Exploration and Research on the Integration of Artificial Intelligence and Metacosmos into Innovation and Entrepreneurship Education and the Integration of Industry, Science, Innovation and Education

Innovation and entrepreneurship is a strong driving force for social development. Innovation and entrepreneurship educators need to think about how to build an innovation and entrepreneurship education that is conducive to the promotion of Chinese-style modernization, so as to better serve the overall situation of Chinese-style modernization [1–3]. Nowadays, the digital era has arrived, the application of digital technology is profoundly affecting all aspects of people’s life and work, and digital thinking and digital technology have become an important engine of social development.

In this context, artificial intelligence technology provides unprecedented opportunities for innovation and entrepreneurship education in colleges and universities, which can not only improve teaching efficiency, but also promote students’ personalized learning and practical ability [4–5]. Artificial intelligence, through data analysis, machine learning and other technologies, can provide personalized learning paths and resource recommendations for students’ different backgrounds, interests and learning needs [6–9]. The application of artificial intelligence can create real entrepreneurial practice scenarios for students and make up for the lack of practical experience in traditional teaching [10–11]. Artificial intelligence can also optimize the curriculum of innovation and entrepreneurship courses in colleges and universities, and provide students with accurate guidance and real-time feedback through the intelligent tutor system to help them avoid potential risks in entrepreneurship [12–14]. In college innovation and entrepreneurship education, AI is not only a tool, but also an enabler, which promotes the transformation of the education model from traditional indoctrination to interactive and practical, significantly improves the quality of education, and promotes the cultivation of high-quality and innovative entrepreneurial talents [15–18].

In addition, research on the application of digital technology in education, such as meta-universe, has emerged, and the study of innovation and entrepreneurship education meta-universe is beneficial to the transformation and change of innovation and entrepreneurship education itself and its quality upgrading, which is beneficial to the promotion of the prosperity of innovation and entrepreneurship practice in society as a whole [19–21]. Innovation and entrepreneurship education itself emphasizes the cultivation of students’ digital thinking and digital worldview, and the development of digital productivity in the process of innovation and entrepreneurship [22–23]. In turn, the meta-universe synthesized by multiple digital technologies and digital social rules is bound to bring qualitative changes to the development of innovation and entrepreneurship education [24–25].

The study designed that artificial intelligence technology can be integrated into innovation and entrepreneurship education from three aspects: teaching management, curriculum teaching, and teaching evaluation. Metacosmos can play the role of virtual-real integration, deep immersion and interactive collaboration in innovation and entrepreneurship education. On this basis, we explore the integration of industry, science, innovation and education, and design an improved AHP-Fuzzy method to evaluate the degree of integration of industry, science, innovation and education. According to the weight of each index, the established judgment set to determine the evaluation index affiliation matrix. According to the principle of maximum affiliation, the grade of industry-technology-creation-education is judged, and relevant suggestions for improving the integration of industry-technology-creation-education are put forward.

2

Artificial intelligence and meta-universe technologies in innovation and entrepreneurship education

2.1

The Path of Artificial Intelligence Practiced in Innovation and Entrepreneurship Education

2.1.1

Paths to Artificial Intelligence Enabled Instructional Management

1)

Intelligent Class Schedule Management

The Intelligent Class Schedule Management System optimizes the scheduling process by applying artificial intelligence technology, using heuristic algorithms, genetic algorithms, or multi-objective optimization algorithms to achieve a more reasonable course schedule. The system collects information about the available time for teachers, students and classrooms, as well as information about the courses, and searches for the optimal or near-optimal scheduling plan under the premise of satisfying the constraints.

2)

Faculty Reminder System

The faculty reminder system utilizes artificial intelligence and automation technology to send timely reminders about teaching activities to students and teachers to improve teaching efficiency and student satisfaction [26].

The Academic Affairs Reminder System analyzes teaching activity data through natural language processing technology to automatically generate personalized reminders. Students and teachers can customize the content of reminders and set reminders about courses, exams and deadlines according to their own schedules and preferences, effectively reducing the risk of missing key teaching activities.

3)

Teaching Calendar Management

Academic calendar management involves the planning and tracking of course schedules, exam times, vacations, and other important academic activities. Teaching calendar management coordinates resources and ensures that teaching resources such as classrooms and laboratories are allocated and utilized effectively. Increase transparency and provide students and faculty with a clear view of instructional activities. Reduces scheduling conflicts for courses, exams, and activities, helping students and teachers plan their time and activities in advance.

2.1.2

Paths to Artificial Intelligence-Enabled Curriculum Instruction

1)

Intelligent Recommender System

Intelligent recommender systems are an innovative application of AI technology in the field of education, which provide personalized learning resources and paths by analyzing students ’ behaviors and preferences. The core of such a system lies in its ability to understand the unique needs of each student and thus recommend course content, teaching methods and learning materials that best suit them. Intelligent recommender systems are also able to dynamically adjust their recommendations based on student progress and feedback. By analyzing students’ behaviors and preferences and providing personalized learning resources and paths, it not only improves students’ learning efficiency, but also enhances their learning motivation and engagement, which is of great significance for cultivating students’ innovation and entrepreneurship.

2)

Knowledge mapping technology

Knowledge mapping technology is another important application of artificial intelligence technology in the field of education. It demonstrates the correlation relationship between different knowledge points by constructing a structured network of knowledge points, which enables students to understand the whole knowledge system in a more systematic way. In innovation and entrepreneurship education, knowledge mapping can help students identify connections between different fields, such as the interaction between technology and market, or the relationship between product design and user experience. Utilizing knowledge mapping, AI systems can intelligently recommend the next step in students’ learning based on their mastery of a particular knowledge point, guiding them to explore deeper along the knowledge network. This approach not only deepens students’ understanding of individual knowledge points, but also promotes the formation of their interdisciplinary thinking, which is particularly important for innovation and entrepreneurship education.

2.1.3

Paths to Artificial Intelligence-Enabled Teaching Evaluation

1)

Collecting data to realize dynamic assessment

Artificial intelligence technology is able to realize dynamic assessment of students’ innovation and entrepreneurship ability by collecting data on students’ online learning behavior and project practice. This type of assessment is not limited to a single test score or subjective evaluation by teachers, but comprehensively assesses students’ innovative and entrepreneurial ability by analyzing their various interactions, participation and ability to solve practical problems in the learning process. The system can track how much students contribute to team projects, how innovative thinking is demonstrated, and how they apply what they have learned to solve real-world problems. Such data collection and analysis provides educators with a multi-dimensional assessment perspective.

2)

Utilizing Artificial Intelligence Tools for Quality Assessment

The comprehensive utilization of artificial intelligence tools provides a new way to assess the quality of students’ innovation and entrepreneurship. The system is able to analyze students’ learning status and output, and assess their key qualities such as innovative thinking, risk management ability, teamwork spirit, and market insight. The application of artificial intelligence technology makes the assessment of these qualities more scientific and objective. In addition, the AI system is able to provide educators with real-time feedback and suggestions based on the assessment results, helping them to adjust their teaching strategies in a timely manner, as well as promoting students’ self-reflection and improvement.

2.2

The path of meta-universe practice in innovation and entrepreneurship education

2.2.1

Integration of virtual and real, expanding the practice scene of innovation and entrepreneurship

With the help of digital twin technology to create a virtual world that maps the real world, Yuan Universe restores the different scenarios of small and micro-enterprise creation and operation with high precision through cross-platform information streaming and feedback devices, simulating the formation of entrepreneurial teams and operations such as strategic management, product management, supply chain management, financial management, human resources management and other operational aspects. Compared with traditional innovation and entrepreneurship education, the virtual world of meta-universe visualizes abstract thinking, realizes “visible is knowable” and “thinkable is tryable”, breaks through the time and space boundaries of traditional innovation and entrepreneurship education, and allows teachers and students to carry out formal and informal learning in different scenarios and at different times. Teachers and students can carry out formal and informal learning in different scenes and at different times. Moreover, the system of virtual-reality integration breaks the boundaries between the real world and the virtual world, and the learning content and learning outcomes can be freely converted in the two worlds. Entrepreneurial practices that are difficult to carry out in the real world can be carried out in the virtual world, and the learning outcomes in the virtual world will be materialized in the real world.

2.2.2

Deep immersion to unlock the embodied experience of innovation and entrepreneurship

The meta-universe mobilizes a variety of senses, such as sight, hearing, and touch, to enable students to experience different aspects of business creation and operational management. The signals from each sense are transmitted to the external area of the brain, where they are integrated and sent to the core of the brain to form the cognition of innovation and entrepreneurship. The interaction of multi-sensory and high simulation virtual environment has a strong sense of presence and embodiment, which triggers students’ mental simulation and experiential learning process, and the immersive environment constructed by the meta-universe shields out the external interference, so that students are easy to enter into the highly focused state of mind flow.

2.2.3

Interaction and collaboration to develop teamwork in innovation and entrepreneurship

The meta-universe is decentralized and highly scalable, and supports group collaborative manipulation and distributed collaboration. Each user generates a mirror subject, which can interact with each other and with the real subject. Students’ mirror subjects play different job roles, communicate and collaborate on problematic situations in the entrepreneurial process, and analyze and discuss problem solutions. The mirror subject can learn independently or form a learning community with other mirror subjects to explore problems in interaction and cooperation. The real subject makes a prediction of its own learning behavior by observing the interactive results of the mirror subject, and then targets to improve the real-world collaboration. Meta-universe language transformation technologies, such as the XLS-R speech training self-supervised model released by Meta, can help people with different native languages to talk directly in the meta-universe, facilitating broader cross-space communication and collaboration.

3

Evaluation of the degree of integration of obstetrics, science, innovation and education based on improved AHP-Fuzzy

Integration of industry, science, innovation and education, i.e. the organic combination of “industry”, “science and technology” and “innovation and entrepreneurship education”. The integration of artificial intelligence, metaverse and entrepreneurship education, i.e., the integration of “science and technology” and “entrepreneurship education”, was designed above. In this chapter, “industry” is integrated into it, and the degree of integration is evaluated by improving AHP-Fuzzy.

3.1

Integration of industry, science, innovation and education

The core of industry-teaching-creation-teaching integration is talent cultivation. It emphasizes that education must be closely aligned with the needs of industrial development, integrating new technologies, new techniques and new ideas in the actual production process of enterprises into the education and teaching process, so that students can not only master solid theoretical knowledge of the profession, but also get the exercise of practical skills and the enhancement of vocational literacy in the learning process. This education model aims to cultivate applied and compound high-quality talents with innovative spirit and practical ability, and realize the effective connection between education chain, talent chain and industrial chain and innovation chain. The integration of industry, education and creation is reflected in the sharing and interaction between educational resources and industrial resources. This includes the construction of training bases and R&D centers by schools and enterprises, the joint development of curriculum standards and teaching content, the implementation of dual tutor system and other diversified cooperation methods, so that educational resources and industrial resources can complement each other’s strengths, and the formation of a collaborative educating mechanism for the integration of industry, academia, research and application. The integration of industry, education, creation and education has a significant effect on promoting the upgrading of industrial structure and social and economic development. It can promote a closer integration of the education system and economic and social development, and provide strong intellectual support and talent guarantee for regional economic and industrial upgrading.

3.2

Evaluation of the degree of integration of industry, science, innovation and education

The evaluation of the integration degree of obstetrics, science, innovation and education based on the improved AHP-Fuzzy includes the following steps: 1)

Establishing the weight of each indicator based on the improved AHP method

The AHP method is to compare the importance of each indicator two by two based on the knowledge, experience and ability of the judging experts, construct the judgment matrix, and then determine the weight of each indicator. Among them, the consistency test for constructing judgment matrix is the difficulty and tediousness of this method. This paper proposes an improved AHP method to determine the weight of each evaluation index of the integration of industry, science, innovation and education. The method uses the three-scaled method to construct the judgment matrix, find its optimal transfer matrix, directly obtain the judgment matrix that meets the consistency requirements, and then obtain the weight value of each influential factor. The advantage of this method is that the judgment matrix can naturally meet the consistency requirements, thus avoiding the consistency test and complex adjustment process [27]. If the number of indicators at the criterion level or indicator levels of the evaluation object is n, the improved AHP method is introduced as follows:

The first step is to construct judgment matrix A (A is a n rd order square matrix). Let A represent the element as a_ij, and compare the importance of each layer of factors two by two by using the three-scaled method to obtain: if indicator i is more important than indicator j, a_ij = 1 if indicator i is equally important as indicator j, and if indicator i is not as important as indicator j, a_ij = 1.

In the second step, its optimal transfer matrix and consistency matrix are obtained based on A, so that A directly meets the consistency requirements. Let the optimal transfer matrix of A be the nth order square matrix B with representative element b_ij, then: 1 $b_{i j} = \frac{1}{n} \sum_{k = 1}^{n} (a_{i k} + a_{k j})$

In the third step, the consistency judgment matrix D is constructed by means of the exponential function, whose representative element is d_ij there: 2 $d_{i j} = \exp (b_{i j})$

In the fourth step, the weight values W_i(1 ≤ i ≤ n) for each indicator are as follows: 3 ${\bar{w}}_{i} = {(\prod_{j = 1}^{n} c_{i j})}^{\frac{1}{2}}, W_{i} = \sum_{j}^{n} {\tilde{w}}_{i}$

2)

Establish a jury set

The judging set is a collection of various evaluation results that the judging object may make. Table 1 shows the measurement methods of the evaluation indicators. In this paper, the evaluation of each indicator is divided into grades from good to poor, in the order of “good”, “good”, “fair”, “poor”, and the letters are I., II., III., and iv., and the corresponding scores are 8, 6, 4, and 2 respectively. The judging set is denoted by the symbol v, which is denoted as V = {v₁, v₂, v₃, v₄}, where V_j is the judging value corresponding to the j th grade in the judging set. For quantitative indicators, the quantitative criteria for obtaining different grades can be clearly given. For qualitative indicators, the grades are determined based on the evaluation criteria and the experience and ability of the members of the evaluation team.

3)

Determination of the membership matrix of evaluation indicators

For the first-level or second-level indicators, it is necessary to determine the membership matrix of each index through evaluation, for the membership degree of quantitative indicators, first determine the value range of each grade [f_jmin, f_jmax), if the value of an indicator is in this range, the membership degree of the index in the level is 1, and in the rest of the grades is 0. For the membership degree of qualitative indicators, a number of experts or relevant management personnel are selected to select the evaluation level, and according to the proportion of each evaluation level, a fuzzy evaluation matrix can be established R_m (m represents the m th evaluation index. The representative elements are r_ij, i = 1, 2, …, m_i, j = 1, 2,…, 4), m_i is the number of indicators, 4 is the number of ratings, and j = 1, 2, 3, 4 is “excellent”, “good”, “fair”, “poor”. r_ij represents the proportion of the total number of evaluators who chose level j for indicator i. After obtaining R_m, the singlelevel comprehensive evaluation vector is expressed as: 4 $E^{m} = W^{m} \circ (R_{m}, e_{m 1} e_{m 2} e_{m 3}) e_{m 4}$

The formula “∘” is the synthesis operator, and the calculation rules of the synthesis operator can be determined according to the actual situation.

4)

Multi-level comprehensive evaluation

Multi-level comprehensive evaluation is carried out layer by layer from the lower level to the higher level, taking subset C_ij as a factor of the higher level C_i (or taking subset C_ijk as a factor of the higher level C_ij), the higher level comprehensive evaluation vector matrix will be composed of the next layer of judgment vectors [28]: R = [Z₁, Z₂, …, Z_n]^T = (z_ij)_m×4. Therefore, the highest level fuzzy comprehensive evaluation vector is: 5 $E = w \circ R = (e_{1}, e_{2}, e_{3}, e_{4})$

5)

Processing of evaluation results

The comprehensive evaluation result E obtained in step (4) is a fuzzy vector indicating the affiliation degree of the integration of industry, science and education ultimately belonging to different rubric levels, and the evaluation level of the integration of industry, science and education is judged according to the principle of maximum affiliation degree.

Table 1.

Evaluation index measurement method

Evaluation grade	I	II	III	IV
Score	8	6	4	2
Describe	Good	Better	General	Bad

4

Analysis of the degree of integration of industry, science, technology and education

4.1

Evaluation index system construction of the integration degree of industry, science, innovation and education

The evaluation index system of the degree of integration of industry, science, innovation and education constructed in this paper with reference to related literature is shown in Table 2 [29]. It contains three first-level indicators and nine second-level indicators for the input of industrytechnology-creation-teaching integration, the process of industry-technology-creation-teaching integration and the effect of industry-technology-creation-teaching integration.

Table 2.

The system of evaluation index of Industry-Technology-Education Integration

	Primary indicator	Secondary indicator
Industry Technology-Education Integration	Fusion input (A)	Funding (A1)
	Fusion input (A)	Training base (A2)
	Fusion process (B)	Faculty (B1)
		Professional construction (B2)
		Course setting (B3)
		Teaching and training (B4)
	Fusion effect (C)	School benefit (C1)
		Enterprise benefit (C2)
		Graduate development (C3)

4.2

Establishment of indicator weights

Based on the index system of the degree of integration of industry, science, innovation and education and the improved AHP method, this study designs the questionnaire about the weights of the indicators, and invites 10 experts and scholars in the field of integration of industry, science, innovation and education, faculty members of universities and representatives of cooperative enterprises to compare and evaluate the importance of the indicators in each level. Finally, with the help of YaahpV.12.8 software, the judgment matrix was drawn, the questionnaire data was entered, and the weights of the indicators were calculated.

As an example, the judgment matrix of the first level indicator is scored by expert A. The judgment matrix of the first level indicator and the scoring results of expert A are shown in Table 3. When comparing the “inputs to the integration of industry, science, innovation and education” with the “process of integration of industry, science, innovation and education”, the expert gave a rating of 1/5, which indicates that in the process of integration of industry, science, innovation and education, Expert A believes that the process of integration of industry, science, innovation and education is significantly more important than the inputs to the integration of industry, science, innovation and education.

Table 3.

Expert A’s score

	A	B	C
A	1	1/5	1/2
B	5	1	1/3
C	2	3	1

In this study, with the help of Yaahp V.12.8 software, 10 experts group decision-making consulting data into the software, the consistency of the judgment matrix test, only through the consistency of the test can be carried out the next weighting operation, and finally the use of each expert judgment matrix value weighted arithmetic average of the expert data assembly method, resulting in the weight value of the evaluation of the degree of fusion of industry, science, education and technology in universities and colleges evaluation index system. Taking the weight calculation process of the first-level indicators as an example, combined with the software Yaahp V.12.8, the judgment matrix of the first-level indicators is subjected to consistency test and determination of the weight value, and the judgment matrix of the first-level indicators and the weights are shown in Figure 1. The weight vector of the first-level indicators is W_i = (0.3136, 0.3527, 0.3337), λmax=3.1001, CI=0.0007, RI=0.5796, CR=0.0019<0.1, i.e., the consistency test of the judgment matrix is passed. The weights of the input of industry-technology-creation-teaching integration, the process of industry-technology-creationteaching integration, and the effect of industry-technology-creation-teaching integration are 0.3136, 0.3527, and 0.3337, respectively.

Similarly, using the same method, the consistency test can be carried out on the judgment matrix of the second-level indicators, and it is found that the consistency of all the judgment matrices meets the condition, i.e., CR<0.1. As a result, the weight values of the second-level indicators can be derived, in addition, the hierarchical total ordering also needs to be passed by the consistency test, and the test conditions are the same as those for the hierarchical single-ordering, i.e., to determine whether the value of CR is less than 0.1, and if the CR<0.1, then the consistency If CR<0.1, the consistency test is passed, otherwise the test is not passed. In this study, with the help of Yaahp V.12.8 software, the consistency ratios of the 1st and 2nd criterion levels of the indicator system are calculated to be 0.0106 and 0.0325, respectively, and both of them are less than 0.1, which shows that the overall consistency of the judgment matrix of the indicator system is better, and it satisfies the CR<0.1, and the test is passed. As a result, through the calculation of the weights of the above hierarchical single sort and total sort, the results of the peer weights and comprehensive weights of the evaluation indexes are now organized, and the weights of the evaluation index system of the integration of industry, science, innovation and education are shown in Table 4. Among them, the weights of the second-level indicators of the input of industry-technology-creation-education integration, such as the funding input and the training base, are 0.1744 and 0.1392 respectively, which account for a higher proportion among all the second-level indicators, indicating that the input of the integration of industrytechnology-creation-education integration has a greater impact on the degree of integration of industry-technology-creation-education integration.

Table 4.

Index system weight

Primary indicator	W_i	Secondary indicator	W_i	Global weight	Sort
A	0.3136	A1	0.5561	0.1744	1
A	0.3136	A2	0.4439	0.1392	2
B	0.3527	B1	0.2364	0.0834	8
		B2	0.2569	0.0906	7
		B3	0.2106	0.0743	9
		B4	0.2961	0.1044	5
C	0.3337	C1	0.3325	0.111	4
		C2	0.3766	0.1257	3
		C3	0.2909	0.0971	6

4.3

Determination of the affiliation matrix of evaluation indicators

This paper selects S colleges and universities with better integration of industry, science, innovation and education as the research object of empirical analysis, and before empirical analysis, it is necessary to determine the evaluation grade and fuzzy evaluation matrix of S colleges and universities. To this end, 10 experts from the field of integration of industry, science, innovation and education in S colleges and universities will form a judging panel, experts refer to the index system, based on the current situation of the development of integration of industry, science, innovation and education in S colleges and universities for multiple rounds of discussion, in order to ensure that the reasonableness and operability of the evaluation level, according to the evaluation level provided in Table 1 for scoring, and the evaluation scores for each level 2 evaluation index are statistically shown in Table 5. Among them, the evaluation grades of each index belong to grade I, II and III, indicating that the integration of industry, science, innovation and education in S colleges and universities is effective.

Table 5.

Index evaluation score

Secondary indicator	I		II		III		IV
Secondary indicator	N	Score	N	Score	N	Score	N	Score
A1	3	24	5	30	2	8	0	0
A2	3	24	4	24	3	12	0	0
B1	3	24	6	24	1	4	0	0
B2	3	24	4	16	3	12	0	0
B3	3	24	4	16	3	12	0	0
B4	2	16	6	24	2	8	0	0
C1	2	16	6	24	2	8	0	0
C2	4	32	5	20	1	4	0	0
C3	4	32	5	20	1	4	0	0

The final fuzzy evaluation matrix is obtained as shown in Table 6. It is derived by combining Eq. 5: 6 $E = W \circ R = (0 .3008, 0 .4995, 0 .1999, 0)$

Table 6.

Evaluation index membership

Primary indicator	W_i	Secondary indicator	Global weight	I	II	III
A	0.3136	A1	0.1744	0.3	0.5	0.2
A	0.3136	A2	0.1392	0.3	0.4	0.3
B	0.3527	B1	0.0834	0.3	0.6	0.1
		B2	0.0906	0.3	0.4	0.3
		B3	0.0743	0.3	0.4	0.3
		B4	0.1044	0.2	0.6	0.2
C	0.3337	C1	0.111	0.2	0.6	0.2
		C2	0.1257	0.4	0.5	0.1
		C3	0.0971	0.4	0.5	0.1

4.4

Fuzzy integrated evaluation

According to the principle of maximum affiliation, 0.4995>0.3008>0.1999>0, which indicates that the integration of industry, science, technology and education in this university belongs to the grade of “better”, and this evaluation result is consistent with the results of manual evaluation, which indicates that the integration of industry, science, technology and education can be accurately evaluated based on the improved AHP-Fuzzy.

The level of integration of industry, science, innovation and education in S colleges and universities is still slightly insufficient, and the level of integration needs to be further improved. Accordingly, this paper puts forward targeted recommendations: 1)

On the basis of the continuous improvement of faculty and teachers’ specialization level, the coverage rate of the “Industry-Science-Industry-Technology-Innovation-Teaching-Integration” courses for students should be improved. In terms of theoretical teaching, the school offers elective courses such as management, financial management, marketing and so on for the whole school according to the students’ professional characteristics. In terms of practical teaching, the lower grades focus on basic enterprise cognitive practice, such as visits to enterprises and cognitive internships. Middle and upper grades students are encouraged to carry out academic competitions, entrepreneurship competitions, and skills training. Graduation class students can enter enterprise incubation and carry out entrepreneurial practice training, etc. It is necessary to cooperate with theory and practice to build a multilevel, three-dimensional, whole-process curriculum system.

2)

Optimize resource allocation and enhance the integration of industry, science, innovation and education. Private colleges and universities have a single source of funding, which is mainly reflected in the special funds set by the school itself, so the investment in resource allocation is limited. Schools set up special funds for supporting teachers and students to start their own businesses, and schools can expand funding sources in multiple ways, such as donations from enterprises, civil organizations, alumni associations and so on. Multi-channel funding sources can guarantee the sustainable development of “industry-technology-creation-education fusion” education. At the same time, we can help students to solve the difficulties they encounter in starting their own business by providing them with start-up grants and loans and simplifying the procedures. In addition, we can provide space support for small and medium-sized enterprises in the early stage of entrepreneurship and improve the utilization rate of on-campus incubation bases. Give full play to the advantages of school-enterprise cooperation, increase cooperation efforts, provide students with as many entrepreneurial resources as possible, let them feel the spirit of enterprise, understand the operation of the enterprise, expand business channels for students who have entrepreneurial ideas and dare to take action, solve production and marketing problems, and then allow students to gain practical experience.

5

Conclusion

The study explores the path of integrating artificial intelligence and metacosmos into innovation and entrepreneurship education, and designs an improved AHP-Fuzzy-based assessment model for the integration of industry, science, innovation and education. Take S colleges and universities with better integration of industry, science, innovation and education as an example. The model calculates E = W ∕ R = (0.3008,0.4995,0.1999,0), according to the principle of maximum affiliation, 0.4995 belongs to the grade of “better”. It shows that based on the improved AHP-Fuzzy model, the degree of integration of industry, science, innovation and education in S colleges and universities is better, which is consistent with the actual situation and verifies the applicability of the improved AHP-Fuzzy model. In addition, colleges and universities can further improve the integration of industry-science-creation-teaching from three aspects: faculty, faculty specialization level and resource allocation.

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Exploration and Research on the Integration of Artificial Intelligence and Metacosmos into Innovation and Entrepreneurship Education and the Integration of Industry, Science, Innovation and Education

Linjie Cai

Publicado en línea: 19 mar 2025

Recibido: 14 oct 2024

Aceptado: 11 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0516

Palabras claveArtificial Intelligence, Meta-Universe, AHP-Fuzzy, Integration of Industry, Science, Technology and Education

© 2025 Linjie Cai, published by Sciendo

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

Palabras clave
Artificial Intelligence, Meta-Universe, AHP-Fuzzy, Integration of Industry, Science, Technology and Education