Artificial Intelligence Technologies for Teaching and Learning in Higher Education

Author:

Chang Qingqing1,Pan Xiajie2,Manikandan N.3,Ramesh S.4

Affiliation:

1. School of Information Management, Shanghai Lixin University of Accounting and Finance, No. 995 Shangchuan Road Pudong New District, Shanghai 201209, China

2. WuXi City College of Vocational Technology, WuXi 214153, P. R. China

3. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, OMR, Chennai 600119, Tamil Nadu, India

4. Vignan Foundation for Science Technology, Research Guntur 522213, Andhra Pradesh, India

Abstract

The term “Artificial Intelligence” (AI) refers to the simulation of human intelligence on a computer. Higher education can benefit from AI because it is a computationally efficient paradigm. Learning adapted to the changing demands of students is one of the key educational advantages of AI. Students can modify the pace of a course to better competency. Poor faculty and teaching quality and a general lack of motivation and interest among students are among the difficulties facing higher education. An artificial intelligence-assisted integrated teaching–learning framework (AL-ITLF) for higher education is proposed in this research. Multiple tutoring services are also involved in the curriculum, which is skill-based. The extreme learning machine (ELM) technique evaluates designs integrated into the suitable student monitoring model weighted score (WS) and exam results. An educational model that is more efficient, adaptable, and effective than current traditional education has been developed due to AI research in higher education. Higher education’s use of AI has resulted in a more efficient, adaptive, and effective educational model than traditional schooling. High accuracy, higher performance, lower processing costs, and a high prediction and low error rate are advantages of the suggested AI-ITLF approach. The WS and exam results were evaluated using an ELM algorithm as part of a proper student monitoring model.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Energy Engineering and Power Technology,Aerospace Engineering,Safety, Risk, Reliability and Quality,Nuclear Energy and Engineering,General Computer Science

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