AI and computer technology. The experimental results show that the intelligent teaching system based on AI and computer technology has improved the teaching efficiency and management ability of the school, and the help of students has also played a great role. The intelligent teaching system not only helps students improve their learning efficiency by 9.8% but also is favored by 56.8% of teachers and students.


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## Body

## 1. Introduction

The advancement of the Internet and computer technology has made people’s demand for computers continue to increase. The network-based education method solves the bottleneck that relies heavily on time and space in communication learning. At the same time, through the online education system, students can virtualize the actual campus facilities and resources and can enter the education system without additional learning. With the continuous progress of computer AI and other technologies, the intelligent network teaching system provides students with a one-to-one personalized guidance learning system, which students can use at any time. The intelligent education system uses a variety of technologies such as computer networks, digital multimedia, and AI, as well as a combination of traditional education methods such as simulations, tests, and examinations [1, 2]. The current online learning system has two forms. One is to design the content of online courses into web pages, use hypertext links to form a hypertext hypermedia system, and provide some navigation mechanisms. For example, students can read the content index table, page forward button and back button, etc., for free. Another method is to convert the classroom video into a streaming media format that can be played on the Internet and then compare it with the classroom notes. Such a student can click to play on the Internet. However, the performance of this instructional design is a static form of hypermedia, which cannot reflect the process of educational activities. The textbooks are arranged in hypertext because students have to learn independently, so they can learn easily. In the learning process, students can easily deviate from the learning direction of the school and the students themselves.The establishment of the intelligent education system is based on cognitive science, comprehensively using the results of AI technology, educational psychology, computer science, and other fields to implement effective educational technology for students. The intelligent education system based on AI technology combines the advantages of traditional systems as well as the characteristics of AI and computer technology. Over the years, with the continuous development of AI and computer technology, the development of intelligent teaching systems has been able to develop in a higher and better direction, which has further promoted the development of modern education.In recent years, with the continuous development of AI technology, the application field of AI is also constantly developing. In a publicly published article, Bui D T proposed and verified a new hybrid AI method, called Particle Swarm Optimization Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire susceptibility. In the proposed method, a neurofuzzy (NF) inference system is used to build a forest fire model, and particle swarm optimization (PSO) is used to study the optimal values of model parameters [3]. Labovitz et al. used an AI platform to conduct research on measuring and improving the drug compliance of anticoagulant therapy for stroke patients. Studies have shown that the introduction of direct oral anticoagulants, while reducing the need for monitoring, also puts pressure on patients for self-management [4]. In education, the application of AI is also gradually expanding. Timms said that the AI-enabled education industry (AIED) based on AI technology is now mature enough to get rid of the transmission method mainly through computers and tablets so that it can interact with students in new ways and help teachers teach more effectively [5]. Not only that, Burton et al. as an educator has also practiced. They also pointed out that the recent surge in interest in AI ethics may make many educators wonder how to solve moral, ethical, and philosophical issues in their AI courses [6]. Of course, with the increasing use of AI technology, AI technology is also constantly improving. Chatila et al. made a report on the scope, goals, and initiatives of the IEEE Global AI and Autonomous System Ethical Consideration Initiative at a conference, aiming to establish more scientific AI technology and continuously improve the application of AI in modern life [7]. Craswell et al. used computer technology to monitor the mortality and morbidity of mothers and infants and other population health data collected around the world to generate perinatal data [8]. Wu pointed out that intelligent teaching systems are the top priority in the teaching field. In this process, there is no need for human tutors to participate, and the student model is the core of the intelligent teaching system [9]. Although these studies presented that the intelligent teaching system made by using computer technology and AI technology is conducive to the development of modern teaching, its production and maintenance costs are high, the required technical requirements are also high, the realization is difficult, and its practical is also not strong enough.The innovation of this paper is primarily to combine existing research on intelligent education systems, using AI and computer technology to record and dig out students’ actions, footprints, and results in traditional educational activities. Combine artificial intelligence technology and computer technology to establish a model of students and teacher managers in the teaching process, and conduct intelligent teaching management based on this model. The school should construct a student model that suits the students’ individual characteristics and design and implement specific knowledge areas and intelligent personalization on the basis of this research [10–12]. Secondly, the establishment of an intelligent education system based on AI and computing technology has established a more efficient platform for the school’s future distance education. Its advantages are that it provides a good auxiliary means for school teaching [13]. The research and development of the intelligent education system has played a certain role in promoting the innovation of school teaching method, teaching method, and teaching mode. The most innovative aspect is that parents of students can supervise the learning of teachers and students online through the intelligent teaching system established by the school, making the school’s teaching methods more transparent and also supervising the children’s every move in school for parents [14].


## 2. The Method of Establishing an Intelligent Teaching System Based on AI and Computer Technology

### 2.1. The Establishment of Student Learning Model

The student’s learning model at school is the basis for the establishment of an intelligent teaching system. Only by continuously improving the research and establishment of the learning model of students at school can we better reason and track the learning situation of students so as to establish an understanding of each student one-to-one learning system [15]. Make sure that every student can understand the learning system so as to achieve the maximum teaching effect. At present, there are also some problems with the establishment of student models. Generally speaking, there are many difficulties in students’ large base, different personalities, and sharing of student information.


#### 2.1.1. The Overall Structure of the Student Learning Model

The overall structure of the learning model includes three aspects. One is the basic information of the student, which includes the student’s name, gender, age, student number, class, and academic performance over the years. The basic information of these students is the most critical source of information for the initialization of the student learning model [16]. The second is the student’s personal hobbies, especially the students’ learning hobbies, such as which aspects of knowledge students are more interested in and what learning methods they prefer. The third point is the student’s personal learning performance. Learning performance is reflected in the changes in students’ cognitive ability and knowledge learning. It is very important to correctly evaluate the method of cognitive ability model [17]. Through the student’s test performance, the cognitive ability of each knowledge item is statistically analyzed, and the overall cognitive ability of the student is formed in an all-round way. Figure 1 shows the overall structure of the student learning model.Figure 1
The overall structure of the student learning model.


#### 2.1.2. Analysis of the Learning Model

To establish a student’s learning model, it is necessary to conduct an overall analysis [18]. First, make a model analysis of students’ hobbies, use feature vectors to define the form of learning resources, and record students’ browsing, learning, testing, and learning resources corresponding to other actions so as to simulate students’ interests. Among them, the formula defined by the eigenvector is(1)W=a1,b1,c1,a2,b2,c2,…an,bn,cn.Due to the consideration of differences in interest and the need for continuous development, the model is defined as(2)model=a1,S1d,S1,rd1,c1,p1,…an,Snd,Sn,rdn,cn,pn.Among them,an is the feature item; Snd is the short-term interest weight of an; Snr is the long-term interest weight of an; dn is the update time; cn is the sort of an; pn is the parent feature item of an; when there is no parent feature item, pn will be 0.The short-term interest calculation formula is as follows:(3)Snd=1t∑g=1t1Dm∑k=1Dmsan,in,where T is the statistical time size (usually in days). S refers to the page feature weight, and the calculation formula is as follows:(4)san,in=qan,in∗lgw/wan+0.01∑in∈Ran∈A∗lgw/wan+0.01∗constin.Among them,qan,in is the number of times that page in contains an; A is the set of feature items; R is the set of pages; w is the total number of pages; w, an is the number of pages with an; const(in) is a parameter in in.When evaluating long-term benefits, time benefits and short-term benefits should also be combined [19]. The calculation method is as follows:(5)Snr=Snr−pre∗Exq2x∗qcfew−wn+Snd.In the information mining of students’ browsing pages, the time spent by students browsing the learning page resources and the length or size of the learning resources are the main mining objects. The student browsing speed calculated through these two pieces of information reflects the students’ learning resources [20]. Generally speaking, the slower the browsing speed is, the more careful the student browses and the more interested he is in this resource; on the contrary, faster browsing speed means that the student turns off the browsing of this resource immediately if they are not interested [21]. The flowchart used is shown in Figure 2.Figure 2
Algorithm flowchart of the generation and update of the learning interest model.After analyzing the student’s interest model, the next step is the construction and analysis of the student’s self-cognition ability model. The first thing to do is to use the vector calculation method to test the students themselves [22–24]. Record the results after testing the selected problem type. Suppose that after the students have tested the English spelling type questions many times, the cognitive vector of the given test question is(6)AQI=tp1,tp2,tp3,tp4,tp5,tp6.According to the students' self-assessment results, the data shown in Table 1 are obtained.Table 1
Test result table.
Knowledge pointsCorrect number of testsNumber of test errors1I (1)I (1-1)2I (2)I (2-2)3I (3)I (3-3)The correct usage rate of knowledge point 1 in this type of question type isI(N):(7)IN=I1I1+I1−1.The obtained cognitive ability vector is WQ:(8)WQ=I1∗Wai.Because there are many types of questions, each student’s mastery is different:(9)WE=WE1,WE2,…,WEn.Calculate the student’s cognitive level formulaO:(10)O=TI∗WE.Through the self-cognition test and analysis of students, the most basic student self-ability analysis model can be established so as to better help the intelligent teaching system to provide personalized help for every student.The above are the test results of students’ self-built question bank based on vector recording, and the conclusions reached are not accurate. In order to better and accurately establish the student’s learning model, modeling should also be based on the test results of vector recording expert question bank.For the questions given by the expert question bank, there are many kinds of questions. Each question has been set in the question bank during the entry process of the cognitive abilities that can be obtained during the test. When students choose an expert question bank to conduct an authoritative test on themselves, the test results will be recorded in a vector table. The structure of the vector table is shown in Table2.Table 2
Test result vector table.
Question numberMemorizeUnderstandApplicationAnalyzeComprehensiveEvaluation10101−1121−1−110−1310−1−110…………………n−111−1−11For the value in this table, when the student tests the question corresponding to the question number, if the student answers this question correctly, then the cognitive ability marked at the time of entry corresponds to a score of 1, and if the answer is incorrect, the value is −1; of course, the unmarked cognitive ability item in this question will take the value 0. Suppose students test multiple-choice question types and calculate cognitive ability based on the test results. The calculation method is(11)QR=ERI1ERI1+ERI−1,where QR is the number of single-choice questions in the test and ERI1 is the total number of correct answers to the category of cognitive ability during this test.The matrix W of cognitive ability is obtained:(12)W=a11...a16.........a51...a56.Adding the weight vectorC, evaluation result is obtained by the student after the test as F:(13)F=C∗W=F1,F2,F3,F4,M5,M6.Calculate the comprehensive cognitive abilityD obtained by students during this test:(14)D=∑I=16WI∗EI.


### 2.2. The Establishment of Teacher and Administrator Modules

#### 2.2.1. The Establishment of Teacher Module

The teacher module mainly arranges the teaching content dynamically according to the teaching goal and the student model. The establishment of teacher module is mainly to ensure that the teaching process of teachers is rigorous, standardized, and targeted. The database connected with it mainly reflects the student’s cognitive library, learning data library, and teaching strategy library.Its structure model is shown in Figure3.Figure 3
Teacher module structure model.The description of the teacher module is as follows: (1) The knowledge base includes a learning resource library, a teaching strategy library, and a question bank. (2) The teaching controller includes a function module and an inference engine. (3) The function module includes a question bank query, modification, deletion, addition, and learning resource library query, change, delete, add, teaching strategy database query, change, delete, add, discussion and Q&A area startup, management, and related database query. (4) Related databases include cognitive status database, student basic information, and test record database.The establishment of the teaching strategy database is the most important task of the teacher’s teaching module. The database of teaching startegy is shown inTable3.Table 3
Teaching strategy table.
Field nameType of dataIllustrateIDIntStrategy numberAbilityIntCognitive ability valueResultIntTest result of learned knowledgeBoolIntWhether to learn new knowledgeHintIntHintThe related databases are described as follows: learning resource library: store teaching content and update teaching content; teaching strategy library: include class types, key points, difficulties, and teaching requirements and contain teaching methods (may be more species); question bank: store all kinds of test questions to check the learning effect of students, used for examinations and in-class tests.Figure 4
Management module structure model.


#### 2.2.2. Establishment of Management Module

(1)
The structural model of the management module is shown in Figure4.(2)
The definition of the management module is as follows.The knowledge base includes educational administration information database, teacher information database, and student information database; controller refers to a functional module and inference engine.The functional modules include management information release, student information query and change, teacher information query and change, and related database query and maintenance.


### 2.3. Bayesian Network

Bayesian network probabilistic reasoning analyzes the position and forecast trend of intelligent teaching system parameters by observing the probability distribution data and obtains the optimal solution and the best effect. The mathematical description is(15)CP|H,L=CP|L∗CH|P,LCH,L,where L is prior information, H is additional evidence, and P is trust: C(P|H, L) is the posterior probability; C(H|L) is the prior probability of H under the condition of a given L; C(H|P, L) is the likelihood, which is the probability of evidence H, of course, assuming that P and L are both true; C(H, L) is the scale factor, which is independent of P.Bayesian network was proposed by Pearl in 1988. It is a valuable tool in data analysis and uncertainty reasoning, and it can assist humans in applying probability theory to a larger field.Define a Bayesian network as follows:A = <B>.Among them,B is a directed acyclic graph with a set of random variables Y as the vertex, and the function logic relationship is an arc. Assuming that the set of parent variables of the vertex random variable Yi of B is πi and P is the conditional probability of Yi event occurring under the premise of the occurrence of πi event, the joint conditional probability distribution on the set of random variables Y is defined as(16)PY1,Y2,Y3,3…=∏PYN|πN.Bayesian network probabilistic reasoning obtains the optimal decision by observing the probability distribution data and calculating the probability.


### 2.4. Fish School Algorithm

By constructing artificial fish to simulate fish feed, clustering, and collision behavior, we achieve optimized effects.


#### 2.4.1. Predatory Behavior

Suppose the current state of the artificial fish isAi and a state Bi is randomly selected. As we all know, we can transform the maximum problem into the minimum problem, so in this example, the maximum problem is used as an example for the next discussion. If the biggest problem is Qi < Wj, then the selected fish should move one step in this direction; otherwise, randomly select a state Aj to determine whether it meets the preset forward condition; if not, it will move one step randomly. The rules for step movement are as follows:(17)Ai+1=Ai+1+StepBi−AiBi−AiQJ>Qi,Ai+1=A+iStepQJ≤Qi.


#### 2.4.2. Cluster Behavior

The artificial fish searches for the partner number NF and the center positionX in the current situation; this means that there is enough food in the center of the fish school, where it is not too crowded. The relevant mathematical simulation formula for cluster behavior is(18)Ai+1=Ai+StepCa−AiCa−AiBaNF>∂Biand NF≥1,Ai+1=Defalut FormulaBaNF≤∂Bior NF=0.The current state of the artificial fish is Ai, and the number of companions in its visible area is NF to form a set G:(19)G=Bj|Bj−BI≤Visual,I,J=1,2,3,…,n.IfK is not an empty set, it indicates that there is a companion in the field of view; that is, nf ≥ 1; then use formula (20) to explore the center position Xc:(20)Ca=∑jNFBjNF.Among them,Xc represents the state of the center seat.


#### 2.4.3. Following Behavior

SupposeAi is looking for the current state of partner Amax with Bmax nearby if it can be seen that the current location of partner Amax has higher food consistency and keeps it not crowded. AF will take a step towards its partner Amax; otherwise, go to search behavior. This behavior can be expressed by the following mathematical description:(21)Ai+1=Ai+StepCa−AiCa−AiBmaxNF>∂Biand NF≥1,Ai+1=Defalut FormulaBmaxNF≤∂Bior NF=0.


## 2.1. The Establishment of Student Learning Model

The student’s learning model at school is the basis for the establishment of an intelligent teaching system. Only by continuously improving the research and establishment of the learning model of students at school can we better reason and track the learning situation of students so as to establish an understanding of each student one-to-one learning system [15]. Make sure that every student can understand the learning system so as to achieve the maximum teaching effect. At present, there are also some problems with the establishment of student models. Generally speaking, there are many difficulties in students’ large base, different personalities, and sharing of student information.


### 2.1.1. The Overall Structure of the Student Learning Model

The overall structure of the learning model includes three aspects. One is the basic information of the student, which includes the student’s name, gender, age, student number, class, and academic performance over the years. The basic information of these students is the most critical source of information for the initialization of the student learning model [16]. The second is the student’s personal hobbies, especially the students’ learning hobbies, such as which aspects of knowledge students are more interested in and what learning methods they prefer. The third point is the student’s personal learning performance. Learning performance is reflected in the changes in students’ cognitive ability and knowledge learning. It is very important to correctly evaluate the method of cognitive ability model [17]. Through the student’s test performance, the cognitive ability of each knowledge item is statistically analyzed, and the overall cognitive ability of the student is formed in an all-round way. Figure 1 shows the overall structure of the student learning model.Figure 1
The overall structure of the student learning model.


### 2.1.2. Analysis of the Learning Model

To establish a student’s learning model, it is necessary to conduct an overall analysis [18]. First, make a model analysis of students’ hobbies, use feature vectors to define the form of learning resources, and record students’ browsing, learning, testing, and learning resources corresponding to other actions so as to simulate students’ interests. Among them, the formula defined by the eigenvector is(1)W=a1,b1,c1,a2,b2,c2,…an,bn,cn.Due to the consideration of differences in interest and the need for continuous development, the model is defined as(2)model=a1,S1d,S1,rd1,c1,p1,…an,Snd,Sn,rdn,cn,pn.Among them,an is the feature item; Snd is the short-term interest weight of an; Snr is the long-term interest weight of an; dn is the update time; cn is the sort of an; pn is the parent feature item of an; when there is no parent feature item, pn will be 0.The short-term interest calculation formula is as follows:(3)Snd=1t∑g=1t1Dm∑k=1Dmsan,in,where T is the statistical time size (usually in days). S refers to the page feature weight, and the calculation formula is as follows:(4)san,in=qan,in∗lgw/wan+0.01∑in∈Ran∈A∗lgw/wan+0.01∗constin.Among them,qan,in is the number of times that page in contains an; A is the set of feature items; R is the set of pages; w is the total number of pages; w, an is the number of pages with an; const(in) is a parameter in in.When evaluating long-term benefits, time benefits and short-term benefits should also be combined [19]. The calculation method is as follows:(5)Snr=Snr−pre∗Exq2x∗qcfew−wn+Snd.In the information mining of students’ browsing pages, the time spent by students browsing the learning page resources and the length or size of the learning resources are the main mining objects. The student browsing speed calculated through these two pieces of information reflects the students’ learning resources [20]. Generally speaking, the slower the browsing speed is, the more careful the student browses and the more interested he is in this resource; on the contrary, faster browsing speed means that the student turns off the browsing of this resource immediately if they are not interested [21]. The flowchart used is shown in Figure 2.Figure 2
Algorithm flowchart of the generation and update of the learning interest model.After analyzing the student’s interest model, the next step is the construction and analysis of the student’s self-cognition ability model. The first thing to do is to use the vector calculation method to test the students themselves [22–24]. Record the results after testing the selected problem type. Suppose that after the students have tested the English spelling type questions many times, the cognitive vector of the given test question is(6)AQI=tp1,tp2,tp3,tp4,tp5,tp6.According to the students' self-assessment results, the data shown in Table 1 are obtained.Table 1
Test result table.
Knowledge pointsCorrect number of testsNumber of test errors1I (1)I (1-1)2I (2)I (2-2)3I (3)I (3-3)The correct usage rate of knowledge point 1 in this type of question type isI(N):(7)IN=I1I1+I1−1.The obtained cognitive ability vector is WQ:(8)WQ=I1∗Wai.Because there are many types of questions, each student’s mastery is different:(9)WE=WE1,WE2,…,WEn.Calculate the student’s cognitive level formulaO:(10)O=TI∗WE.Through the self-cognition test and analysis of students, the most basic student self-ability analysis model can be established so as to better help the intelligent teaching system to provide personalized help for every student.The above are the test results of students’ self-built question bank based on vector recording, and the conclusions reached are not accurate. In order to better and accurately establish the student’s learning model, modeling should also be based on the test results of vector recording expert question bank.For the questions given by the expert question bank, there are many kinds of questions. Each question has been set in the question bank during the entry process of the cognitive abilities that can be obtained during the test. When students choose an expert question bank to conduct an authoritative test on themselves, the test results will be recorded in a vector table. The structure of the vector table is shown in Table2.Table 2
Test result vector table.
Question numberMemorizeUnderstandApplicationAnalyzeComprehensiveEvaluation10101−1121−1−110−1310−1−110…………………n−111−1−11For the value in this table, when the student tests the question corresponding to the question number, if the student answers this question correctly, then the cognitive ability marked at the time of entry corresponds to a score of 1, and if the answer is incorrect, the value is −1; of course, the unmarked cognitive ability item in this question will take the value 0. Suppose students test multiple-choice question types and calculate cognitive ability based on the test results. The calculation method is(11)QR=ERI1ERI1+ERI−1,where QR is the number of single-choice questions in the test and ERI1 is the total number of correct answers to the category of cognitive ability during this test.The matrix W of cognitive ability is obtained:(12)W=a11...a16.........a51...a56.Adding the weight vectorC, evaluation result is obtained by the student after the test as F:(13)F=C∗W=F1,F2,F3,F4,M5,M6.Calculate the comprehensive cognitive abilityD obtained by students during this test:(14)D=∑I=16WI∗EI.


## 2.1.1. The Overall Structure of the Student Learning Model

The overall structure of the learning model includes three aspects. One is the basic information of the student, which includes the student’s name, gender, age, student number, class, and academic performance over the years. The basic information of these students is the most critical source of information for the initialization of the student learning model [16]. The second is the student’s personal hobbies, especially the students’ learning hobbies, such as which aspects of knowledge students are more interested in and what learning methods they prefer. The third point is the student’s personal learning performance. Learning performance is reflected in the changes in students’ cognitive ability and knowledge learning. It is very important to correctly evaluate the method of cognitive ability model [17]. Through the student’s test performance, the cognitive ability of each knowledge item is statistically analyzed, and the overall cognitive ability of the student is formed in an all-round way. Figure 1 shows the overall structure of the student learning model.Figure 1
The overall structure of the student learning model.


## 2.1.2. Analysis of the Learning Model

To establish a student’s learning model, it is necessary to conduct an overall analysis [18]. First, make a model analysis of students’ hobbies, use feature vectors to define the form of learning resources, and record students’ browsing, learning, testing, and learning resources corresponding to other actions so as to simulate students’ interests. Among them, the formula defined by the eigenvector is(1)W=a1,b1,c1,a2,b2,c2,…an,bn,cn.Due to the consideration of differences in interest and the need for continuous development, the model is defined as(2)model=a1,S1d,S1,rd1,c1,p1,…an,Snd,Sn,rdn,cn,pn.Among them,an is the feature item; Snd is the short-term interest weight of an; Snr is the long-term interest weight of an; dn is the update time; cn is the sort of an; pn is the parent feature item of an; when there is no parent feature item, pn will be 0.The short-term interest calculation formula is as follows:(3)Snd=1t∑g=1t1Dm∑k=1Dmsan,in,where T is the statistical time size (usually in days). S refers to the page feature weight, and the calculation formula is as follows:(4)san,in=qan,in∗lgw/wan+0.01∑in∈Ran∈A∗lgw/wan+0.01∗constin.Among them,qan,in is the number of times that page in contains an; A is the set of feature items; R is the set of pages; w is the total number of pages; w, an is the number of pages with an; const(in) is a parameter in in.When evaluating long-term benefits, time benefits and short-term benefits should also be combined [19]. The calculation method is as follows:(5)Snr=Snr−pre∗Exq2x∗qcfew−wn+Snd.In the information mining of students’ browsing pages, the time spent by students browsing the learning page resources and the length or size of the learning resources are the main mining objects. The student browsing speed calculated through these two pieces of information reflects the students’ learning resources [20]. Generally speaking, the slower the browsing speed is, the more careful the student browses and the more interested he is in this resource; on the contrary, faster browsing speed means that the student turns off the browsing of this resource immediately if they are not interested [21]. The flowchart used is shown in Figure 2.Figure 2
Algorithm flowchart of the generation and update of the learning interest model.After analyzing the student’s interest model, the next step is the construction and analysis of the student’s self-cognition ability model. The first thing to do is to use the vector calculation method to test the students themselves [22–24]. Record the results after testing the selected problem type. Suppose that after the students have tested the English spelling type questions many times, the cognitive vector of the given test question is(6)AQI=tp1,tp2,tp3,tp4,tp5,tp6.According to the students' self-assessment results, the data shown in Table 1 are obtained.Table 1
Test result table.
Knowledge pointsCorrect number of testsNumber of test errors1I (1)I (1-1)2I (2)I (2-2)3I (3)I (3-3)The correct usage rate of knowledge point 1 in this type of question type isI(N):(7)IN=I1I1+I1−1.The obtained cognitive ability vector is WQ:(8)WQ=I1∗Wai.Because there are many types of questions, each student’s mastery is different:(9)WE=WE1,WE2,…,WEn.Calculate the student’s cognitive level formulaO:(10)O=TI∗WE.Through the self-cognition test and analysis of students, the most basic student self-ability analysis model can be established so as to better help the intelligent teaching system to provide personalized help for every student.The above are the test results of students’ self-built question bank based on vector recording, and the conclusions reached are not accurate. In order to better and accurately establish the student’s learning model, modeling should also be based on the test results of vector recording expert question bank.For the questions given by the expert question bank, there are many kinds of questions. Each question has been set in the question bank during the entry process of the cognitive abilities that can be obtained during the test. When students choose an expert question bank to conduct an authoritative test on themselves, the test results will be recorded in a vector table. The structure of the vector table is shown in Table2.Table 2
Test result vector table.
Question numberMemorizeUnderstandApplicationAnalyzeComprehensiveEvaluation10101−1121−1−110−1310−1−110…………………n−111−1−11For the value in this table, when the student tests the question corresponding to the question number, if the student answers this question correctly, then the cognitive ability marked at the time of entry corresponds to a score of 1, and if the answer is incorrect, the value is −1; of course, the unmarked cognitive ability item in this question will take the value 0. Suppose students test multiple-choice question types and calculate cognitive ability based on the test results. The calculation method is(11)QR=ERI1ERI1+ERI−1,where QR is the number of single-choice questions in the test and ERI1 is the total number of correct answers to the category of cognitive ability during this test.The matrix W of cognitive ability is obtained:(12)W=a11...a16.........a51...a56.Adding the weight vectorC, evaluation result is obtained by the student after the test as F:(13)F=C∗W=F1,F2,F3,F4,M5,M6.Calculate the comprehensive cognitive abilityD obtained by students during this test:(14)D=∑I=16WI∗EI.


## 2.2. The Establishment of Teacher and Administrator Modules

### 2.2.1. The Establishment of Teacher Module

The teacher module mainly arranges the teaching content dynamically according to the teaching goal and the student model. The establishment of teacher module is mainly to ensure that the teaching process of teachers is rigorous, standardized, and targeted. The database connected with it mainly reflects the student’s cognitive library, learning data library, and teaching strategy library.Its structure model is shown in Figure3.Figure 3
Teacher module structure model.The description of the teacher module is as follows: (1) The knowledge base includes a learning resource library, a teaching strategy library, and a question bank. (2) The teaching controller includes a function module and an inference engine. (3) The function module includes a question bank query, modification, deletion, addition, and learning resource library query, change, delete, add, teaching strategy database query, change, delete, add, discussion and Q&A area startup, management, and related database query. (4) Related databases include cognitive status database, student basic information, and test record database.The establishment of the teaching strategy database is the most important task of the teacher’s teaching module. The database of teaching startegy is shown inTable3.Table 3
Teaching strategy table.
Field nameType of dataIllustrateIDIntStrategy numberAbilityIntCognitive ability valueResultIntTest result of learned knowledgeBoolIntWhether to learn new knowledgeHintIntHintThe related databases are described as follows: learning resource library: store teaching content and update teaching content; teaching strategy library: include class types, key points, difficulties, and teaching requirements and contain teaching methods (may be more species); question bank: store all kinds of test questions to check the learning effect of students, used for examinations and in-class tests.Figure 4
Management module structure model.


### 2.2.2. Establishment of Management Module

(1)
The structural model of the management module is shown in Figure4.(2)
The definition of the management module is as follows.The knowledge base includes educational administration information database, teacher information database, and student information database; controller refers to a functional module and inference engine.The functional modules include management information release, student information query and change, teacher information query and change, and related database query and maintenance.


## 2.2.1. The Establishment of Teacher Module

The teacher module mainly arranges the teaching content dynamically according to the teaching goal and the student model. The establishment of teacher module is mainly to ensure that the teaching process of teachers is rigorous, standardized, and targeted. The database connected with it mainly reflects the student’s cognitive library, learning data library, and teaching strategy library.Its structure model is shown in Figure3.Figure 3
Teacher module structure model.The description of the teacher module is as follows: (1) The knowledge base includes a learning resource library, a teaching strategy library, and a question bank. (2) The teaching controller includes a function module and an inference engine. (3) The function module includes a question bank query, modification, deletion, addition, and learning resource library query, change, delete, add, teaching strategy database query, change, delete, add, discussion and Q&A area startup, management, and related database query. (4) Related databases include cognitive status database, student basic information, and test record database.The establishment of the teaching strategy database is the most important task of the teacher’s teaching module. The database of teaching startegy is shown inTable3.Table 3
Teaching strategy table.
Field nameType of dataIllustrateIDIntStrategy numberAbilityIntCognitive ability valueResultIntTest result of learned knowledgeBoolIntWhether to learn new knowledgeHintIntHintThe related databases are described as follows: learning resource library: store teaching content and update teaching content; teaching strategy library: include class types, key points, difficulties, and teaching requirements and contain teaching methods (may be more species); question bank: store all kinds of test questions to check the learning effect of students, used for examinations and in-class tests.Figure 4
Management module structure model.


## 2.2.2. Establishment of Management Module

(1)
The structural model of the management module is shown in Figure4.(2)
The definition of the management module is as follows.The knowledge base includes educational administration information database, teacher information database, and student information database; controller refers to a functional module and inference engine.The functional modules include management information release, student information query and change, teacher information query and change, and related database query and maintenance.


## 2.3. Bayesian Network

Bayesian network probabilistic reasoning analyzes the position and forecast trend of intelligent teaching system parameters by observing the probability distribution data and obtains the optimal solution and the best effect. The mathematical description is(15)CP|H,L=CP|L∗CH|P,LCH,L,where L is prior information, H is additional evidence, and P is trust: C(P|H, L) is the posterior probability; C(H|L) is the prior probability of H under the condition of a given L; C(H|P, L) is the likelihood, which is the probability of evidence H, of course, assuming that P and L are both true; C(H, L) is the scale factor, which is independent of P.Bayesian network was proposed by Pearl in 1988. It is a valuable tool in data analysis and uncertainty reasoning, and it can assist humans in applying probability theory to a larger field.Define a Bayesian network as follows:A = <B>.Among them,B is a directed acyclic graph with a set of random variables Y as the vertex, and the function logic relationship is an arc. Assuming that the set of parent variables of the vertex random variable Yi of B is πi and P is the conditional probability of Yi event occurring under the premise of the occurrence of πi event, the joint conditional probability distribution on the set of random variables Y is defined as(16)PY1,Y2,Y3,3…=∏PYN|πN.Bayesian network probabilistic reasoning obtains the optimal decision by observing the probability distribution data and calculating the probability.


## 2.4. Fish School Algorithm

By constructing artificial fish to simulate fish feed, clustering, and collision behavior, we achieve optimized effects.


### 2.4.1. Predatory Behavior

Suppose the current state of the artificial fish isAi and a state Bi is randomly selected. As we all know, we can transform the maximum problem into the minimum problem, so in this example, the maximum problem is used as an example for the next discussion. If the biggest problem is Qi < Wj, then the selected fish should move one step in this direction; otherwise, randomly select a state Aj to determine whether it meets the preset forward condition; if not, it will move one step randomly. The rules for step movement are as follows:(17)Ai+1=Ai+1+StepBi−AiBi−AiQJ>Qi,Ai+1=A+iStepQJ≤Qi.


### 2.4.2. Cluster Behavior

The artificial fish searches for the partner number NF and the center positionX in the current situation; this means that there is enough food in the center of the fish school, where it is not too crowded. The relevant mathematical simulation formula for cluster behavior is(18)Ai+1=Ai+StepCa−AiCa−AiBaNF>∂Biand NF≥1,Ai+1=Defalut FormulaBaNF≤∂Bior NF=0.The current state of the artificial fish is Ai, and the number of companions in its visible area is NF to form a set G:(19)G=Bj|Bj−BI≤Visual,I,J=1,2,3,…,n.IfK is not an empty set, it indicates that there is a companion in the field of view; that is, nf ≥ 1; then use formula (20) to explore the center position Xc:(20)Ca=∑jNFBjNF.Among them,Xc represents the state of the center seat.


### 2.4.3. Following Behavior

SupposeAi is looking for the current state of partner Amax with Bmax nearby if it can be seen that the current location of partner Amax has higher food consistency and keeps it not crowded. AF will take a step towards its partner Amax; otherwise, go to search behavior. This behavior can be expressed by the following mathematical description:(21)Ai+1=Ai+StepCa−AiCa−AiBmaxNF>∂Biand NF≥1,Ai+1=Defalut FormulaBmaxNF≤∂Bior NF=0.


## 2.4.1. Predatory Behavior

Suppose the current state of the artificial fish isAi and a state Bi is randomly selected. As we all know, we can transform the maximum problem into the minimum problem, so in this example, the maximum problem is used as an example for the next discussion. If the biggest problem is Qi < Wj, then the selected fish should move one step in this direction; otherwise, randomly select a state Aj to determine whether it meets the preset forward condition; if not, it will move one step randomly. The rules for step movement are as follows:(17)Ai+1=Ai+1+StepBi−AiBi−AiQJ>Qi,Ai+1=A+iStepQJ≤Qi.


## 2.4.2. Cluster Behavior

The artificial fish searches for the partner number NF and the center positionX in the current situation; this means that there is enough food in the center of the fish school, where it is not too crowded. The relevant mathematical simulation formula for cluster behavior is(18)Ai+1=Ai+StepCa−AiCa−AiBaNF>∂Biand NF≥1,Ai+1=Defalut FormulaBaNF≤∂Bior NF=0.The current state of the artificial fish is Ai, and the number of companions in its visible area is NF to form a set G:(19)G=Bj|Bj−BI≤Visual,I,J=1,2,3,…,n.IfK is not an empty set, it indicates that there is a companion in the field of view; that is, nf ≥ 1; then use formula (20) to explore the center position Xc:(20)Ca=∑jNFBjNF.Among them,Xc represents the state of the center seat.


## 2.4.3. Following Behavior

SupposeAi is looking for the current state of partner Amax with Bmax nearby if it can be seen that the current location of partner Amax has higher food consistency and keeps it not crowded. AF will take a step towards its partner Amax; otherwise, go to search behavior. This behavior can be expressed by the following mathematical description:(21)Ai+1=Ai+StepCa−AiCa−AiBmaxNF>∂Biand NF≥1,Ai+1=Defalut FormulaBmaxNF≤∂Bior NF=0.


## 3. Experimental Design and Result Analysis

### 3.1. Design of an Intelligent Teaching System Based on Computer Technology

In order to realize the modernization of education and teaching, a school reformed and upgraded the school’s teaching system. The following table is part of the database and part of the interface display established by the school in the reform of the course selection system, as shown in Table4.Table 4
Course selection system database.
Serial numberField nameType of dataIllustrate1Course_IdChar (20)Empty string is not allowed2TeacheridChar (20)Allow empty string3Course_NameVarchar (50)Allow empty string4Course_StartVarchar (50)Allow empty string5Course_EndVarchar (30)Allow empty string6StatusChar (1)0 is valid; 1 is invalid7Course_MemoVarchar (500)Course introductionThe data in the previous table are all extracted from the actual situation. The school conducted some investigations on teachers and students. The results of the investigation and analysis are shown in Figure5.Figure 5
The evaluation chart of school teachers and students on the course selection system before and after the improvement.Based on the above experimental data, we can conclude that the improved course selection system based on computer technology is quite feasible and can be used to assist in the production of online course selection system. In addition, the use of database technology to evaluate students’ online learning and monitor their learning efficiency is also a major goal of building an intelligent teaching system. Table5 is the test storage structure table of the intelligent teaching system.Table 5
Test storage table structure.
Property Chinese nameAttribute nameTypeLengthIllustrateQuestion numberIdInt100Primary keyQuestion typeTypeInt200Question difficultyLevelVarchar500Question contentQuestionVarchar500Correct answerRightanswerVarchar600Knowledge pointsknowledgeIdsId collection100VisitsVisitNumber100As shown from the above table, each module of the storage table structure is longer. In order to facilitate the management of the system’s question bank and at the same time consider reducing the complexity of system function implementation, the question bank has designed a database table for each question type.Through the monitoring and analysis of the data, a schematic diagram of the students’ cognitive ability and cognitive ability improvement in school can be drawn, as shown in Figure6.Figure 6
The effect of student learning performance.Through the analysis of the effect of students’ learning performance, it can be seen that, through the intelligent teaching system, students can improve their self-cognition ability and level more quickly and discover their deficiencies in time.


### 3.2. Design and Analysis of Intelligent Teaching System Based on AI Technology

When designing the intelligent teaching system, the school used AI technology to combine with the teaching system, used the combined model for construction, and obtained the advantage diagram of the combined model and other models in the intelligent teaching system, as shown in Figure7.Figure 7
Comparison and improvement of the combined model and other models.The ordinate represents the evaluation value of the interest model, self-cognition model, and self-ability model. The performance of the combined model compared with other models has increased by 5.8%, 6.9%, and 12.5%, respectively.On the whole, the differences between the proposed combination model and other models mostly pass the 90% significance test, indicating that the combination model proposed based on AI technology has a better effect on the establishment of an intelligent teaching system. Figure8 shows the comparison of the test results of each model and the combined model.Figure 8
DM test results of each model and combined model.


### 3.3. Combination Analysis of Intelligent Teaching System Based on AI and Computer Technology

A partial schematic diagram of an intelligent teaching system is shown in Figure9.Figure 9
Part of the interface diagram of a teaching system.Through the user experience survey of the new system, an intelligent teaching system development company obtained the following table data as shown in Table6.Table 6
Survey of users’ preference for intelligent teaching systems.
DifferenceGenerallyGoodVery satisfiedStudent20568192Teacher13589085Administrator15368669Through the analysis of survey data, it can be concluded that more than 50% of users are satisfied with the intelligent teaching system, and only about 20% of users are not satisfied with the system. The intelligent teaching system developed by using AI and computer technology can not only help group students to learn better but also enable a broad group of teachers to share teaching resources and improve teaching efficiency. At the same time, it also facilitates the school’s management of teaching. It is of great significance to promote the development of intelligent teaching and the progress of education.


## 3.1. Design of an Intelligent Teaching System Based on Computer Technology

In order to realize the modernization of education and teaching, a school reformed and upgraded the school’s teaching system. The following table is part of the database and part of the interface display established by the school in the reform of the course selection system, as shown in Table4.Table 4
Course selection system database.
Serial numberField nameType of dataIllustrate1Course_IdChar (20)Empty string is not allowed2TeacheridChar (20)Allow empty string3Course_NameVarchar (50)Allow empty string4Course_StartVarchar (50)Allow empty string5Course_EndVarchar (30)Allow empty string6StatusChar (1)0 is valid; 1 is invalid7Course_MemoVarchar (500)Course introductionThe data in the previous table are all extracted from the actual situation. The school conducted some investigations on teachers and students. The results of the investigation and analysis are shown in Figure5.Figure 5
The evaluation chart of school teachers and students on the course selection system before and after the improvement.Based on the above experimental data, we can conclude that the improved course selection system based on computer technology is quite feasible and can be used to assist in the production of online course selection system. In addition, the use of database technology to evaluate students’ online learning and monitor their learning efficiency is also a major goal of building an intelligent teaching system. Table5 is the test storage structure table of the intelligent teaching system.Table 5
Test storage table structure.
Property Chinese nameAttribute nameTypeLengthIllustrateQuestion numberIdInt100Primary keyQuestion typeTypeInt200Question difficultyLevelVarchar500Question contentQuestionVarchar500Correct answerRightanswerVarchar600Knowledge pointsknowledgeIdsId collection100VisitsVisitNumber100As shown from the above table, each module of the storage table structure is longer. In order to facilitate the management of the system’s question bank and at the same time consider reducing the complexity of system function implementation, the question bank has designed a database table for each question type.Through the monitoring and analysis of the data, a schematic diagram of the students’ cognitive ability and cognitive ability improvement in school can be drawn, as shown in Figure6.Figure 6
The effect of student learning performance.Through the analysis of the effect of students’ learning performance, it can be seen that, through the intelligent teaching system, students can improve their self-cognition ability and level more quickly and discover their deficiencies in time.


## 3.2. Design and Analysis of Intelligent Teaching System Based on AI Technology

When designing the intelligent teaching system, the school used AI technology to combine with the teaching system, used the combined model for construction, and obtained the advantage diagram of the combined model and other models in the intelligent teaching system, as shown in Figure7.Figure 7
Comparison and improvement of the combined model and other models.The ordinate represents the evaluation value of the interest model, self-cognition model, and self-ability model. The performance of the combined model compared with other models has increased by 5.8%, 6.9%, and 12.5%, respectively.On the whole, the differences between the proposed combination model and other models mostly pass the 90% significance test, indicating that the combination model proposed based on AI technology has a better effect on the establishment of an intelligent teaching system. Figure8 shows the comparison of the test results of each model and the combined model.Figure 8
DM test results of each model and combined model.


## 3.3. Combination Analysis of Intelligent Teaching System Based on AI and Computer Technology

A partial schematic diagram of an intelligent teaching system is shown in Figure9.Figure 9
Part of the interface diagram of a teaching system.Through the user experience survey of the new system, an intelligent teaching system development company obtained the following table data as shown in Table6.Table 6
Survey of users’ preference for intelligent teaching systems.
DifferenceGenerallyGoodVery satisfiedStudent20568192Teacher13589085Administrator15368669Through the analysis of survey data, it can be concluded that more than 50% of users are satisfied with the intelligent teaching system, and only about 20% of users are not satisfied with the system. The intelligent teaching system developed by using AI and computer technology can not only help group students to learn better but also enable a broad group of teachers to share teaching resources and improve teaching efficiency. At the same time, it also facilitates the school’s management of teaching. It is of great significance to promote the development of intelligent teaching and the progress of education.


## 4. Discussion

This paper is dedicated to the research of AI and computer technology and applies it to modern intelligent teaching systems. Based on the original teaching system design, an intelligent teaching system is designed and implemented to assist modern digital teaching; students are the foundation and core of the intelligent and personalized service of the intelligent teaching system. This paper mainly focuses on modeling techniques such as learning interest, learning style, student cognitive ability, and student knowledge mastery and puts forward one of the above focus points. Through the analysis of the paper, the demonstration stage confirmed the powerful advantages and functions of AI and computer technology in modern intelligent teaching systems.Through the analysis and demonstration of the case in this paper, it can be known that the intelligent teaching system based on AI and computer technology not only is more scientific and effective than the original teaching system but also provides more personalized services to ShenYang University students. The innovation of the intelligent teaching system is a brand-new educational reform, especially in the current period of the new crown epidemic, which enables students to no longer be restricted by geographical areas, and can enter the system at any time to choose the content that they are interested in or have not mastered in the course. Relearning improves students’ interest and attention so that the learning efficiency is greatly improved. At the same time, as a manager, the school can enter the system at any time and change the original teaching plan and content at any time according to the students’ learning situation, helping ShenYang University students to maximize the utilization of time during their school.


## 5. Conclusions

The case in this paper is based on the online course selection system of a certain school and the opinions of teachers and students on the intelligent teaching system. The research content firstly analyzes and introduces the modern intelligent online course selection system, and then through the survey of teachers and students, the online course selection system based on AI and computer technology has been favored by more than 60% of teachers and students. Then use the intelligent teaching system based on AI and computer technology to conduct a favorite survey. The survey results show that the modern intelligent teaching system is deeply liked by teachers and students, but some teachers and students are still not satisfied with the intelligent teaching system. It shows that the system still needs to be improved and perfected more. This also shows that the research in this paper is still insufficient, and more innovation in the research methods is still needed.



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*Source: 6300299-2022-06-16.xml*