The Application of AI Teachers in Facilitating Game-Based Literacy Learning

2022 ◽  
pp. 381-395
Author(s):  
Yixun Li ◽  
Lin Zou
Keyword(s):  
Artificial Intelligence ◽  
Scientific Community ◽  
Empirical Studies ◽  
Scientific Research ◽  
Literacy Learning ◽  
Research Area ◽  
Classroom Teaching ◽  
Theoretical Frameworks ◽  
Key Concepts ◽  
Human Teachers

This chapter discusses the theoretical frameworks for artificial intelligence (AI) teachers and how AI teachers have been applied to facilitate game-based literacy learning in existing empirical studies. While the application of artificial intelligence (AI) in education is a relatively emerging research area, it has received increasing attention in the scientific community. In the future, AI teachers are likely to be able to serve as powerful supplementary tools in classroom teaching in support of human teachers. The main goal here is to provide the readers with new insights on promoting game-based literacy learning from the perspectives of AI teachers. To this end, the authors introduce the readers to the key concepts of AI teachers, the merits and demerits of AI teachers in education, scientific research on AI teachers in literacy learning, and some highlighted examples of AI teachers in literacy classrooms for practical concerns.

Tremendous Contribution of Dr. Shahid Ullah to Scientific Community during COVID-19 Pandemic in the Form of Scientific Research

2020 ◽  
Author(s):  
Sher Ali Khan
Keyword(s):  
Scientific Community ◽  
Scientific Research ◽  
Research Area ◽  
Leading Indicators ◽  
Biological Databases ◽  
Creative Mind ◽  
Global Challenge ◽  
The World ◽  
Local Researcher ◽  
Visualization Of Data

Legends are often the leading indicators of progress, civilization, culture for society and especially for the scientific community. The world is running on such beneficent, diligent, and creative-mind people, which sacrificing their precious time of life for the aid of society. Dr. Shahid Ullah is one of those who tried day night for humanity and have provided a great platform for the scientific community as well as for local researcher in the form of S Khan Lab, which has all updated biological databases of all research area that were not been provided on such friendly finding forum. The purpose of biological databases is to store, organize and distribute data in a standardized and searchable manner to facilitate the processing and visualization of data for humans. Taken together he has collected all biological databases to one easy and friendly finding manner platform which is available at http://www.habdsk.org/ with timely updates. Further, he has also provided two databases on the Covid-19, a global challenge for the scientific community recently.

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.

The Role of Theories of Embodied Cognition in Research and Modeling of Emotions

2019 ◽  
Vol 62 (5) ◽  
pp. 124-138
Author(s):  
Alexandra V. Shiller
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Mirror Neurons ◽  
Empirical Studies ◽  
Unknown Quantity ◽  
Emotional Processes ◽  
Strong Artificial Intelligence ◽  
European Philosophy ◽  
Postmodern Philosophy

The article analyzes the role of theories of embodied cognition for the development of emotion research. The role and position of emotions changed as philosophy developed. In classical and modern European philosophy, the idea of the “primacy of reason” prevailed over emotions and physicality, emotions and affective life were described as low-ranking phenomena regarding cognitive processes or were completely eliminated as an unknown quantity. In postmodern philosophy, attention focuses on physicality and sensuality, which are rated higher than rational principle, mind and intelligence. Within the framework of this approach, there is a recently emerged theory of embodied cognition, which allows to take a fresh look at the place of emotions in the architecture of mental processes – thinking, perception, memory, imagination, speech. The article describes and analyzes a number of empirical studies showing the impossibility of excluding emotional processes and the significance of their research for understanding the architecture of embodied cognition. However, the features of the architecture of embodied cognition remain unclear, and some of the discoveries of recent years (mirror neurons or neurons of simulation) rather raise new questions and require further research. The rigorously described and clear architecture of the embodied cognition can grow the theoretical basis that will allow to advance the studies of learning processes, language understanding, psychotherapy techniques, social attitudes and stereotypes, highlight the riddle of consciousness and create new theories of consciousness or even create an anthropomorphic artificial intelligence that is close to “strong artificial intelligence.”

Research Methods in Clinical Psychology

2010 ◽  
pp. 51-76 ◽  
Author(s):  
Philip C. Kendall ◽  
Jonathan S. Comer
Keyword(s):  
Scientific Community ◽  
Measurement Data ◽  
Design Procedure ◽  
Mechanisms Of Change ◽  
Random Assignment ◽  
Scientific Evaluation ◽  
Scientific Rigor ◽  
Design Considerations ◽  
Key Concepts ◽  
Treatment Manuals

This chapter describes methodological and design considerations central to the scientific evaluation of treatment efficacy and effectiveness. Matters of design, procedure, measurement, data analysis, and reporting are examined and discussed. The authors consider key concepts of controlled comparisons, random assignment, the use of treatment manuals, integrity and adherence checks, sample and setting selection, treatment transportability, handling missing data, assessing clinical significance, identifying mechanisms of change, and consolidated standards for communicating study findings to the scientific community. Examples from the treatment outcome literature are offered, and guidelines are suggested for conducting treatment evaluations that maximize both scientific rigor and clinical relevance.

scholarly journals Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 441
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Li-Fong Lin ◽  
Shuo-Chen Chien ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
English Language ◽  
R Package ◽  
Research Area ◽  
Republic Of China ◽  
Comprehensive Picture ◽  
The Usa ◽  
The Republic ◽  
Academy Of Sciences

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

Artificial Intelligence in Legal Practice: Exploring Theoretical Frameworks for Algorithmic Literacy in the Legal Information Profession

2021 ◽  
Vol 21 (2) ◽  
pp. 97-117
Author(s):  
Dominique Garingan ◽  
Alison Jane Pickard
Keyword(s):  
Artificial Intelligence ◽  
Computer Literacy ◽  
Intelligent Systems ◽  
Technological Literacy ◽  
Legal Practice ◽  
Theoretical Frameworks ◽  
User Experiences ◽  
Legal Information ◽  
Literacy Initiatives ◽  
Academic Settings

AbstractIn response to evolving legal technologies, this article by Dominique Garingan and Alison Jane Pickard explores the concept of algorithmic literacy, a technological literacy which facilitates metacognitive practices surrounding the use of artificially intelligent systems and the principles that shape ethical and responsible user experiences. This article examines the extent to which existing information, digital, and computer literacy frameworks and professional competency standards ground algorithmic literacy. It proceeds to identify various elements of algorithmic literacy within existing literature, provide examples of algorithmic literacy initiatives in academic and non-academic settings, and explore the need for an algorithmic literacy framework to ground algorithmic literacy initiatives within the legal information profession.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

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