Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education.

Autor: Hemachandran K; Woxsen University, Hyderabad, Telangana, India., Verma P; School of Business Studies, Sharda University, Greater Noida, India., Pareek P; TAPMI School of Business Studies, Manipal University, Jaipur, India., Arora N; Dr Ambedkar Institute of Management Studies, Bangalore, India., Rajesh Kumar KV; Woxsen University, Hyderabad, Telangana, India., Ahanger TA; College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia., Pise AA; School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa.; Department of Sustainable Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha Nagar, Thandalam, Chennai 602105, Tamil Nadu, India., Ratna R; Gedu College of Business Studies, Royal University of Bhutan, Thimphu, Bhutan.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 May 09; Vol. 2022, pp. 1410448. Date of Electronic Publication: 2022 May 09 (Print Publication: 2022).
DOI: 10.1155/2022/1410448
Abstrakt: Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 K. Hemachandran et al.)
Databáze: MEDLINE
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