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The Turing Teacher: Identifying core attributes for AI learning in K-12
INTRODUCTION: Artificial intelligence in the educational domain has many uses; however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794853/ https://www.ncbi.nlm.nih.gov/pubmed/36590861 http://dx.doi.org/10.3389/frai.2022.1031450 |
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author | Pelaez, Alexander Jacobson, Amal Trias, Kara Winston, Elaine |
author_facet | Pelaez, Alexander Jacobson, Amal Trias, Kara Winston, Elaine |
author_sort | Pelaez, Alexander |
collection | PubMed |
description | INTRODUCTION: Artificial intelligence in the educational domain has many uses; however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs far exceed mere fact recollection. Utilizing prior research using AI in education and online education in the K-12 space, we explore some of the hurdles that AI applications face in K-12 teaching and provide core attributes for a “Turing Teacher,” i.e., an AI powered technology for learning, specifically targeting the K-12 space. METHODS: Using a survey, which included qualitative responses during the implementation of online learning during the Covid Pandemic, we analyze the results using univariate and multivariate tests and analyzed the qualitative responses to create core attributes needed for AI powered teaching technology. RESULTS: The results present the challenges faced by any technology in an education setting and show that AI technology must help overcome negative feelings about technology in education. Further, the core attributes identified in the research must be addressed from the three stakeholder perspectives of teachers, parents and students. DISCUSSION: We present our findings and lay the groundwork for future research in the area of AI powered education. The Turing Teacher must be able to adapt and collaborate with real teachers and address the varying needs of students. In addition, we explore the use of AI technology as a means to close the digital divide in traditionally disadvantaged communities. |
format | Online Article Text |
id | pubmed-9794853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97948532022-12-29 The Turing Teacher: Identifying core attributes for AI learning in K-12 Pelaez, Alexander Jacobson, Amal Trias, Kara Winston, Elaine Front Artif Intell Artificial Intelligence INTRODUCTION: Artificial intelligence in the educational domain has many uses; however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs far exceed mere fact recollection. Utilizing prior research using AI in education and online education in the K-12 space, we explore some of the hurdles that AI applications face in K-12 teaching and provide core attributes for a “Turing Teacher,” i.e., an AI powered technology for learning, specifically targeting the K-12 space. METHODS: Using a survey, which included qualitative responses during the implementation of online learning during the Covid Pandemic, we analyze the results using univariate and multivariate tests and analyzed the qualitative responses to create core attributes needed for AI powered teaching technology. RESULTS: The results present the challenges faced by any technology in an education setting and show that AI technology must help overcome negative feelings about technology in education. Further, the core attributes identified in the research must be addressed from the three stakeholder perspectives of teachers, parents and students. DISCUSSION: We present our findings and lay the groundwork for future research in the area of AI powered education. The Turing Teacher must be able to adapt and collaborate with real teachers and address the varying needs of students. In addition, we explore the use of AI technology as a means to close the digital divide in traditionally disadvantaged communities. Frontiers Media S.A. 2022-12-14 /pmc/articles/PMC9794853/ /pubmed/36590861 http://dx.doi.org/10.3389/frai.2022.1031450 Text en Copyright © 2022 Pelaez, Jacobson, Trias and Winston. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Pelaez, Alexander Jacobson, Amal Trias, Kara Winston, Elaine The Turing Teacher: Identifying core attributes for AI learning in K-12 |
title | The Turing Teacher: Identifying core attributes for AI learning in K-12 |
title_full | The Turing Teacher: Identifying core attributes for AI learning in K-12 |
title_fullStr | The Turing Teacher: Identifying core attributes for AI learning in K-12 |
title_full_unstemmed | The Turing Teacher: Identifying core attributes for AI learning in K-12 |
title_short | The Turing Teacher: Identifying core attributes for AI learning in K-12 |
title_sort | turing teacher: identifying core attributes for ai learning in k-12 |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794853/ https://www.ncbi.nlm.nih.gov/pubmed/36590861 http://dx.doi.org/10.3389/frai.2022.1031450 |
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