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Pathology of artificial intelligence applications in education Using the SWOT analysis method
Afshin Motaghi Destenaei, Ali Karami, Milad Piri Fath Abad,
Volume 0, Issue 0 (5-2022)
Abstract
Background and purpose: During the last 25 years, artificial intelligence has made achievements in some fields that have also affected education, although criticisms have also been raised against excessive optimism regarding contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methodology: This study was conducted using the SWAT analysis method. The research community includes 20 articles on the application of artificial intelligence in education, which have been collected using Sandelowski and Barroso's systematic review framework.
Research findings: The findings of the SWAT analysis of the use of artificial intelligence in education show that the strengths include the effective use of computer-based STEM systems, teachable agents for natural language learning, learning tool ecosystems, and evaluation of learning models. On the other hand, weaknesses include dumb training systems, biased data and algorithms, and interference of AI customization with standardization. Opportunities include changing the role of teacher to guide, freeing teachers from routine tasks, providing assistance to students anytime and anywhere, and enabling personalized learning. But the threats include changing the role of the teacher to the custodian of the system, fear of unemployment, lack of readiness to use artificial intelligence in education, and disruption in the creation of independent learning skills in students along with its unquestioning acceptance due to the hype surrounding artificial intelligence.
Conclusion: From various aspects, it seems that artificial intelligence has an advertising mode, but like other advertising fields, it has the potential to grow with specific applications in educational and learning activities. Being aware of artificial intelligence and studying the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence for the use of artificial intelligence in education.
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Machine Learning and Citizen Science: Opportunities and Challenges of Human-Computer Interaction
Ms. Maryam Abolghasemi, Dr. Fatemeh Fahimnia,
Volume 8, Issue 4 (2-2022)
Abstract
Background and Aim: In processing large data, scientists have to perform the tedious task of analyzing hefty bulk of data. Machine learning techniques are a potential solution to this problem. In citizen science, human and artificial intelligence may be unified to facilitate this effort. Considering the ambiguities in machine performance and management of user-generated data, this paper aims to explain how machine learning can be combined with the active citizenship concept. In addition, it discusses the necessary conditions for advancing the citizen science and beyond.
Method: The review method and comprehensive systematic study was applied to assess the concept of machine learning, citizen science and human-computer interaction.
Results: Many research problems seem to be computationally insolvable and may demand human cognitive skills. Therefore, due to classification activities which are performed in the majority of large-scale citizenship science projects, in addition to participants who may learn lessons about the science, machines also learn lessons about human and imitate him and slowly its learning capacity enhances over time. Artificial intelligence, particularly machine learning is a debatable topic with related ambiguities and biases which should strongly take into consideration.
Conclusion: The application of machine learning techniques carries many advantages including classification time cut and masterful evaluations in the process of making decisions on big data sets. However, algorithms usually act as a black box where data biases are not observable at first glance. Taking this problem into consideration may mitigate serious risks arising from the application of such techniques.
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