scholarly journals Demystification of Artificial Intelligence in Education – How much AI is really in the Educational Technology?

2020 ◽  
Vol 2 (1) ◽  
pp. 14 ◽  
Author(s):  
André Renz ◽  
Swathi Krishnaraja ◽  
Elisa Gronau
Keyword(s):  
Artificial Intelligence ◽  
Educational Technology ◽  
Learning Analytics ◽  
Public Discourse ◽  
Business Models ◽  
Data Driven ◽  
Current Status ◽  
Causal Connection ◽  
Artificial Intelligence In Education ◽  
The Relationship

<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>

The Relationship between the Internet and Replication with PIONY

2011 ◽  
Vol 460-461 ◽  
pp. 552-557
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Artificial Intelligence ◽  
Model Checking ◽  
Web Services ◽  
Mesh Networks ◽  
Current Status ◽  
The Internet ◽  
Event Driven ◽  
The Relationship

Authenticated algorithms and 802.11 mesh networks have garnered limited interest from both systems engineers and system administrators in the last several years. Given the current status of event-driven modalities, theorists particularly desire the refinement of Web services, which embodies the confirmed principles of artificial intelligence. Here we concentrate our efforts on arguing that model checking can be made event-driven, permutable, and wearable.

scholarly journals Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110255
Author(s):  
Wim Naudé ◽  
Ricardo Vinuesa
Keyword(s):  
Artificial Intelligence ◽  
Data Gathering ◽  
Analytical Techniques ◽  
Data Driven ◽  
Global Development ◽  
Limit Value ◽  
Digital Divides ◽  
Data Gaps ◽  
Development Outcomes ◽  
The Relationship

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficulty reusing data will limit value from data; (v) data deprivation, digital gaps and digital divides are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; (vi) having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; (vii) decentralised data gathering and use can help to build trust – particularly important for coordination of behaviour.

scholarly journals Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

Information ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Petros Lameras ◽  
Sylvester Arnab
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Teaching And Learning ◽  
Data Driven ◽  
Synthesis Process ◽  
Analysis And Synthesis ◽  
Analysis Strategy ◽  
Artificial Intelligence In Education ◽  
The Impact ◽  
Meaningful Data

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models.

scholarly journals Evaluating the impact of AI on insurance: The four emerging AI- and data-driven business models

2019 ◽  
Vol 1 ◽  
pp. 15 ◽  
Author(s):  
Alex Zarifis ◽  
Christopher P. Holland ◽  
Alistair Milne
Keyword(s):  
Artificial Intelligence ◽  
Value Chain ◽  
Business Models ◽  
Data Driven ◽  
Insurance Companies ◽  
Customer Base ◽  
Insurance Sector ◽  
The Third ◽  
The Impact ◽  
The Way

The increasing capabilities of artificial intelligence (AI) are changing the way organizations operate and interact with users both internally and externally. The insurance sector is currently using AI in several ways but its potential to disrupt insurance is not clear. This research evaluated the implementation of AI-led automation in 20 insurance companies. The findings indicate four business models (BM) emerging: In the first model the insurer takes a smaller part of the value chain allowing others with superior AI and data to take a larger part. In the second model the insurer keeps the same model and value chain but uses AI to improve effectiveness. In the third model the insurer adapts their model to fully utilize AI and seek new sources of data and customers. Lastly in the fourth model a technology focused company uses their existing AI prowess, superior data and extensive customer base, and adds insurance provision.

scholarly journals Review of Artificial Intelligence Techniques inGreen/Smart Buildings.

2021 ◽  
Author(s):  
Diego Rodriguez-Gracia ◽  
Maria de las Mercedes Capobianco-Uriarte ◽  
Eduardo Teran-Yepez ◽  
Jose Piedra-Fernandez ◽  
Luis Iribarne ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Rapid Evolution ◽  
Environmental Benefits ◽  
Current Status ◽  
Future Research ◽  
Smart Buildings ◽  
The Past ◽  
Intelligent Buildings ◽  
The Many ◽  
The Relationship

Abstract The many benefits offered by green or smart buildings have led to an increase in their construction. In turn, this growth has been accompanied by a rapid evolution of research on this topic. Thus, given the specialist interest, research on the use of artificial intelligence in this type of construction has been gaining space. This topic, although still novel, due to its current and future importance requires a literature review to identify the main actors, evaluate the past and establish future lines of research. The results based on 174 manuscripts detected in Web of Science and Scopus databases allow us to establish the main authors, institutions, countries and journals as well as the seminal papers in this field. Furthermore, through a keywords co-occurrence analysis this study identifies some of the topics that have received most interest in the past as well as some promising future research trends. This bibliometric study analyzes the relationship between the main clusters DML&B (Deep - Machine Learning and Building Constructions) by means of a detailed description of the fundamental concepts identified in the content analysis. It is complemented by a temporal keyword analysis focusing on the economic, social and environmental benefits obtained through green or intelligent buildings. Consequently, this research contributes to the literature by providing an overview of the past and current status of this field, as well as by opening future research lines.

scholarly journals Who Else do You Need for a Data-Driven Business Model? Exploring Roles and Exchanged Values

2021 ◽  
Author(s):  
Florian Leski ◽  
◽  
Michael Fruhwirth ◽  
Viktoria Pammer-Schindler ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Business Model ◽  
Business Models ◽  
Data Driven ◽  
Automotive Company ◽  
New Business ◽  
New Business Models ◽  
Structured Literature Review ◽  
Analyze Data

The increasing volume of available data and the advances in analytics and artificial intelligence hold the potential for new business models also in offline-established organizations. To successfully implement a data-driven business model, it is crucial to understand the environment and the roles that need to be fulfilled by actors in the business model. This partner perspective is overlooked by current research on datadriven business models. In this paper, we present a structured literature review in which we identified 33 relevant publications. Based on this literature, we developed a framework consisting of eight roles and two attributes that can be assigned to actors as well as three classes of exchanged values between actors. Finally, we evaluated our framework through three cases from one automotive company collected via interviews in which we applied the framework to analyze data-driven business models for which our interviewees are responsible.

scholarly journals How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers

2019 ◽  
Vol 1 (1) ◽  
pp. 2567-2576 ◽  
Author(s):  
Sebastian Bickel ◽  
Tobias C. Spruegel ◽  
Benjamin Schleich ◽  
Sandro Wartzack
Keyword(s):  
Artificial Intelligence ◽  
Product Development ◽  
Development Process ◽  
Data Driven ◽  
Product Development Process ◽  
Current Status ◽  
Plausibility Check ◽  
Development Processes ◽  
Digital Engineering ◽  
Product Development Processes

AbstractCurrent trends in product development are digital engineering, the increasing use of assistance tools based on artificial intelligence and in general shorter product lifecycles. These trends and new tools strongly rely on available data and will irreversibly change established product development processes. One example for such a new data driven tool is the plausibility check of linear finite element simulations with Convolutional Neural Networks (CNN). This tool is capable of determining whether new simulation results are plausible or non-plausible according to numeric input data. The digitalization and the increased use of data driven tools employing algorithms known from Artificial Intelligence also shifts the roles of many involved engineers. This paper describes and highlights this transition from current product development processes to a data driven / simulation driven product development process. Particularly, the shifts and changes of different roles and domains are illustrated and an example for changing roles in the design and simulation department is described. Furthermore, required adjustments in the design process are derived and compared to the current status.

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