Reflections on Artificial Intelligence Enabling the Precision Development of Ideological and Political Education in Colleges and Universities

In today’s era of rapid development of science and technology, artificial intelligence (AI), with its powerful functions and wide range of applications, is profoundly affecting various fields, and ideological and political education is no exception. The emergence of AI has brought new opportunities for ideological and political education, but also a series of challenges for change [1-4]. How to make full use of the advantages of AI and actively respond to the challenges it brings has become an important issue to be solved in the field of ideological and political education [5-6].

Artificial intelligence technology can realize personalized education mode. Through big data analysis and machine learning algorithms, AI can customize exclusive learning programs for each student according to their learning habits, interests, knowledge level and other factors [7-10]. This kind of personalized education can better meet the individual needs of students and improve the relevance and effectiveness of ideological and political education.AI can integrate a huge amount of educational resources, including text, pictures, videos and other forms [11-13]. Ideological and political educators can use these rich resources to teach students more vividly and vividly, make the educational content more colorful, and stimulate students’ interest in learning [14-15]. In addition, with the automation and intelligence characteristics of artificial intelligence, it can quickly process and analyze a large amount of data, such as students’ learning situation, ideological dynamics and so on. This helps educators to understand the situation of students in a timely manner, so as to carry out educational work more efficiently [16-19]. However, AI has brought challenges to ideological and political education, mainly including the technological divide, data security and privacy issues [20].

This paper puts the research on precision of ideological and political education in colleges and universities under the background of artificial intelligence, and builds a general framework for precision management of ideological and political education in colleges and universities through the technical support of data acquisition, learning status characterization, effectiveness prediction, analysis and evaluation, and pedagogical decision-making. On this basis, it further explains how to realize the precise education program. Through extensive questionnaire research in colleges and universities in Province Z, a huge amount of data information is collected and analyzed in depth, and through statistical charts, the knowledge and demand of the survey respondents on the precise education of college civic politics are intuitively fed back. Precision education is applied in practice, and its feasibility and effectiveness are compared and analyzed.

2

Artificial Intelligence Enabling the Development of Precision in Civic Education in Colleges and Universities

2.1

Precision Teaching Framework

Precision of ideological and political education means that under the guidance of precise thinking and with the help of information technology and artificial intelligence technology, targeted education is adopted to promote ideological and political education in terms of quality, quantity, and degree to the goal of fine and accurate development, to achieve a basic balance between supply and demand in education, so as to realize the process of precise human education. The universal framework design for the precise management of school ideological and political teaching consists of data acquisition, learning state characteristics, effectiveness prediction, analysis and evaluation, and teaching decision-making, and the precise teaching framework is shown in Figure 1. Among them, data acquisition is an important guarantee for learning state characterization, and learning state characterization is a prerequisite for learning effectiveness prediction.

2.2

Customized and precise educational programs

The process of customizing the precise education program is shown in Figure 2. The traditional approach to ideological and political education is one-on-one and passive, often based on the student’s final examination results and the psychological state that is presented on the surface for general or individual guidance or conversation. In contrast, accurate ideological and political education based on big data technology makes use of the collation and analysis of students’ learning behaviors and daily behavioral trajectories to generate student portraits that belong exclusively to different individuals, thus enhancing the accuracy of ideological and political education. In general, a complete student portrait is not a simple “input - output” process, but a complex system involving teachers, students, the ubiquitous network environment, and at the same time containing the collection of educational big data, data mining platforms, and other elements of the synergistic efforts. Ideological and political education is to do human work, should recognize, grasp and guide the real needs of individuals, accurately grasping the actual needs of the education object and the reality of the pattern is an important link to realize the precise ideological and political. Only by responding to and solving the needs of education targets in a timely manner from a practical point of view can we continue to improve their sense of gain and happiness and enhance the effectiveness of ideological and political education. In view of the increasingly diversified and multi-level needs of the current education targets, we should conduct deeper excavation and prediction of students’ needs according to the goals and laws of ideological and political education in colleges and universities.

After comprehensively considering the personality characteristics and real needs of the education target, customized and precise education programs can help educators to give full play to the advantages of the ideological and political workforce in a more accurate and efficient way, grasp the changing ideological dynamics of the students in real time and analyze them accurately, and satisfy the needs of the education target in the new era to the maximum extent possible.

1)

Analyzing the differences in the needs of educational targets. The individual needs of the educational target may seem to be their personal needs, but in fact, they also reflect the needs of certain specific groups. Based on the collation and analysis of the real needs of education targets, educators should focus on the fundamental interests of education targets and pay attention to the individual characteristics of each education target. In the face of their specific differences, they should maximize their strengths and potentials to meet their diversified and precise development needs, promote their comprehensive development, and satisfy the real needs of self-realization. Only “when the demand and desire for education and self-education are stimulated, young students’ active passion for knowledge can meet our planned and systematic educational indoctrination, thus realizing the educational practice that shocks the heart and directs to the heart”. That is to say, educators only active understanding of the needs and desires of the education object and targeted guidance, in order to better explore the hidden data information, mining the future needs of the education object.

2)

Demand prediction model. Educational targets need to test the conclusions of the prediction model according to their own actual situation, and put forward evaluation and feedback suggestions, at the same time, the behavioral data in the feedback process will also be stored and recorded by the system as a reference for the next round of optimization data. Among them, the careful selection of predictive factors is a strong guarantee to improve the accuracy of prediction. At this point, the small data collected from the third party is particularly important. Take the academic resource retrieval of Zhi.com as an example, the college student group often generates the motivation to retrieve papers based on academic research or graduation needs, and in the process of retrieval, Zhi.com also provides detailed records of their academic needs, retrieval directions, and other information, so that as long as they integrate and analyze their historical query records and small data of their learning behaviors, the accurate judgment of the research work they are currently carrying out, so as to achieve the purpose of precise customization and precise push. Finally, to meet the reasonable demands of education objectives. After understanding the precise information needed by the educational targets, ideological and political educators should screen the multifaceted and multi-level needs of the educational targets on the basis of the actual teaching status quo, the expectation of effectiveness, the possibility of operation, etc., and formulate the teaching program in combination with the educational objectives and teaching direction to realize the principle of “Learning what they want, and responding to what they want” and “Learning what they want, and responding to what they want, and responding to what they want”. The goal of “I will respond to what you want to learn” and “I will provide what you want to learn” can be achieved, thus maximizing the satisfaction of the individual needs of the target students and enhancing their motivation and initiative in learning.

3

Methods of realizing teaching precision

3.1

Learning effectiveness analysis and assessment

In order to achieve dynamic and precise teaching management, learning effectiveness assessment should also be relatively flexible. The traditional assessment method of regular examination is difficult to adapt to the relatively dynamic and precise teaching management requirements, and cannot reflect the teaching effect of teachers and the learning characteristics of students in a timely manner. And based on the Internet platform technology, students’ behavioral performance, physical data and test scores in the learning process can be used for dynamic analysis to provide effective data support for teaching guidance in time. Regarding the assessment of learning effectiveness, there are mainly test assessment methods, homework assessment methods, classroom performance assessment methods, project assessment methods, comprehensive assessment methods, etc. In the test assessment method, teachers can set different test questions according to the teaching content to understand the mastery level of students, and discover the deficiencies of students’ learning process in time, so as to provide guidance and counseling. Assignment assessment method, teachers through the correction of homework, to understand the degree of understanding and application of knowledge, to guide students to self-assessment, so that students reflect and summarize the experience and lessons.

3.2

Predictive modeling of learning effectiveness

Learning effectiveness prediction is the basis for accurate management of teaching and learning. Learning effectiveness prediction helps to evaluate the effectiveness of teaching and learning, which is crucial for improving teaching strategies. The state and characteristic information of students in the learning process constantly changes, which also provides an important data base for predicting learning effectiveness. In this paper, we propose to use linear regression analysis to predict learning effectiveness, conduct correlation analysis, and carry out a significance test based on the influence of learning state characteristics on learning effectiveness.

Students’ Civics performance is affected by a variety of comprehensive factors, and the random expression of the overall regression prediction function is as follows: (1) $\hat{Y} = β_{0} + β_{1} X_{1} + β_{2} X_{2} + \dots + β_{i} X_{i} + μ_{i} i = 1, 2, 3, \dots, n$

Neglecting random uncertainties such as field environment, psychological condition, etc., and the most important factor is the physical fitness condition, the non-random prediction function expression model can be set as follows: (2) $\hat{Y} = β_{0} + β_{1} X_{1 i} + β_{2} X_{2 i} + \dots + β_{k} X_{k i} i = 1, 2, 3, \dots, n$

The general form of the multiple linear regression model can be expressed as: (3) $\begin{array}{l} \hat{Y} = β_{0} + β_{1} X_{1} + β_{2} X_{2} + \dots + β_{i} X_{i} + μ_{i} \\ i = 1, 2, 3, \dots, n \end{array}$

where Y is the dependent variable, $\hat{Y}$ is the predicted value of the dependent variable, X_i is the independent variable, β₀ is the intercept, β_i is the regression coefficient of the i th independent variable, and μ is the error term. By fitting the multiple linear regression model, the coefficient estimates of each independent variable and the intercept term can be obtained, and these coefficients can be used to explain the relationship between the independent and dependent variables.

3.3

Instructional Decision Modeling

Teaching decision-making is the key to precise management of teaching. Traditional Civics teaching is often constrained by the personal teaching experience and practical knowledge of teachers. In addition, Civics teachers are affected by tedious transactional work and heavy teaching tasks, so it is difficult for them to pay full attention to the changes in the learning status of each student and realize all-round guidance for each student. The use of the Internet and big data technology to dynamically make teaching decisions based on in-depth analysis of students’ learning characteristics is an important way to achieve accurate management of teaching.

Suppose, the whole teaching process is divided into a number of phases denoted as K , where k(k = 1,...k,). Where the learning state characteristics are denoted as X, the learning effectiveness is denoted as Y , the teaching guidance is denoted as P , and the teaching decision function is denoted as d, and the teaching guidance can be chosen to perform a certain kind of guidance or not at the specific teaching phase. At this point, in the first learning stage G_l, the initial learning state characteristic X₁, the instructional decision d₁, the choice of instructional guidance A₁, and the learning outcome Y₁. In the second learning stage G₂, the learning state characteristic X₂, the instructional decision d₂, the choice of instructional guidance A₂, and the learning outcome Y₂. analogously, in the K th stage G_k, the learning state characteristic X_k, the instructional decision d_k, the choice of instructional guidance A_k, and the learning outcome Y_K. where G_k, X_k, A_k, and Y_k are random variables and d_k is the decision function.

The learning state characteristics X_k of each instructional stage G_k are often influenced by a combination of the learning state X_k–1 and instructional guidance A_k−1 of the previous instructional stage. At this point, the final learning outcome Y_k is closely related to each teaching stage. Accordingly, the correspondence between teaching stage d_k, teaching decision function d_k, and teaching guidance A_k can be expressed as follows: (4) $d_{k} (G_{K}) = A_{k}$

Through transformation, the correspondence between instructional guidance and instructional decision function can be obtained as: (5) $A_{k} = d_{k} (G_{K})$

At this point, the problem of managing teaching accuracy is transformed into the problem of solving the optimal decision function d′ = (d₁,d₂,...,d_k) and obtaining the optimal learning effectiveness Y, denoted as: (6) $d' = \max [Y | A_{1} = {d^{'}}_{1} (G_{1}), A_{2} = {d^{'}}_{2} (G_{2}), \dots, A_{K} = {d^{'}}_{k} (G_{k})]$

4

An empirical study of precise teaching interventions in Civics in colleges and universities

4.1

Analysis of the Precision Situation of Ideological and Political Education in Colleges and Universities

Based on the questionnaire research and interviews, it is possible to further accurately grasp the current problems of ideological and political education in colleges and universities in terms of precision. Combined with the relevant findings of this study and the conclusions of other scholars, it can be seen that the ideological and political education in colleges and universities in the new era has achieved certain results in terms of precision, but there are still many problems in the current situation that need to be improved.

After polishing the questionnaire title many times, to ensure the effectiveness of the questionnaire, the survey object is the students of colleges and universities in the region of Z province. To ensure the breadth and relevance of the survey object, this study uses the platform of questionnaire star to make online questionnaire links and pictures, and carries out the survey by online invitation to fill in, network group forwarding diffusion, etc., to break the limitation of space and time, and a total of 570 valid questionnaires were returned. Among them, the overall situation of the survey sample is shown in Table 1: in terms of gender, male students accounted for 43.86%, female students accounted for 56.14%. In terms of political affiliation, members of the Communist Youth League (CYL) dominated, accounting for 73.68%, followed by members of the Communist Party of China (CPC), accounting for 16.14%, and the democratic parties and the masses accounted for 0.7% and 9.47%, respectively. In terms of school category, it covers all levels of institutions, among which students of key institutions account for 17.19%, students of general undergraduate institutions account for 55.79%, and students of private schools account for 27.02%, which is a wide sample distribution. The sample of students’ professional categories covers all major professional categories, with 33.33% of students majoring in humanities, 36.84% of students majoring in social sciences, 23.51% of students majoring in science and technology, and 6.32% of students majoring in arts and sports, with a relatively even sample distribution. In summary, combined with the detailed data, it is found that the sample taken for this survey covers a wide range and is relatively even. Although there is a gap between the proportion of individual items, it is within a reasonable range and does not impact the overall survey effect. The overall structure of the questionnaire sample is reasonable and has certain reference values.

Table 1.

Sample structure generalization

Project	Dimension	Person-time	Proportion
Gender	Female	250	43.86%
Gender	Male	320	56.14%
Political appearance	League member	420	73.68%
	Party member	92	16.14%
	Democrats	4	0.70%
	masses	54	9.47%
School class	Key school	98	17.19%
	General under graduate	318	55.79%
	Private school	154	27.02%
Professional category	Human literature	190	33.33%
	Social sciences	210	36.84%
	Science and engineering	134	23.51%
	Art sports	36	6.32%

College students have varying demands for the content, methods, and channels of ideological and political education. Analyzing the demand of college students is the basis for understanding whether the supply of ideological and political education in colleges and universities is accurate, and it can provide a comparative reference basis for the supply of ideological and political education. Therefore, this questionnaire is designed with relevant questions in order to understand the specific needs of college students for ideological and political education in colleges and universities. The survey results are shown in Figure 3.

When asked “what aspects of ideological and political education in colleges and universities are expected to satisfy their own needs”, options 1~4: cultural needs account for 56.62%, ability needs account for 84.7%, spiritual needs account for 40.2%, and others account for 7.4%, which shows that college students’ needs are diversified, and they hope that their demands in various aspects can be satisfied.

When counting the respondents’ demands for the improvement of ideological and political education ability, options 1~5: 58.7% for employment ability, 63.2% for psychological quality, 59.4% for learning ability, 65.3% for humanistic cultivation, 64.1% for interpersonal communication, and 1% for others, thus it can be seen that college students expect to improve themselves in many aspects, such as employment ability, psychological quality, learning ability, etc., and the ability to improve themselves in many aspects. This shows that college students mainly expect to improve themselves in their employment ability, psychological quality, learning ability, and other aspects, and the demand for improvement in these areas is more extensive.

In terms of the demand for the content of ideological and political education, options 1~6: 65.4% of the respondents want to learn about the Party’s major policies, 67.2% want to learn about science and technology, history, psychology, and other general knowledge, 62.3% want to learn about the situation at home and abroad, 51.8% want to learn about laws and regulations related to their direct interests, and 63.4% want to learn about the reasoning of standing up for themselves and being a human being. Respondents’ needs for the content of ideological and political education are more diverse. Others were responsible for 12%.

In terms of ideological and political education method preference (demand), options 1~6: 14.1% of the respondents prefer practical education method, 38.2% prefer self-education method, 17.3% prefer typical education method, 9.4% prefer theoretical education method, and a small proportion of the respondents preferred diversionary education method (15.7%) and motivational education method (7%).

In terms of ideological and political education pathway preference (demand), options 1~10: preference for new media platforms, ideological and political theory classes, exemplary role of student cadres, psychological counseling, campus culture construction, preference for counselor education, mentor education, preference for on-campus activities, social practice, and others. Those who prefer on-campus activities, social practice, campus culture construction, ideological and political theory classes account for more than 40%, those who prefer new media platforms and psychological counseling range from 35% to 40%, and those who prefer counselor education, exemplary roles of student cadres, and mentor education are relatively few, but they are all above 20%. It can be seen that college students have diversified demands for ideological and political education and expect comprehensive development. Accordingly, the precise supply of ideological and political education in colleges and universities needs to be based on the overall goal of cultivating the new man of the times, and accurately grasp the needs of college students for the content and methods of ideological and political education, and improve them in these aspects.

4.2

Empirical Research on Civic and Political Precision Education in Colleges and Universities

4.2.1

Comparison of achievements

The data for this empirical study come from two main sources: students’ learning records on Wisdom Learning Online and Civics Classroom at a university in the region of Province Z. Students’ learning records on WISE are the data of students’ learning process in WISE, including the number, duration and correct rate of students practicing different topics such as study questions, orientation questions and easy-to-learn and easy-to-fail questions, and other indicators. The time span is the entire academic year, and the study records include records of students who have completed thirteen Civics exercises on ZhiXueNian, totaling 680 entries. The Civics classroom data are the Civics exam results and the corresponding grade rankings, including the results of three exams, the entrance exam one month after the start of the school year, the final exam of the first semester, and the final exam of the second semester.

First, the change in students’ civics performance in class during the three exams, namely the entrance exam, the first semester final exam, and the second semester final exam, is analyzed. The comparison of the students’ Civics scores in the entrance exam respectively in the first semester final exam and the second semester final exam is shown in Figure 4. In the entrance exam scores, except for some low scores (20-40 segments), the overall scores of the students are close to the normal distribution. In the final exam of the first semester, the distribution of students’ overall scores was not much different from that of the entrance exam. The final exam of the second semester saw a significant increase in the number of students in the high score bands (80-120), while the number of students in the low score bands decreased significantly. By comparing the changes in students’ scores on the three exams, it can be seen that the overall academic performance of the students in this class was significantly improved during the course of the academic year in which the Precision Exercise Intervention was carried out.

At the same time, this study also analyzes the grade rank of the students’ Civics scores in class, taking into account the differences in the difficulty of the questions in different exams. The total number of students in this class is 1840, and the comparison of box plots of the grade rankings of the Civics and Political Science scores of the 56 students in this class in the three exams, namely, the entrance exam, the final exam of the first semester and the final exam of the second semester, is shown in Figure 5. It can be seen that the grade rankings of the three exam scores increase one by one, and the grade rankings of the final exam scores of the second semester increase significantly. The paired samples test was conducted on the three exam grade rankings of the class two by two (i.e., the entrance exam and the first semester final exam, the entrance exam and the second semester final exam, and the first semester final exam and the second semester final exam were tested sequentially, and the p-values of their probability of significance were obtained as 0.046, 0.004, and 0.18, respectively) Thus, at the significance level of 0.05, the relationship between the entrance exam and the first semester and second semester final exam grade rankings was significant. Therefore, at the significance level of 0.05, there is a significant difference between the grade ranks in the entrance examination and the final examination of the first and second semesters. In conclusion, the overall learning effect of the students in this class was significantly improved after one academic year of precise problem intervention.

4.2.2

Learning Effectiveness and Correspondence Behavior Analysis

Learning effectiveness analysis and prediction of effectiveness using students’ behavioral performance during the learning process, such as frequency of entry and eating on time. Preliminary analysis of the dataset showed that students with different academic performance had different behavioral patterns. Three behavioral patterns corresponding to different academic performance were compared as shown in Figure 6. In the figure, POOR denotes students with poor academic performance, i.e., they are in the 0.05 quartile, Medium and Excellence are students with moderate (0.5 quartile) and excellent (0.95 quartile) academic performance, respectively. The results showed that a combination of academic effectiveness analysis and adult prediction found that students with higher GPAs ate meals on time more frequently, studied, and attended the library more frequently. Understanding these differences has important implications for individualized education because it is the foundation and life of teachers and administrators to identify factors that contribute to high or poor performance, detect unexpected and unusual behaviors in a timely manner, and implement individualized interventions when necessary to promote student learning.

The aim of conducting precise instructional interventions is to promote student learning and assist students in improving their learning outcomes. Learning effectiveness is an important indicator of learning effectiveness, timely and accurate prediction of learning effectiveness in the teaching process is to carry out precision teaching thousand pre guarantee, the effective implementation of precision teaching intervention is of great significance. The dynamic student learning status and characteristics established in the teaching process and constantly updated can reflect the learning situation of students in the teaching process in a timely manner, which is an important basis for accurate prediction of learning effectiveness.

5

Conclusion

This paper constructs a framework of precision of ideological and political education in colleges and universities through the techniques of data acquisition, learning state characteristics, effectiveness prediction, analysis and evaluation, and teaching decision-making, and conducts research on the practice of precision teaching in the curriculum. Through the questionnaire survey and experimental comparison method, college students have diversified characteristics of content demand, ability demand, and so on. For precise ideological and political education. In the future, it is important to strengthen the enhancement of educational content and methods. By comparing the changes of students’ three times’ Civics and Political Science examination scores, it can be found that the precise teaching can significantly improve students’ scores (P<0.05). In the analysis of different behavioral patterns, it was found that students with higher grades had a correlation between eating meals on time, studying, and entering the library more frequently. Therefore, precise teaching helps students develop in all aspects of their abilities.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Reflections on Artificial Intelligence Enabling the Precision Development of Ideological and Political Education in Colleges and Universities

Baochun Wang

Published Online: Mar 17, 2025

Received: Oct 05, 2024

Accepted: Jan 30, 2025

DOI: https://doi.org/10.2478/amns-2025-0304

KeywordsInstructional decision-making, Precision teaching, Artificial intelligence, Effectiveness prediction

© 2025 Baochun Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Instructional decision-making, Precision teaching, Artificial intelligence, Effectiveness prediction