scholarly journals A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xuesong Zhai ◽  
Xiaoyan Chu ◽  
Ching Sing Chai ◽  
Morris Siu Yung Jong ◽  
Andreja Istenic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Ethical Issues ◽  
Role Playing ◽  
Research Trends ◽  
Inappropriate Use ◽  
Teachers And Students ◽  
Changing Roles

This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

scholarly journals Explaining Courage in Ethical Decision-making by Nursing Managers: A Qualitative Content Analysis

2021 ◽  
Vol 31 (4) ◽  
pp. 254-262
Author(s):  
Mostafa Roshanzadeh ◽  
◽  
Zohreh Vanaki ◽  
Afsaneh Sadooghiasl ◽  
Ali Tajabadi ◽  
...  
Keyword(s):  
Decision Making ◽  
Content Analysis ◽  
Ethical Decision ◽  
Nurse Managers ◽  
Ethical Decision Making ◽  
Ethical Issues ◽  
Qualitative Content Analysis ◽  
Role Playing ◽  
Face To Face ◽  
Nursing Managers

Introduction: Ethical decision-making by nursing managers is influenced by various essential factors, such as courage, without which it is impossible to act on them. Objective: This study aimed to explore the experiences of nursing managers about courage in ethical decision-making. Materials and Methods: The current study was conducted in Iran by a qualitative content analysis approach in 2018. Nineteen nurse managers were selected purposefully from hospitals in Tehran and Shahrekord cities. Data were collected using semi-structured, in-depth, face-to-face interviews, and after transcription, they were analyzed according to the Graneheim and Lundman method. Results: Based on data analysis, we extracted 2 categories (obligation, decisiveness) and 8 subcategories (clearness in expressing decisions, the ability of the manager to make decisions in critical and complex situations, authority/decision-making as a religious responsibility, follow the decision process, being responsible, making compensatory decisions, making preventive decisions). Conclusion: The findings showed that managers who are committed to ethical decision-making have enough assertiveness to make the decisions. Educating, empowering, and sensitizing managers and enhancing their insight into ethical issues through problem-solving and role-playing techniques can play an essential role in promoting their commitment and responsibility.

scholarly journals Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

2021 ◽  
Vol 13 (2) ◽  
pp. 800
Author(s):  
Aras Bozkurt ◽  
Abdulkadir Karadeniz ◽  
David Baneres ◽  
Ana Elena Guerrero-Roldán ◽  
M. Elena Rodríguez
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Research Area ◽  
Learning Processes ◽  
Machine Learning Algorithms ◽  
Research Themes

Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

A Survey of Ambient Intelligence

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Rob Dunne ◽  
Tim Morris ◽  
Simon Harper
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Ambient Intelligence ◽  
Ethical Issues ◽  
State Of The Art ◽  
Research Trends ◽  
Healthcare Facilities ◽  
The Past ◽  
Assistive Living ◽  
Key Terms

Ambient Intelligence (AmI) is the application and embedding of artificial intelligence into everyday environments to seamlessly provide assistive and predictive support in a multitude of scenarios via an invisible user interface. These can be as diverse as autonomous vehicles, smart homes, industrial settings, and healthcare facilities—referred to as Ambient Assistive Living. This survey gives an overview of the field; defines key terms; discusses social, cultural, and ethical issues; and outlines the state of the art in AmI technology, and where opportunities for further research exist. We guide the reader through AmI from its inception more than 20 years ago, focussing on the important topics and research achievements of the past 10 years since the last major survey, before finally detailing the most recents research trends and forecasting where this technology is likely to develop. This survey covers domains, use cases, scenarios, and datasets; cultural concerns and usability issues; security, privacy, and ethics; interaction and recognition; prediction and intelligence; and hardware, infrastructure, and mobile devices. This survey serves as an introduction for researchers and the technical layperson into the topic of AmI and identifies notable opportunities for further research.

scholarly journals Refining the ML/DL Argument for the SensorAble Project

2020 ◽  
Author(s):  
David Ruttenberg ◽  
Kaśka Porayska-Pomsta ◽  
Sarah White ◽  
Joni Holmes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Statistical Approach ◽  
Ethical Issues ◽  
Public Information ◽  
Stress Factors ◽  
Multimodal Data ◽  
Design Build

Is Machine Learning/Deep Learning (ML/DL) a technological necessity when implementing SensorAble or is it something to be investigated because of its potential? Should ML/DL be implemented because it permits processing large quantities of multimodal data enabling modelling of autistic neurocognitive processes that well relate to distractibility and anxiety? Or would interventional prototyping using old-fashioned Artificial Intelligence (AI), Bayesian theory or a hand-crafted rule be preferable?Following Participant Public Information (PPI), can ML/DL techniques permit greater understanding of how disruptions occur and properly align/prepare the groundwork for an interventional prototype? Would heuristics, data mining, or perhaps some other statistical approach adequately provide evidence proceeding a design? With the constellation of supervisors who have invested in this project, can fundamental science properly situate SensorAble in a broader vision that creates practical tools? It is one thing to understand and model a problem. It’s another to simply design/build. Doing the latter may inform the user, but how does it guarantee that other stress factors, ethical issues and newly created anomalies aren’t inadvertently introduced?

PENDIDIKAN BERBASIS BUDAYA (Presfektif Pemikiran H.A.R. Tilaar)

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
AGUNG KURNIAWAN DJIBRAN
Keyword(s):  
Content Analysis ◽  
Text Book ◽  
Interactive Process ◽  
National Education ◽  
Teachers And Students ◽  
The Government ◽  
Education Culture ◽  
Education Opportunity ◽  
Education Concept ◽  
Education Process

AbstractH.A.R. Tilaar emphasizes to the importance of education based on culture, because education is process of culture. Therefore, between the education and culture has been greatly relation, because the education is not able to be separated from culture that has reflected and grown up dynamically in Indonesian society.The purpose of this research is to determine how the education based on culture according to H.A.R. Tilaar’s perspective. The object of this research was H.A.R. Tilaar’s Perspective which concerns to the education based on culture.The approach of this research was literature review. The source of the data were a text book written by H.A.R. Tilaar and other literatures related to this research. The technique of analyzing data were the content analysis of the text book written by H.A.R. Tilaar and other literatures.The result of this research are : (a) H.A.R. Tilaar conceptualizes the education as an culturing processes; (b) the education process is an culturing process through the interactive process between teachers and students; (c) it is necessary to the Government of Indonesia to correct the National education concept by proposing several aspects such as ; (1) the basic value of education; (2) to notice the function of sociological education; (3) the relation between culture and education; (4) the education as The Agent ofChange, and (5) to get the equalization of education opportunity; and (d) to grow up the creative and adaptive thinking toward education phenomenawhich always move dynamically in the environment of the Indonesian community which has its complexity.Keyword: Education, Culture.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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