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 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.