<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper’s objective optimization problem belongs to the encoding problem of real numbers, and the classical particle swarm optimization algorithm is chosen. The establishment of intelligent learning model was finally completed through the assumption of model components, initial model establishment, model cycle evaluation and validation. First, after the implementation of the E-GPPE-C education model, the test was conducted on the educated people, and the AI literacy questionnaire was also distributed to the students before and after the implementation of the model education. Second, a statistical analysis explores which technologies will most likely be applied to higher education. Finally, the variability of higher education subject relations under the influence of AI and related ethical issues were explored. The results show that the highest score on the students’ final test was 96. 20 students scored more than 80 points, indicating that half of the students had a solid grasp of the knowledge and applications related to the E-GPPE-C model, and that there was a vast difference between the degree of AI knowledge and skill mastery before and after the implementation of education. Natural language understanding, computer vision, and biometrics are the three technologies most suitable for application in higher education.</p>
</abstract>

This paper’s objective optimization problem belongs to the encoding problem of real numbers, and the classical particle swarm optimization algorithm is chosen. The establishment of intelligent learning model was finally completed through the assumption of model components, initial model establishment, model cycle evaluation and validation. First, after the implementation of the E-GPPE-C education model, the test was conducted on the educated people, and the AI literacy questionnaire was also distributed to the students before and after the implementation of the model education. Second, a statistical analysis explores which technologies will most likely be applied to higher education. Finally, the variability of higher education subject relations under the influence of AI and related ethical issues were explored. The results show that the highest score on the students’ final test was 96. 20 students scored more than 80 points, indicating that half of the students had a solid grasp of the knowledge and applications related to the E-GPPE-C model, and that there was a vast difference between the degree of AI knowledge and skill mastery before and after the implementation of education. Natural language understanding, computer vision, and biometrics are the three technologies most suitable for application in higher education.

Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology

Applied Mathematics and Nonlinear Sciences

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

This paper’s objective optimization problem belongs to the encoding problem of real numbers, and the classical particle swarm optimization algorithm is chosen....

Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 04. Jan. 2024

Akzeptiert: 14. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0378

Schlüsselwörter
Hierarchical analysis, Particle swarm algorithm, Intelligent learning model, Coding problem

© 2024 Junqiao Wang, published by Sciendo

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

Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 04. Jan. 2024

Akzeptiert: 14. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0378

SchlüsselwörterHierarchical analysis, Particle swarm algorithm, Intelligent learning model, Coding problem

© 2024 Junqiao Wang, published by Sciendo

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

Schlüsselwörter
Hierarchical analysis, Particle swarm algorithm, Intelligent learning model, Coding problem