An Analysis of Domestic Research Trend of Artificial Intelligence Education

2021 ◽  
Vol 3 (2) ◽  
pp. 51-62
Author(s):  
Jung-Ho Park ◽  
Keyword(s):  
Artificial Intelligence ◽  
Research Trend ◽  
Intelligence Education

scholarly journals Analysis and Prediction of “AI + Education” Attention Based on Baidu Index—Taking Guizhou Province as an Example

2021 ◽  
Vol 13 (5) ◽  
pp. 120
Author(s):  
Yulin Zhao ◽  
Junke Li ◽  
Jiang-E Wang
Keyword(s):  
Artificial Intelligence ◽  
Southwest China ◽  
Arima Model ◽  
Modern Technology ◽  
Guizhou Province ◽  
Western Region ◽  
Elastic Coefficient ◽  
The Sustainable Development ◽  
Intelligence Education ◽  
Coefficient Method

Studying the attention of “artificial intelligence + education” in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of “artificial intelligence (AI) + education” in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public’s attention to “AI + education” differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas.

scholarly journals Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective

2020 ◽  
Vol 12 (14) ◽  
pp. 5568 ◽  
Author(s):  
Thomas K.F. Chiu ◽  
Ching-sing Chai
Keyword(s):  
Artificial Intelligence ◽  
Curriculum Design ◽  
Teaching Experience ◽  
Self Determination Theory ◽  
Curriculum Planning ◽  
Self Determination ◽  
Structured Interviews ◽  
Theoretical Frameworks ◽  
Intelligence Education ◽  
K 12

The teaching of artificial intelligence (AI) topics in school curricula is an important global strategic initiative in educating the next generation. As AI technologies are new to K-12 schools, there is a lack of studies that inform schools’ teachers about AI curriculum design. How to prepare and engage teachers, and which approaches are suitable for planning the curriculum for sustainable development, are unclear. Therefore, this case study aimed to explore the views of teachers with and without AI teaching experience on key considerations for the preparation, implementation and continuous refinement of a formal AI curriculum for K-12 schools. It drew on the self-determination theory (SDT) and four basic curriculum planning approaches—content, product, process and praxis—as theoretical frameworks to explain the research problems and findings. We conducted semi-structured interviews with 24 teachers—twelve with and twelve without experience in teaching AI—and used thematic analysis to analyze the interview data. Our findings revealed that genuine curriculum creation should encompass all four forms of curriculum design approach that are coordinated by teachers’ self-determination to be orchestrators of student learning experiences. This study also proposed a curriculum development cycle for teachers and curriculum officers.

scholarly journals Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Membranes ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 672
Author(s):  
Md. Ashrafuzzaman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Research Trend ◽  
Cellular Processes ◽  
Research Areas ◽  
Huge Data ◽  
Functional Aspects

Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

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