Virtual Teachers And Teaching Robots: Transform Innovation Of The Future Of Learning In The Era Of Artificial Intelligence

This research aims to analyze the role of virtual teachers and teaching robots as transformative innovations in learning systems in the era of artificial intelligence (AI). With the development of AI technology, this research focuses on how these two technologies can increase learning effectiveness, personalize education, as well as the challenges and opportunities faced in their implementation. The research method used is a literature study with a qualitative approach, collecting and analyzing data from various sources such as journals, articles and reports related to virtual teachers, teaching robots and the application of AI in education. The research results show that virtual teachers and teaching robots are able to provide adaptive solutions in learning, such as 24/7 service, instant feedback, and a more interactive learning experience. However, its implementation still faces obstacles such as emotional limitations, ethical data use, and infrastructure readiness. This study concludes that this innovation has the potential to revolutionize education, as long as it is supported by appropriate policies, improving the quality of technology, and the readiness of human resources. This research provides recommendations for technology developers, educators and policy makers to optimize the use of AI in learning in the future.

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Publication Details

Journal
Journal of International Multidisciplinary Research
Published
2025-05-09
DOI
https://doi.org/10.62504/jimr1239
Primary Topic
Technology-Enhanced Education Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Virtual Teachers And Teaching Robots: Transform Innovation Of The Future Of Learning In The Era Of Artificial Intelligence

Akhmad Ramli, Nasril Nasril, Chairul Anwar, Darimis Darimis et al.
Journal of International Multidisciplinary Research
Technology-Enhanced Education Studies
article

Virtual Teachers And Teaching Robots: Transform Innovation Of The Future Of Learning In The Era Of Artificial Intelligence

Akhmad Ramli, Nasril Nasril, Chairul Anwar, Darimis Darimis, Viviana Lisma Lestari
article en

Abstract

This research aims to analyze the role of virtual teachers and teaching robots as transformative innovations in learning systems in the era of artificial intelligence (AI). With the development of AI technology, this research focuses on how these two technologies can increase learning effectiveness, personalize education, as well as the challenges and opportunities faced in their implementation. The research method used is a literature study with a qualitative approach, collecting and analyzing data from various sources such as journals, articles and reports related to virtual teachers, teaching robots and the application of AI in education. The research results show that virtual teachers and teaching robots are able to provide adaptive solutions in learning, such as 24/7 service, instant feedback, and a more interactive learning experience. However, its implementation still faces obstacles such as emotional limitations, ethical data use, and infrastructure readiness. This study concludes that this innovation has the potential to revolutionize education, as long as it is supported by appropriate policies, improving the quality of technology, and the readiness of human resources. This research provides recommendations for technology developers, educators and policy makers to optimize the use of AI in learning in the future.

Journal of International Multidisciplinary ResearchVol. 3(5)
Syarif Hidayatullah State Islamic University Jakarta (ID), Universitas Islam Negeri Sultan Syarif Kasim Riau (ID), Institut Agama Islam Negeri Bengkulu (ID)
Openalex Percentile: Top 90%
Technology-Enhanced Education Studies
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Virtual Teachers And Teaching Robots: Transform Innovation Of The Future Of Learning In The Era Of Artificial Intelligence — Akhmad Ramli, Nasril Nasril, et al. · Journal of International Multidisciplinary Research (2025) | TGRS Research Map | TGRS