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

Author(s):  
André Renz ◽  
Romy Hilbig

An amendment to this paper has been published and can be accessed via the original article.

Author(s):  
André Renz ◽  
Swathi Krishnaraja ◽  
Elisa Gronau

<span lang="EN-US">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.</span>


2018 ◽  
Vol 61 (2) ◽  
pp. 59-83 ◽  
Author(s):  
Massimo Garbuio ◽  
Nidthida Lin

The future of health care may change dramatically as entrepreneurs offer solutions that change how we prevent, diagnose, and cure health conditions, using artificial intelligence (AI). This article provides a timely and critical analysis of AI-driven health care startups and identifies emerging business model archetypes that entrepreneurs from around the world are using to bring AI solutions to the marketplace. It identifies areas of value creation for the application of AI in health care and proposes an approach to designing business models for AI health care startups.


2021 ◽  
Vol 3 (10) ◽  
Author(s):  
Bianca Weber-Lewerenz

AbstractDigitization is developing fast and has become a powerful tool for digital planning, construction and operations, for instance digital twins. Now is the right time for constructive approaches and to apply ethics-by-design in order to develop and implement a safe and efficient artificial intelligence (AI) application. So far, no study has addressed the key research question: Where can corporate digital responsibility (CDR) be allocated, and how shall an adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Therefore, the research on how best practices meet their corporate responsibility in the digital transformation process and the requirements of the EU for trustworthy AI and its human-friendly use is essential. Its transformation bears a high potential for companies, is critical for success and thus, requires responsible handling. This study generates data by conducting case studies and interviewing experts as part of the qualitative method to win profound insights into applied practice. It provides an assessment of demands stated in the Sustainable Development Goals by the United Nations (SDGs), White Papers on AI by international institutions, European Commission and German Government requesting the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of AI in construction engineering from an ethical perspective. This research critically evaluates opportunities and risks concerning CDR in construction industry. To the author’s knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to digitization and AI, to mitigate digital transformation both in large, medium- and small-sized companies. This study applies a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation and examine benefits as well as risks of AI. Furthermore, the goal is to define ethical principles which are key for success, resource-cost-time efficiency and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. This study concludes that innovative corporate organizations starting new business models are more likely to succeed than those dominated by a more conservative, traditional attitude.


Author(s):  
Pranav Ghadge ◽  
Riddhik Tilawat ◽  
Prasanna Sand ◽  
Parul Jadhav

Satellite system advances, remote sensing and drone technology are continuing. These progresses produce high-quality images that need efficient processing for smart agricultural applications. These possibilities to merge computer vision and artificial intelligence in agriculture are exploited with recent deep educational technology. This involves essential phenomena of data and huge quantities of data stored, analysed and used when making decisions. This paper demonstrates how computer vision in agriculture can be used.


Author(s):  
Samaher Ahmed Al-Qarni, Amani Mohammed Omran Samaher Ahmed Al-Qarni, Amani Mohammed Omran

The research seeks to know the effect of Artificial Intelligence (Microbit) in raising the motivation towards learning programming among the students of educational technology at King Abdulaziz University in Jeddah. The sample consisted of (14) students, and the research followed the quasi-experimental method for one experimental group. Also, a pre-measurement and post-measurement was done by using the motivation measure towards learning programming. The results of the research confirmed that there were statistically significant differences at the level of significance (0.001) between the results of students for their motivation towards learning programming before and after the use of the (Microbit) in favor of post-measurement. The research also recommended the importance of employing artificial intelligence techniques in curricula and academic projects for its effective role in making the education process active, improving the performance of male and female students and raising their motivation. As well as, preparing educational institutions and centers, and training teachers to work using artificial intelligence techniques, especially the Microbit device.


2021 ◽  
Author(s):  
Julian Heim

Data is the core of Internet-based business models. Ever since Facebook took over WhatsApp, European antitrust law has been faced with the question of how to deal with mergers, especially those involving the well-known Internet giants ("FANG"). Under what circumstances can market power be based as a prohibition criterion on the possession of and access to data? What competitive effects of data-based market power are to be feared in horizontal, vertical and conglomerate mergers? How can any commitments remedy this form of market power? The work takes into account technical developments such as artificial intelligence as well as data protection aspects.


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