Aufsätze

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Wissenschaftliche Aufsätze

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Auflistung Aufsätze nach Forschungsgruppen "Datenbasierte Geschäftsmodellinnovationen"

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    Demystification of Artificial Intelligence in Education – How much AI is really in the Educational Technology?
    (2020) Renz, André; Krishnaraja, Swathi; Gronau, Elisa
    The data-driven development of education through Learning Analytics in combination with Artificial Intelligence is an emerging field in the education sector. In the field of Artificial Intelligence in Education, numerous studies and research have been carried out over the past 60 years, and since then drastic changes have taken place. In the first part of this paper we present a brief overview of the current status of Learning Analytics and Artificial Intelligence in education. In order to develop a better understanding of the relationship between Learning Analytics and Artificial Intelligence in education, we outline the relationship between the two phenomena. The results show that the previous studies only vaguely distinguish between them: the terms are often used synonymously. In the second part of the paper we focus on the question why the European market currently has hardly any real applications for Artificial Intelligence in education. The research is based on a meta-investigation of data-driven business models, in particular the so-called Educational Technology providers. The core of the analysis is the question of how data-driven these companies really are, how much Learning Analytics and Artificial Intelligence is applied and whether there is a causal connection between the growth of the Educational Technology market and the application relevance of Artificial Intelligence in Education. In the scientific and public discourse, we can observe a distortion between the theoretical-conjunctive understanding of the application of Artificial Intelligence in Education and the current practical relevance.
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    Lessons Learned from Establishing the Energy-Informatics Business Model: Case of a German Energy Company
    (2019) Grosse, Matti; Send, Hendrik; Schildhauer, Thomas
    Energy and utilities companies find themselves in a paradoxical situation in which their traditional business models are losing profitability, and they must advocate energy efficiency and climate-protection goals, and thus encourage their customers to save energy. As a result, they must partially cannibalize their business models and experiment with new models and techniques. Energy Informatics (EI) offers promising business opportunities that alleviate the concerns of energy companies about traditional revenue streams. However, recent discussions on this issue lack proof of concept and success determinants. This business case study fills this gap by describing the journey of German energy company Energiequelle, which established a sustainable business model based on EI. On the basis of our interview data, we analyzed Energiequelle’s EI strategy and stakeholder management and present six lessons learned. We believe that our practice-oriented research provides profound insight, especially to high-level executives and policymakers.
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    Prerequisites for artificial intelligence in further education. Identification of drivers, barriers, and business models of educational technology companies
    (2020) Renz, André; Hilbig, Romy
    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.
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    Reinvigorating the Discourse on Human-Centered Artificial Intelligence in Educational Technologies
    (2021) Renz, André; Vladova, Gergana
    The increasing relevance of artificial intelligence (AI) applications in various domains has led to high expectations of benefits, ranging from precision, efficiency, and optimization to the completion of routine or time-consuming tasks. Particularly in the field of education, AI applications promise immense innovation potential. A central focus in this field is on analyzing and evaluating learner characteristics to derive learning profiles and create individualized learning environments. The development and implementation of such AI-driven approaches are related to learners' data, and thus involves several privacies, ethics, and morality challenges. In this paper, we introduce the concept of human-centered AI, and consider how an AI system can be developed in line with human values without posing risks to humanity. Because the education market is in the early stages of incorporating AI into educational tools, we believe that this is the right time to raise awareness about the use of principles that foster human-centered values and help in building responsible, ethical, and value-oriented AI.