scholarly journals An Ontology-based Framework and its Application to Effective Collaboration

2008 ◽  
Vol 11 (1) ◽  
Author(s):  
Seiji Isotani ◽  
Riichiro Mizoguchi
Keyword(s):  
Artificial Intelligence ◽  
Learning Theory ◽  
Learning Activities ◽  
Learning Theories ◽  
The Past ◽  
Partial Representation ◽  
Present Part ◽  
Ontological Framework ◽  
Effective Design ◽  
Common Understanding

In the past few years Artificial Intelligence has been gradually introduced to enhance Education through technologies. However, usual approaches provide systems with a kind of expertise using a set of heuristics and domain theories built in the procedures. As a result they cannot justify their recommendations systematically and scientifically. To overcome such problems it is necessary to establish a common understanding of what a learning theory is and how to represent it adequately. In this work we present part of our ontological framework that allows for the partial representation of learning theories considering explicitness, formalism, concepts and vocabulary. Then, we propose sophisticated techniques to reasoning on these theories considering their semantics showing the use of this framework to build a system called CHOCOLATO to facilitate the effective design and analysis of collaborative learning activities.

scholarly journals Discover AI Knowledge to Preserve Cultural Heritage

2021 ◽  
Author(s):  
Leonardo Ranaldi ◽  
Fabio Massimo Zanzotto
Keyword(s):  
Artificial Intelligence ◽  
Cultural Heritage ◽  
Reference Points ◽  
Learning Theories ◽  
Human Knowledge ◽  
Rule Based ◽  
Symbolic Representations ◽  
The Past ◽  
New Vision ◽  
The Right

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

scholarly journals Connectivism: Learning theory of the future or vestige of the past?

2008 ◽  
Vol 9 (3) ◽  
Author(s):  
Rita Kop ◽  
Adrian Hill
Keyword(s):  
Learning Theory ◽  
Learning Theories ◽  
New Developments ◽  
The Past ◽  
The Future ◽  
The Status ◽  
E Learning ◽  
Approach To Teaching ◽  
Class Room ◽  
Traditional Class

Siemens and Downes initially received increasing attention in the blogosphere in 2005 when they discussed their ideas concerning distributed knowledge. An extended discourse has ensued in and around the status of ‘connectivism’ as a learning theory for the digital age. This has led to a number of questions in relation to existing learning theories. Do they still meet the needs of today’s learners, and anticipate the needs of learners of the future? Would a new theory that encompasses new developments in digital technology be more appropriate, and would it be suitable for other aspects of learning, including in the traditional class room, in distance education and e-learning? This paper will highlight current theories of learning and critically analyse connectivism within the context of its predecessors, to establish if it has anything new to offer as a learning theory or as an approach to teaching for the 21st Century.

Mobile Affordances and Learning Theories in Supporting and Enhancing Learning

2017 ◽  
Vol 9 (2) ◽  
pp. 61-73 ◽  
Author(s):  
Kathryn MacCallum ◽  
Stephanie Day ◽  
David Skelton ◽  
Michael Verhaart
Keyword(s):  
Experiential Learning ◽  
Mobile Learning ◽  
Learning Theory ◽  
Mobile Technology ◽  
Learning Activities ◽  
Learning Theories ◽  
Learning Approaches ◽  
Learning Practices ◽  
Support Students ◽  
Specific Learning

Mobile technology promises to enhance and better support students' learning. The exploration and adoption of appropriate pedagogies that enhance learning is crucial for the wider adoption of mobile learning. An increasing number of studies have started to address how existing learning theory can be used to underpin and better frame mobile learning activities. In particular, there are a number of learning theories that have been identified which particularly lend themselves to the specific affordances of mobile learning. This paper examines how mobile technology was incorporated within three different computing courses. These case studies explore how specific learning approaches (collaborative learning, connectivism and experiential learning) were adopted to frame the use of the technology within each course and how the affordances of mobile technology were harnessed to enhance and better support existing learning practices.

Socially Shared Regulation of Learning: A Review

2015 ◽  
Vol 20 (3) ◽  
pp. 190-203 ◽  
Author(s):  
Ernesto Panadero ◽  
Sanna Järvelä
Keyword(s):  
Empirical Evidence ◽  
Learning Theory ◽  
Qualitative Data ◽  
Social Regulation ◽  
Observation Data ◽  
Optimal Form ◽  
Self Regulated Learning ◽  
The Past ◽  
Recorded Observation ◽  
Socially Shared Regulation

Abstract. Socially shared regulation of learning (SSRL) has been recognized as a new and growing field in the framework of self-regulated learning theory in the past decade. In the present review, we examine the empirical evidence to support such a phenomenon. A total of 17 articles addressing SSRL were identified, 13 of which presented empirical evidence. Through a narrative review it could be concluded that there is enough data to maintain the existence of SSRL in comparison to other social regulation (e.g., co-regulation). It was found that most of the SSRL research has focused on characterizing phenomena through the use of mixed methods through qualitative data, mostly video-recorded observation data. Also, SSRL seems to contribute to students’ performance. Finally, the article discusses the need for the field to move forward, exploring the best conditions to promote SSRL, clarifying whether SSRL is always the optimal form of collaboration, and identifying more aspects of groups’ characteristics.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

2021 ◽  
pp. 155626462110027
Author(s):  
Gabrielle Samuel ◽  
Jenn Chubb ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Health Research ◽  
Decision Makers ◽  
Societal Impact ◽  
The Past ◽  
Research And Innovation ◽  
The United Kingdom ◽  
Responsible Research ◽  
Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

The Future of the Planetary Trust in a Kaleidoscopic World

2021 ◽  
pp. 1-8
Author(s):  
Edith Brown Weiss
Keyword(s):  
Artificial Intelligence ◽  
Natural Resource ◽  
Human Activities ◽  
New Technologies ◽  
Future Generations ◽  
Nuclear War ◽  
Human Environment ◽  
The Past ◽  
Resource Degradation ◽  
Shared Goal

Today, it is evident that we are part of a planetary trust. Conserving our planet represents a public good, global as well as local. The threats to future generations resulting from human activities make applying the normative framework of a planetary trust even more urgent than in the past decades. Initially, the planetary trust focused primarily on threats to the natural system of our human environment such as pollution and natural resource degradation, and on threats to cultural heritage. Now, we face a higher threat of nuclear war, cyber wars, and threats from gene drivers that can cause inheritable changes to genes, potential threats from other new technologies such as artificial intelligence, and possible pandemics. In this context, it is proposed that in the kaleidoscopic world, we must engage all the actors to cooperate with the shared goal of caring for and maintaining planet Earth in trust for present and future generations.

scholarly journals Application of Artificial Intelligence in Food Industry—a Guideline

2021 ◽  
Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Food Industry ◽  
Near Infrared ◽  
Vision System ◽  
Electronic Tongue ◽  
World Population ◽  
Computer Vision System ◽  
The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

scholarly journals Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

AI and Ethics ◽  
2021 ◽  
Author(s):  
Muhammad Ali Chaudhry ◽  
Emre Kazim
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Policy Makers ◽  
Tutoring Systems ◽  
Research And Practice ◽  
The Past ◽  
Artificial Intelligence In Education ◽  
Potential Impact ◽  
High Level ◽  
Contextualized Learning

AbstractIn the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

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