Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies

Abstract The ongoing datafication of our social reality has resulted in the emergence of new data-based business models. This development is also reflected in the education market. An increasing number of educational technology (EdTech) companies are entering the traditional education market with da...

Full description

Bibliographic Details
Main Authors: André Renz, Romy Hilbig
Format: Article
Language:English
Published: SpringerOpen 2020-04-01
Series:International Journal of Educational Technology in Higher Education
Subjects:
Online Access:http://link.springer.com/article/10.1186/s41239-020-00193-3
id doaj-3971089e68eb41bbb042cbd5115b5cea
record_format Article
spelling doaj-3971089e68eb41bbb042cbd5115b5cea2020-11-25T02:20:03ZengSpringerOpenInternational Journal of Educational Technology in Higher Education2365-94402020-04-0117112110.1186/s41239-020-00193-3Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companiesAndré Renz0Romy Hilbig1University of the Arts Berlin / Weizenbaum Institute for the Networked SocietyUniversity of the Arts Berlin / Weizenbaum Institute for the Networked SocietyAbstract The ongoing datafication of our social reality has resulted in the emergence of new data-based business models. This development is also reflected in the education market. An increasing number of educational technology (EdTech) companies are entering the traditional education market with data-based teaching and learning solutions, and they are permanently transforming the market. However, despite the current market dynamics, there are hardly any business models that implement the possibilities of Learning Analytics (LA) and Artificial Intelligence (AI) to create adaptive teaching and learning paths. This paper focuses on EdTech companies and the drivers and barriers that currently affect data-based teaching and learning paths. The results show that LA especially are integrated into the current business models of EdTech companies on three levels, which are as follows: basic Learning Analytics, Learning Analytics and algorithmic or human-based recommendations, and Learning Analytics and adaptive teaching and learning (AI based). The discourse analysis reveals a diametrical relationship between the traditional educational ideal and the futuristic idea of education and knowledge transfer. While the desire for flexibility and individualization drives the debate on AI-based learning systems, a lack of data sovereignty, uncertainty and a lack of understanding of data are holding back the development and implementation of appropriate solutions at the same time.http://link.springer.com/article/10.1186/s41239-020-00193-3Learning analyticsAlgorithms-based learningArtificial intelligenceFurther educationHigher educationBusiness models
collection DOAJ
language English
format Article
sources DOAJ
author André Renz
Romy Hilbig
spellingShingle André Renz
Romy Hilbig
Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
International Journal of Educational Technology in Higher Education
Learning analytics
Algorithms-based learning
Artificial intelligence
Further education
Higher education
Business models
author_facet André Renz
Romy Hilbig
author_sort André Renz
title Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
title_short Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
title_full Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
title_fullStr Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
title_full_unstemmed Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
title_sort prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies
publisher SpringerOpen
series International Journal of Educational Technology in Higher Education
issn 2365-9440
publishDate 2020-04-01
description Abstract The ongoing datafication of our social reality has resulted in the emergence of new data-based business models. This development is also reflected in the education market. An increasing number of educational technology (EdTech) companies are entering the traditional education market with data-based teaching and learning solutions, and they are permanently transforming the market. However, despite the current market dynamics, there are hardly any business models that implement the possibilities of Learning Analytics (LA) and Artificial Intelligence (AI) to create adaptive teaching and learning paths. This paper focuses on EdTech companies and the drivers and barriers that currently affect data-based teaching and learning paths. The results show that LA especially are integrated into the current business models of EdTech companies on three levels, which are as follows: basic Learning Analytics, Learning Analytics and algorithmic or human-based recommendations, and Learning Analytics and adaptive teaching and learning (AI based). The discourse analysis reveals a diametrical relationship between the traditional educational ideal and the futuristic idea of education and knowledge transfer. While the desire for flexibility and individualization drives the debate on AI-based learning systems, a lack of data sovereignty, uncertainty and a lack of understanding of data are holding back the development and implementation of appropriate solutions at the same time.
topic Learning analytics
Algorithms-based learning
Artificial intelligence
Further education
Higher education
Business models
url http://link.springer.com/article/10.1186/s41239-020-00193-3
work_keys_str_mv AT andrerenz prerequisitesforartificialintelligenceinfurthereducationidentificationofdriversbarriersandbusinessmodelsofeducationaltechnologycompanies
AT romyhilbig prerequisitesforartificialintelligenceinfurthereducationidentificationofdriversbarriersandbusinessmodelsofeducationaltechnologycompanies
_version_ 1724873872699817984