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Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education
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 commu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110123/ https://www.ncbi.nlm.nih.gov/pubmed/35586099 http://dx.doi.org/10.1155/2022/1410448 |
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author | Hemachandran, K. Verma, Priti Pareek, Purvi Arora, Nidhi Rajesh Kumar, Korupalli V. Ahanger, Tariq Ahamed Pise, Anil Audumbar Ratna, Rajnish |
author_facet | Hemachandran, K. Verma, Priti Pareek, Purvi Arora, Nidhi Rajesh Kumar, Korupalli V. Ahanger, Tariq Ahamed Pise, Anil Audumbar Ratna, Rajnish |
author_sort | Hemachandran, K. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9110123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91101232022-05-17 Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education Hemachandran, K. Verma, Priti Pareek, Purvi Arora, Nidhi Rajesh Kumar, Korupalli V. Ahanger, Tariq Ahamed Pise, Anil Audumbar Ratna, Rajnish Comput Intell Neurosci Research Article 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. Hindawi 2022-05-09 /pmc/articles/PMC9110123/ /pubmed/35586099 http://dx.doi.org/10.1155/2022/1410448 Text en Copyright © 2022 K. Hemachandran et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hemachandran, K. Verma, Priti Pareek, Purvi Arora, Nidhi Rajesh Kumar, Korupalli V. Ahanger, Tariq Ahamed Pise, Anil Audumbar Ratna, Rajnish Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education |
title | Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education |
title_full | Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education |
title_fullStr | Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education |
title_full_unstemmed | Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education |
title_short | Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education |
title_sort | artificial intelligence: a universal virtual tool to augment tutoring in higher education |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110123/ https://www.ncbi.nlm.nih.gov/pubmed/35586099 http://dx.doi.org/10.1155/2022/1410448 |
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