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

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dc.contributor.authorRenz, André
dc.contributor.authorHilbig, Romy
dc.date.accessioned2023-08-30T14:18:49Z
dc.date.available2023-08-30T14:18:49Z
dc.date.issued2020
dc.description.abstractThe 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.
dc.description.sponsorshipOur work has been funded by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 16DII111 (Deutsches Internet-Institut). This research project was funded by the German Federal Ministry of Education and Research (Funding Number: 16DII115).
dc.identifier.citationRenz, A., & Hilbig, R. (2020). 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, 17(1), 14. https://doi.org/10.1186/s41239-020-00193-3
dc.identifier.doihttps://doi.org/10.1186/s41239-020-00193-3
dc.identifier.eissn2365-9440
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/199
dc.language.isoeng
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComputers and Education
dc.subjectEducational Technology
dc.subjectHigher Education
dc.subjectComputer Appl. in Social and Behavioral Sciences
dc.subjectStatistics for Social Sciences, Humanities, Law
dc.subjectInformation Systems Applications (incl.Internet)
dc.subject.ddc370 Bildung und Erziehung
dc.subject.ddc310 Statistiken
dc.titlePrerequisites for artificial intelligence in further education. Identification of drivers, barriers, and business models of educational technology companies
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.articlenumber14
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleInternational Journal of Educational Technology in Higher Education
dcterms.bibliographicCitation.volume17
local.researchgroupDatenbasierte Geschäftsmodellinnovationen
local.researchtopicMarkt – Wettbewerb – Ungleichheit
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