Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology
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 establishmen...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns-2024-0378 |
| _version_ | 1797279131426095104 |
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| author | Wang Junqiao |
| author_facet | Wang Junqiao |
| author_sort | Wang Junqiao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T16:20:54Z |
| format | Article |
| id | doaj.art-88ef1d487a4946619b85ff9f93cd49ee |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T16:20:54Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-88ef1d487a4946619b85ff9f93cd49ee2024-03-04T07:30:40ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0378Exploring New Directions in Higher Education Aided by Artificial Intelligence TechnologyWang Junqiao01Culture and Arts Contents, Dongbang Culture University, 02838, Seoul, South Korea.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.https://doi.org/10.2478/amns-2024-0378hierarchical analysisparticle swarm algorithmintelligent learning modelcoding problem68t01 |
| spellingShingle | Wang Junqiao Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology Applied Mathematics and Nonlinear Sciences hierarchical analysis particle swarm algorithm intelligent learning model coding problem 68t01 |
| title | Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology |
| title_full | Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology |
| title_fullStr | Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology |
| title_full_unstemmed | Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology |
| title_short | Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology |
| title_sort | exploring new directions in higher education aided by artificial intelligence technology |
| topic | hierarchical analysis particle swarm algorithm intelligent learning model coding problem 68t01 |
| url | https://doi.org/10.2478/amns-2024-0378 |
| work_keys_str_mv | AT wangjunqiao exploringnewdirectionsinhighereducationaidedbyartificialintelligencetechnology |