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Educational futures of intelligent synergies between humans, digital twins, avatars, and robots - the iSTAR framework.

Authors :
Ronghuai Huang
Tlili, Ahmed
Lin Xu
Ying Chen
Lanqin Zheng
Metwally, Ahmed Hosny Saleh
Ting Da
TingWen Chang
Huanhuan Wang
Mason, Jon
Stracke, Christian M.
Sampson, Demetrios
Bonk, Curtis J.
Source :
Journal of Applied Learning & Teaching (JALT); Dec2023, Vol. 6, p28-43, 16p
Publication Year :
2023

Abstract

With the rapid advances of Artificial Intelligence (AI) and its technologies, human teachers and machines are now capable of collaborating to effectively achieve specified outcomes. In educational settings, such collaboration requires consideration of several dimensions to ensure safe, responsible, and ethical usage. While various research studies have discussed human-machine collaboration or cooperation in education, a framework is now needed that aligns with contemporary affordances. Providing such a framework can help to better understand how human teachers and machines can team up in education and what should be considered while doing so. To address this gap, this paper outlines the iSTAR (Intelligent human-machine Synergy in collaborative teaching: utilizing the digital Twins, Avatars/Agents and Robots) framework. iSTAR represents human-machine collaboration as an ecosystem that goes beyond the simple collaboration between human teachers and machines in education. Therefore, it presents core dimensions of DELTA (design, ethics, learning, teaching and assessments) that should be considered in designing safe, responsible, and ethical learning opportunities. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2591801X
Volume :
6
Database :
Complementary Index
Journal :
Journal of Applied Learning & Teaching (JALT)
Publication Type :
Academic Journal
Accession number :
176527351
Full Text :
https://doi.org/10.37074/jalt.2023.6.2.33