# Exploration on College Ideological and Political Education Integrating Artificial Intelligence-Intellectualized Information Technology

Authors: Wenjuan Li; Fengkai Liu
Journal: Computational Intelligence and Neuroscience
 (2022)

---

## Abstract

In recent years, with the vigorous development and application of Artificial Intelligence (AI), the application of AI in education is becoming more and more extensive. This study makes a theoretical analysis of AI-Intellectualized Information Technology (IT). Discrete Cosine Transform (DCT)-Based Speech Recognition (SR) and Genetic Algorithm (GA)-Based Image Recognition (IR) are used to analyze the College Ideological and Political Education (IAPE). The research findings prove that the advantages of integrating AI-intellectualized IT on College IAPE outweigh the disadvantages. The improvement of technological development, which accounts for 71.17% of undergraduate gains, is the most significant, and the smallest gain is technology coverage, which is 36.80%. Overall, 57.21% are interested in new technology, and the students’ enthusiasm accounts for 30.77%. Most of the students focus on the innovation performance of technology, accounting for 75.92%. With an average influence of 89.04% on undergraduates, technology has the largest impact, followed by 85.78% on students with masters or higher degrees. The largest impact of diversified teaching methods for all students is 62.48%. This study provides some reference values for AI-intellectualized IT research and analysis, as well as students’ IAPE.


---

## Body

## 1. Introduction

Recently, with the continuous development of Artificial Intelligence (AI) and Information Technology (IT), major colleges and universities have also developed AI and IT-related majors one after another, making AI one of the hot topics [1]. Many AI education and training companies have become capital chasing industries, and the track of AI plus education is also recognized as the golden track [2]. In particular, AI can replace complex but repetitive manual educational works such as homework, examination, and evaluation [3]. Moreover, the solidification of the educational model is one of the most significant problems in higher education. A rigorous teaching paradigm, for example, can quickly lead to issues like boring teaching assessment indications, a lack of learning motivation, and cramming teaching. Teachers are finding it more challenging to provide personalized instruction as the number of students grows and their personalities become more diverse [4]. Under such background, AI might provide a feasible solution and technical support and solve several classic problems in learning links, including language processing, reasoning, planning, and cognitive modeling [5].The twenty-first century has witnessed accelerated integration of various disciplines, emerging new domains, and extension of scientific frontiers [6]. Consequently, a new round of IT revolution is underway, including Robotic Process Automation (RPA), Big Data Analytics (BDA), and Three-Dimensional Printing (3DP) technologies, becoming the new research focus [7]. Meanwhile, the active IT innovation gives birth to a series of new products, applications, and new models. They have extensively promoted the development and growth of emerging industries, thus accelerating the industrial structure adjustment, transformation, upgrading, and changing the traditional economic development modes [8]. China’s first exploration of AI might trace back to the 1980s. Since then, various fields have been actively exploring a new road to integrating AI and education. In particular, the Chinese government has realized the significance of integrating AI and the Ideological and Political Education (IAPE) of College Students, referring to many international research achievements [9]. Fusing AI and IT could provide new learning approaches and help educational experts formulate teaching plans and decisions [10]. Importantly, in-school students-oriented education is an important part of social life. With AI and IT penetrating the educational field, a new integrated educational approach is being explored by combining AI and IT with education [11].Li et al. [12] underlined the importance of academics demonstrating assistance connected to network press for learners based on an examination of practical concerns and literary components to outline the relevance, features, and newest events of the complete network. Alhabbash et al. [13] presented a Smart Grammar Tutoring System for students to learn English grammar rapidly and efficiently. The system assigns all written English subjects and generates a set of questions to be answered for each subject. The approach takes into account all of the distinctions between students, as well as the fundamental effect of easier to more challenging tasks. The authors in [14] explored teacher participation in the Committee of Youth Study (Council), civic engagement, and the formation of civic agencies that include high school students in public participation research projects aimed at addressing educational inequalities and motivating others toward social justice. Zhang and Fagan [15] concentrated on the outcomes of a variety of methods that explored the role of intellectual and social learning in college students’ democratic knowledge and participation. Based on psychological quality evaluations, Zhong proposed [16] Artificial Intelligence-Based Ideological and Political Learning (AI-IPL) for college students. The AI-IPL approach is used to support college students’ mental and physical growth as well as their ideological and political development. Shanshan developed [17] a method for college students who must develop, innovate, and grow collaboratively in their Ideological and political education. The work of scholars of idealistic and political intonation is used in a collaborative way of technological development [18].This study provides a theoretical analysis of AI and IT in the development of the ideological and physical education of college students. Using the Discrete Cosine Transform (DCT)-Based Speech Recognition (SR) and Genetic Algorithm (GA)-Based Image Recognition (IR) in students’ IAPE, the advantages and disadvantages of AI-intellectualized IT on College IAPE are examined and gains analysis and its impact on education are provided. The present study has certain reference significance for the research and analysis of AI-intellectualized IT and students’ IAPE.The rest of the manuscript is organized as follows. Section2 provides an overview of the College IAPE Based on AI-intellectualized IT. Section 3 illustrates different results and provides an analysis of the obtained results. Section 4 concludes the manuscript.


## 2. College IAPE Based on AI-Intellectualized IT

### 2.1. AI-Intellectualized IT Theory

AI is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence [19]. It aims is to enable machines to the way people think and reason. AI is a subset of IT and the intersection of natural science, social science, and technical science. Modern IT refers to the technology in which people use binary numbers with different codes to process information through digital equipment, such as computers. AI might involve computer science, psychology, philosophy, linguistics, and other disciplines [20], thus covering almost all-natural science and social science disciplines. Its scope has exceeded the scope of computer science. In particular, AI and thinking science share the same relationship as practice and theory [21]. AI is at the technical application level of thinking science and is one of its application branches. AI presents itself from two dimensions: “artificial” and “intelligence.” The cognitive research of AI is a technical science that enables computers to simulate, extend, and expand human intelligence. IT mainly uses big data-based Machine Learning (ML) algorithms to out-calculate humans. Figure 1 shows the diagram of the AI-intellectualized IT knowledge framework.Figure 1
AI-intellectualized IT knowledge framework.Figure1 illustrates AI-intellectualized IT from intelligent speech recognition (SR), Character Recognition (CR), Machine Translation (MT), and Natural Language Processing (NLP). SR is to convert speech signals into corresponding text or instructions. CR recognizes the text in the image and converts the image into text. MT uses computers to convert one natural language (source language) into another (target language). Currently, MT is also the most active research field in AI, which is based on linguistics, mathematics, and computer science. Linguists provide dictionaries and grammatical rules suitable for computer processing; mathematicians formalize and code the materials supplied by linguists. Computer scientists provide software and hardware equipment for designing MT and programs. Thus, MT is made possible only by integrating the above three aspects, and the effect of MT depends entirely on their joint efforts. So far, MT has achieved some valuable results but is still a far cry in terms of translation quality. Lastly, NLP studies various theories and methods to communicate effectively with humans and computers with natural language. Additionally, Optical Character Recognition (OCR) technology is now mainly employed in CR. Figure 2 specifies the AI-based character recognition process.Figure 2
The AI-based character recognition process.Figure2 outlines the OCR technology process. Specifically, it processes the original document and obtains the image with the scanner or digital camera for image preprocessing. Image preprocessing mainly includes image binary conversion, noise removal, and tilt correction. Next, the OCR software is invoked to recognize characters. Firstly, it opens the image file. Then, the extract feature vectors are roughly classified and finely matched with templates based on the feature template library to determine the algorithm of characters [22, 23]. Secondly, it extracts the character features and the multidimensional features from the character image that are reserved for the later feature matching-oriented pattern recognition algorithm. Postprocessing correction can correct the recognition results according to the relationship of specific language context. The syntax detector can detect whether the combination logic of characters is reasonable. Finally, the document recognition technology can call the algorithms for different input image formats to save the text. A complete AI-intellectualized IT system fuses the AI core and the IT system. The core AI system is the core of the complete AI-intellectualized IT system and deals with problems at the knowledge and intelligence level. In contrast, the IT system is located outside the complete AI-intellectualized IT system and mainly deals with some issues at the information level. At the same time, it serves as the interface between the core AI system and the external environment. For example, the IT system obtains ontology information from the environment and interacts with the intelligent behaviors imposed on the environment at the other end. Meanwhile, the IT system can provide humans with convenient and fast information-sharing services. Figure 3 demonstrates the complete AI-intellectualized IT system.Figure 3
Complete AI-intellectualized IT system.As shown in Figure3, the complete AI-intellectualized IT system comprises the basic, technology, and application layers. The basic layer mainly provides data input and computing power. The technology layer develops the algorithm model on the basis layer, trains and learns through the software framework, and obtains AI technology. The algorithm model is the soul of the AI system. The application layer applies AI technology to different scenarios for commercialization.


### 2.2. College IAPE System Integrating AI-Intellectualized IT

As a general technology, AI can be widely used in various fields, including its integration with education. AI is the core of many virtuous interaction circles, including business data analysis, NLP, SR, machine reasoning, computer vision, robots, and sensors. Surely, applying these technologies in education will help the reform of higher education. Figure4 illuminates the process of college IAPE Based on AI-intellectualized IT.Figure 4
College IAPE process based on AI-intellectualized IT.Figure4 suggests integrating College IAPE and AI-intellectualized IT generating a more accurate analysis result. Before the course, it collects the IAPE videos and recognizes and marks the class video images through IR and SR. Then, it extracts the classroom audio data and classifies them through SR. Afterward, DCT can be used for SR [24, 25]. Next, based on the logarithmic energy of the filter, the Cepstral coefficient can be obtained by DCT, as shown in the following equation (1):(1)Cn=∑m=0N−1smcosπnm−0.5M,n=1,2,…,L,where Cn is the Mel-scale cepstrum parameter of order L. L refers to the order of Mel Frequency Cepstral Coefficient (MFCC). Usually, order 12 can represent the acoustic characteristics. sm denotes the signal after framing, and N stands for the size of the frame. M means the number of triangular filters, and the transfer function of each band-pass filter is calculated using the following equation (2):(2)Hmk=0,k<fm−1,2k−fm−1fm+1−fm−1fm−fm−1,fm−1≤k≤fm,2fm+1−kfm+1−fm−1fm−fm−1,fm≤k≤fm+1,0,k≥fm+1,where Hmk,0≤m≤M represents several band-pass filters set within the sound spectrum range. f(m)is the center frequency of each filter, and in (3), f(m) can be calculated:(3)fm=NfsFmel−1Fmelfl+mFmelfh−FmelflM+1,where fl and fh represent the lowest and highest frequency in the filter frequency range, respectively. N is the length of FFT. f shows the sampling frequency. The corresponding relationship between frequency and Mel-scale is linear below 1,000 Hz and logarithmic above 1,000 Hz. Equations (4) and (5) are the specific calculation processes.(4)melf=2595In1+f700.(5)f=70010m/2595−1.In (4) and (5), f is the frequency, and the unit is Hz. Equation (6) reflects the logarithmic energy output by each filter.(6)sm=In∑k=0N−1Xk2Hmk,where sm is the logarithmic energy output by each filter bank and Xk indicates the time domain signal. Logarithmic processing is performed in the frequency domain, and the logarithm is inversely transformed to obtain the MFCC coefficient, as expressed in (7) and (8).(7)logXk=logHk+logEk,(8)xk=hk+ek.Equation (7) is a logarithmic operation, and Equation (8) is the inverse transformation.Then, the extreme of (7) and (8) is calculated to obtain the coefficient. The above-extracted features are static, while the actual sound is continuous. Therefore, adding features to represent the dynamic changes of continuous sound can be realized by calculating the first-order or second-order difference, as illustrated in the following (9):(9)dt=ct+1−ct−12.In equation (9), d(t) represents the t-the first-order difference and c(t) indicates the t-th Cepstrum coefficient. Secondly, using the automatic analysis and application of AI-intellectualized IT, according to IR and SR results, this study analyzes the teaching process of College Students’ IAPE and the changes in students’ ideology and political views. The video is segmented according to the video image changes in the IR field. Whether each segment belongs to the IAPE teaching process or video playback is judged simultaneously. Then, the video image segmentation data are recorded and stored in the database. IR can be performed through GA [26]. First, appropriate image samples are selected from the video, as computed in the following (10):(10)Pxi=fxi∑j=1Nfxj,where f represents the fitness function. The sample size is N. fxi is the fitness of the sample xi. The SR process converts image files into audio files, extracts sound features, and stores them in the feature list. Then, feature matching is carried out to complete cluster analysis. Finally, the process of IAPE is completed, and the teaching process is analyzed according to the principles of College IAPE. Figure 5 manifests the application of AI-intellectualized IT in College Students’ IAPE.Figure 5
Application of AI-intellectualized IT in College Students’ IAPE.According to the relevant characteristics and technical requirements, in College IAPE, AI-intellectualized IT can not only achieve the knowledge expression of traditional IAPE but also use the Expert System Shell (ESS)-based simple expert system to let students experience the application of AI-intellectualized IT in learning IAPE. Then, it analyzes and solves students’ ideological and political problems. In particular, an Intelligent Computer-Aided Instruction (ICAI) can be used to apply AI-intellectualized IT to the college IAPE system based on a computer medium and cognitive science. Figure6 draws the ICAI design model.Figure 6
ICAI design model.As plotted in Figure6, ICAI can separate the teaching process from the analytic process of College IAPE and pay more attention to systematic reasoning and analysis of students’ IAPE. Integrating the ICAI into higher education can eliminate the existing teaching evaluation dilemma, such as single data, untimely feedback, and neglecting the teaching scene [27]. Through real-time data processing and subtle emotional perception, ICAI can analyze the teaching process and timely feedback targeted information. The expert system module is integrated with the teaching module, and it judges students’ IAPE according to the standards of multiple simulated experts. Figure 7 unfolds the structure of the AI-intellectualized IT expert system.Figure 7
Structure of AI-intellectualized IT expert system.The expert system structure in Figure7 can solve College Students’ IAPE problems. The key to developing an expert system is to use relevant theoretical expert knowledge to analyze the issues of students’ IAPE.


### 2.3. IAPE in Colleges and Universities

College IAPE teachers should well manage AI-intellectualized IT [28]. AI technology provides a wealthy platform, tools, and resources. Teachers can use emotional education to educate students. With the continuous enhancement of students’ interaction with new technologies, students will express more diversified and dynamically changing ideological and political views. Accordingly, teachers should prepare themselves well against students’ dynamic and personalized political opinions and ideology using AI-intellectualized IT [29, 30]. Table 1 lists the principles of College IAPE.Table 1
Principles of College IAPE.
Adhere to principlesEducational focusHumanistic principleAdhere to the combination of teaching and education.Self-principleAdhere to the combination of education and self-education.Practical principleAdhere to the combination of political theory education and social practice.Problem principleAdhere to the combination of solving ideological problems and solving practical problems.Management principlesAdhere to the combination of education and management.Innovation principleAdhere to the combination of inheriting fine traditions and improving innovation.Table1 divides the principles of College IAPE into six sections. Adhering to different principles of IAPE, the educational process should act as the knowledge imparting and become a teaching service provider to meet students’ personalized needs and a companion, a motivator, and an emotional caretaker of students. In the new situation, teachers need to invest more emotion and use their thoughts and experience to guide students and help students grow.


## 2.1. AI-Intellectualized IT Theory

AI is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence [19]. It aims is to enable machines to the way people think and reason. AI is a subset of IT and the intersection of natural science, social science, and technical science. Modern IT refers to the technology in which people use binary numbers with different codes to process information through digital equipment, such as computers. AI might involve computer science, psychology, philosophy, linguistics, and other disciplines [20], thus covering almost all-natural science and social science disciplines. Its scope has exceeded the scope of computer science. In particular, AI and thinking science share the same relationship as practice and theory [21]. AI is at the technical application level of thinking science and is one of its application branches. AI presents itself from two dimensions: “artificial” and “intelligence.” The cognitive research of AI is a technical science that enables computers to simulate, extend, and expand human intelligence. IT mainly uses big data-based Machine Learning (ML) algorithms to out-calculate humans. Figure 1 shows the diagram of the AI-intellectualized IT knowledge framework.Figure 1
AI-intellectualized IT knowledge framework.Figure1 illustrates AI-intellectualized IT from intelligent speech recognition (SR), Character Recognition (CR), Machine Translation (MT), and Natural Language Processing (NLP). SR is to convert speech signals into corresponding text or instructions. CR recognizes the text in the image and converts the image into text. MT uses computers to convert one natural language (source language) into another (target language). Currently, MT is also the most active research field in AI, which is based on linguistics, mathematics, and computer science. Linguists provide dictionaries and grammatical rules suitable for computer processing; mathematicians formalize and code the materials supplied by linguists. Computer scientists provide software and hardware equipment for designing MT and programs. Thus, MT is made possible only by integrating the above three aspects, and the effect of MT depends entirely on their joint efforts. So far, MT has achieved some valuable results but is still a far cry in terms of translation quality. Lastly, NLP studies various theories and methods to communicate effectively with humans and computers with natural language. Additionally, Optical Character Recognition (OCR) technology is now mainly employed in CR. Figure 2 specifies the AI-based character recognition process.Figure 2
The AI-based character recognition process.Figure2 outlines the OCR technology process. Specifically, it processes the original document and obtains the image with the scanner or digital camera for image preprocessing. Image preprocessing mainly includes image binary conversion, noise removal, and tilt correction. Next, the OCR software is invoked to recognize characters. Firstly, it opens the image file. Then, the extract feature vectors are roughly classified and finely matched with templates based on the feature template library to determine the algorithm of characters [22, 23]. Secondly, it extracts the character features and the multidimensional features from the character image that are reserved for the later feature matching-oriented pattern recognition algorithm. Postprocessing correction can correct the recognition results according to the relationship of specific language context. The syntax detector can detect whether the combination logic of characters is reasonable. Finally, the document recognition technology can call the algorithms for different input image formats to save the text. A complete AI-intellectualized IT system fuses the AI core and the IT system. The core AI system is the core of the complete AI-intellectualized IT system and deals with problems at the knowledge and intelligence level. In contrast, the IT system is located outside the complete AI-intellectualized IT system and mainly deals with some issues at the information level. At the same time, it serves as the interface between the core AI system and the external environment. For example, the IT system obtains ontology information from the environment and interacts with the intelligent behaviors imposed on the environment at the other end. Meanwhile, the IT system can provide humans with convenient and fast information-sharing services. Figure 3 demonstrates the complete AI-intellectualized IT system.Figure 3
Complete AI-intellectualized IT system.As shown in Figure3, the complete AI-intellectualized IT system comprises the basic, technology, and application layers. The basic layer mainly provides data input and computing power. The technology layer develops the algorithm model on the basis layer, trains and learns through the software framework, and obtains AI technology. The algorithm model is the soul of the AI system. The application layer applies AI technology to different scenarios for commercialization.


## 2.2. College IAPE System Integrating AI-Intellectualized IT

As a general technology, AI can be widely used in various fields, including its integration with education. AI is the core of many virtuous interaction circles, including business data analysis, NLP, SR, machine reasoning, computer vision, robots, and sensors. Surely, applying these technologies in education will help the reform of higher education. Figure4 illuminates the process of college IAPE Based on AI-intellectualized IT.Figure 4
College IAPE process based on AI-intellectualized IT.Figure4 suggests integrating College IAPE and AI-intellectualized IT generating a more accurate analysis result. Before the course, it collects the IAPE videos and recognizes and marks the class video images through IR and SR. Then, it extracts the classroom audio data and classifies them through SR. Afterward, DCT can be used for SR [24, 25]. Next, based on the logarithmic energy of the filter, the Cepstral coefficient can be obtained by DCT, as shown in the following equation (1):(1)Cn=∑m=0N−1smcosπnm−0.5M,n=1,2,…,L,where Cn is the Mel-scale cepstrum parameter of order L. L refers to the order of Mel Frequency Cepstral Coefficient (MFCC). Usually, order 12 can represent the acoustic characteristics. sm denotes the signal after framing, and N stands for the size of the frame. M means the number of triangular filters, and the transfer function of each band-pass filter is calculated using the following equation (2):(2)Hmk=0,k<fm−1,2k−fm−1fm+1−fm−1fm−fm−1,fm−1≤k≤fm,2fm+1−kfm+1−fm−1fm−fm−1,fm≤k≤fm+1,0,k≥fm+1,where Hmk,0≤m≤M represents several band-pass filters set within the sound spectrum range. f(m)is the center frequency of each filter, and in (3), f(m) can be calculated:(3)fm=NfsFmel−1Fmelfl+mFmelfh−FmelflM+1,where fl and fh represent the lowest and highest frequency in the filter frequency range, respectively. N is the length of FFT. f shows the sampling frequency. The corresponding relationship between frequency and Mel-scale is linear below 1,000 Hz and logarithmic above 1,000 Hz. Equations (4) and (5) are the specific calculation processes.(4)melf=2595In1+f700.(5)f=70010m/2595−1.In (4) and (5), f is the frequency, and the unit is Hz. Equation (6) reflects the logarithmic energy output by each filter.(6)sm=In∑k=0N−1Xk2Hmk,where sm is the logarithmic energy output by each filter bank and Xk indicates the time domain signal. Logarithmic processing is performed in the frequency domain, and the logarithm is inversely transformed to obtain the MFCC coefficient, as expressed in (7) and (8).(7)logXk=logHk+logEk,(8)xk=hk+ek.Equation (7) is a logarithmic operation, and Equation (8) is the inverse transformation.Then, the extreme of (7) and (8) is calculated to obtain the coefficient. The above-extracted features are static, while the actual sound is continuous. Therefore, adding features to represent the dynamic changes of continuous sound can be realized by calculating the first-order or second-order difference, as illustrated in the following (9):(9)dt=ct+1−ct−12.In equation (9), d(t) represents the t-the first-order difference and c(t) indicates the t-th Cepstrum coefficient. Secondly, using the automatic analysis and application of AI-intellectualized IT, according to IR and SR results, this study analyzes the teaching process of College Students’ IAPE and the changes in students’ ideology and political views. The video is segmented according to the video image changes in the IR field. Whether each segment belongs to the IAPE teaching process or video playback is judged simultaneously. Then, the video image segmentation data are recorded and stored in the database. IR can be performed through GA [26]. First, appropriate image samples are selected from the video, as computed in the following (10):(10)Pxi=fxi∑j=1Nfxj,where f represents the fitness function. The sample size is N. fxi is the fitness of the sample xi. The SR process converts image files into audio files, extracts sound features, and stores them in the feature list. Then, feature matching is carried out to complete cluster analysis. Finally, the process of IAPE is completed, and the teaching process is analyzed according to the principles of College IAPE. Figure 5 manifests the application of AI-intellectualized IT in College Students’ IAPE.Figure 5
Application of AI-intellectualized IT in College Students’ IAPE.According to the relevant characteristics and technical requirements, in College IAPE, AI-intellectualized IT can not only achieve the knowledge expression of traditional IAPE but also use the Expert System Shell (ESS)-based simple expert system to let students experience the application of AI-intellectualized IT in learning IAPE. Then, it analyzes and solves students’ ideological and political problems. In particular, an Intelligent Computer-Aided Instruction (ICAI) can be used to apply AI-intellectualized IT to the college IAPE system based on a computer medium and cognitive science. Figure6 draws the ICAI design model.Figure 6
ICAI design model.As plotted in Figure6, ICAI can separate the teaching process from the analytic process of College IAPE and pay more attention to systematic reasoning and analysis of students’ IAPE. Integrating the ICAI into higher education can eliminate the existing teaching evaluation dilemma, such as single data, untimely feedback, and neglecting the teaching scene [27]. Through real-time data processing and subtle emotional perception, ICAI can analyze the teaching process and timely feedback targeted information. The expert system module is integrated with the teaching module, and it judges students’ IAPE according to the standards of multiple simulated experts. Figure 7 unfolds the structure of the AI-intellectualized IT expert system.Figure 7
Structure of AI-intellectualized IT expert system.The expert system structure in Figure7 can solve College Students’ IAPE problems. The key to developing an expert system is to use relevant theoretical expert knowledge to analyze the issues of students’ IAPE.


## 2.3. IAPE in Colleges and Universities

College IAPE teachers should well manage AI-intellectualized IT [28]. AI technology provides a wealthy platform, tools, and resources. Teachers can use emotional education to educate students. With the continuous enhancement of students’ interaction with new technologies, students will express more diversified and dynamically changing ideological and political views. Accordingly, teachers should prepare themselves well against students’ dynamic and personalized political opinions and ideology using AI-intellectualized IT [29, 30]. Table 1 lists the principles of College IAPE.Table 1
Principles of College IAPE.
Adhere to principlesEducational focusHumanistic principleAdhere to the combination of teaching and education.Self-principleAdhere to the combination of education and self-education.Practical principleAdhere to the combination of political theory education and social practice.Problem principleAdhere to the combination of solving ideological problems and solving practical problems.Management principlesAdhere to the combination of education and management.Innovation principleAdhere to the combination of inheriting fine traditions and improving innovation.Table1 divides the principles of College IAPE into six sections. Adhering to different principles of IAPE, the educational process should act as the knowledge imparting and become a teaching service provider to meet students’ personalized needs and a companion, a motivator, and an emotional caretaker of students. In the new situation, teachers need to invest more emotion and use their thoughts and experience to guide students and help students grow.


## 3. Results and Discussion

AI-intellectualized IT can change students’ learning mode but can never alter the ultimate goal of learning. In other words, learners should be well aware that AI, as a technological approach, can assist the learning process but can never replace human intelligence. After all, the goal of learning is to improve the student’s ability. Table2 statistically analyzes College IAPE.Table 2
Investigation and statistics of College IAPE.
Ideological and political awarenessUndergraduate (%)Postgraduate (%)Above postgraduate (%)Agree that “everyone is responsible for the rise and fall of the world”79.573.561.4Have social morality consciousness.90.463.857.3Table2 indicates that undergraduates (UGRDs)’ ideological and political awareness is generally stronger than (score >90.4%) postgraduate and above-postgraduate students. This study mainly focuses on UGRDs and the impact of AI-intellectualized IT on UGRDs’ IAPE.


### 3.1. Advantages and Disadvantages of UGRDs’ IAPE Integrating AI-Intellectualized IT

The advantages and disadvantages of applying AI-intellectualized IT to UGRDs’ IAPE have been evaluated from the following six performance indexes including data comprehensiveness, product performance, technology openness, students’ adaptability, enthusiasm, and technology dependence. Figure8 analyzes the advantages and disadvantages.Figure 8
Advantages and disadvantages of UGRDs’ IAPE.Figure8 displays the pros and cons of UGRDs’ IAPE integrated with AI-intellectualized IT. The advantages outweigh the disadvantages. Specifically, the data comprehensiveness, product performance, and technology openness have shown a maximum of 67.36% advantageous. Students’ adaptability has presented a minimum of 35.21% advantageous. By contrast, the technology openness has shown a 68.53% disadvantageous. Students’ enthusiasm has demonstrated a 26.32% disadvantageous. Overall, technology openness accounts for the most, 57.21%, and the students’ enthusiasm accounts for the least, 30.77%.


### 3.2. Concerns and Gains of UGRDs’ IAPE Integrating AI-Intellectualized IT

The concerns and gains of UGRDs’ IAPE integrated with AI-intellectualized IT can be evaluated from comprehensive ability, innovation, development, and coverage. Figure9 analyzes the concerns and gains of UGRDs’ IAPE.Figure 9
Concerns and gains of UGRDs’ IAPE.As indicated in Figure9, the biggest gain of UGRDs is the improvement of technology development, which is 71.17%, followed by the improvement of students’ comprehensive ability, which is 65.53%. The smallest gain is the coverage of technology, which is 36.80%. Students mainly focus on the innovation performance of technology, accounting for 75.92%, followed by the development of technology, accounting for 67.57%. Finally, the ability and technology coverage account for 46.80% and 45.34%, respectively.


### 3.3. Influence of AI-Intellectualized IT on College Students’ IAPE

According to the educational background, college students are divided into UGRD, postgraduate, and higher grades. The impact of IAPE on UGRDs in integrating AI-intellectualized IT is assessed from the diversification of teaching methods, accuracy of teaching plans, personalized lesson content, intelligent data resources, scientific evaluation methods, dynamic teaching process, intelligent teaching environment, and reducing classroom burden. Figure10 depicts the impact analysis of College Students’ IAPE.Figure 10
Analysis on the influence of College Students’ IAPE.In Figure10, technology has the greatest impact on UGRDs, with an average impact of 89.04% and 85.78% on postgraduate or higher. The largest impact of diversified teaching methods on all College Students is 62.48%. The impact of the accuracy of teaching schemes, personalized lesson content, intelligent data resources, scientific evaluation methods, dynamic teaching process, intelligent teaching environment, and classroom burden reduction is 53.97%, 52.63%, 49.26%, 44.18%, 49.32%, 51.62%, and 29.90%, respectively.


## 3.1. Advantages and Disadvantages of UGRDs’ IAPE Integrating AI-Intellectualized IT

The advantages and disadvantages of applying AI-intellectualized IT to UGRDs’ IAPE have been evaluated from the following six performance indexes including data comprehensiveness, product performance, technology openness, students’ adaptability, enthusiasm, and technology dependence. Figure8 analyzes the advantages and disadvantages.Figure 8
Advantages and disadvantages of UGRDs’ IAPE.Figure8 displays the pros and cons of UGRDs’ IAPE integrated with AI-intellectualized IT. The advantages outweigh the disadvantages. Specifically, the data comprehensiveness, product performance, and technology openness have shown a maximum of 67.36% advantageous. Students’ adaptability has presented a minimum of 35.21% advantageous. By contrast, the technology openness has shown a 68.53% disadvantageous. Students’ enthusiasm has demonstrated a 26.32% disadvantageous. Overall, technology openness accounts for the most, 57.21%, and the students’ enthusiasm accounts for the least, 30.77%.


## 3.2. Concerns and Gains of UGRDs’ IAPE Integrating AI-Intellectualized IT

The concerns and gains of UGRDs’ IAPE integrated with AI-intellectualized IT can be evaluated from comprehensive ability, innovation, development, and coverage. Figure9 analyzes the concerns and gains of UGRDs’ IAPE.Figure 9
Concerns and gains of UGRDs’ IAPE.As indicated in Figure9, the biggest gain of UGRDs is the improvement of technology development, which is 71.17%, followed by the improvement of students’ comprehensive ability, which is 65.53%. The smallest gain is the coverage of technology, which is 36.80%. Students mainly focus on the innovation performance of technology, accounting for 75.92%, followed by the development of technology, accounting for 67.57%. Finally, the ability and technology coverage account for 46.80% and 45.34%, respectively.


## 3.3. Influence of AI-Intellectualized IT on College Students’ IAPE

According to the educational background, college students are divided into UGRD, postgraduate, and higher grades. The impact of IAPE on UGRDs in integrating AI-intellectualized IT is assessed from the diversification of teaching methods, accuracy of teaching plans, personalized lesson content, intelligent data resources, scientific evaluation methods, dynamic teaching process, intelligent teaching environment, and reducing classroom burden. Figure10 depicts the impact analysis of College Students’ IAPE.Figure 10
Analysis on the influence of College Students’ IAPE.In Figure10, technology has the greatest impact on UGRDs, with an average impact of 89.04% and 85.78% on postgraduate or higher. The largest impact of diversified teaching methods on all College Students is 62.48%. The impact of the accuracy of teaching schemes, personalized lesson content, intelligent data resources, scientific evaluation methods, dynamic teaching process, intelligent teaching environment, and classroom burden reduction is 53.97%, 52.63%, 49.26%, 44.18%, 49.32%, 51.62%, and 29.90%, respectively.


## 4. Conclusion

Students must participate in the creation of ethnic education and embrace appropriate norms, policies, values, and processes for academics to continue to progress positively for intellectual Ideological and Political Learning exploration and study. This study explores the integration of AI-intellectualized IT into College Students’ IAPE, the advantages and disadvantages of College IAPE integrated with AI-intellectualized IT along with concerns and gains analysis, and the impact on education through AI-intellectualized IT. The results verify that the advantages of integrating AI-intellectualized IT with IAPE in colleges and universities outweigh the disadvantages. The biggest gain for UGRDs is the improvement of technology development, 71.17%, followed by students’ comprehensive ability improvement, which is 65.53%, and the smallest yield is the coverage of technology, which is 36.80. Most students focus on the innovation performance of technology, accounting for 75.92%. Technology has the greatest impact on UGRDs, with an average impact of 89.04% and 85.78% with postgraduates or above. Overall, the largest impact of diversified teaching methods is 62.48% on all students. This study has specific reference significance for the research and analysis of AI-intellectualized IT and College Students’ IAPE. However, because of the limitations of AI-intellectualized IT in College Students’ IAPE, initiatives are required to understand the limitations of AI and learn to use technology correctly in a reasonable scene.



---

*Source: 4844565-2022-05-18.xml*