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Impact of Artificial Intelligence 2.0 on Teaching and Learning

Published: 23 April 2020 Publication History

Abstract

Artificial intelligence (AI) has experienced more than 60 years of development and accumulated a strong technical foundation. In the past ten years, AI has gone through computational intelligence and perceptual intelligence, and is rapidly moving towards cognitive intelligence. AI has stepped from the 1.0 era, which is only recognized and used by elite minority groups, to the 2.0 era, which is widely used by all walks of life. With the rapid development and application of natural language understanding, facial expression recognition, educational data, virtual reality and robotics, the new intelligent teaching system after introducing these technologies is carrying out intelligent transformation and reconstruction of knowledge representation and learning process, and capturing and perceiving and analyzing learners' knowledge in real time. Learning state, automatic assessment, learning analysis, emotional perception, simulation teaching and intelligent companionship become the proper functions of intelligent teaching system. AI 2.0 will bring about great changes in the field of education. Teaching and learning are undergoing a series of changes, ranging from the generation and flow of knowledge, the form and interaction of learning, the form and construction of learning resources, the organization and implementation of teaching content, expert system, ubiquitous learning to the evaluation and management of teaching performance.

References

[1]
Pan Yunhe. Artificial Intelligence Towards 2.0. Engineering, 2016, 2 (4): 1--6.
[2]
Mou Zhijia. Rethinking and Explaining Individualized Learning Theory in the Age of Artificial Intelligence +. Journal of Distance Education, 2017 (3): 22--30.
[3]
White House. Artificial Intelligence, Automation, and the Economy. [2017-01-25]. https://obamawhitehouse.archives.gov/blog/2016/12/20/artificial-intelligence-automation-and-economy.
[4]
White House. Preparing for the Future of Artificial Intelligence. [2016-12-30]. https://obamawhitehouse.archives.gov/blog/2016/05/03/preparation-future-artificial-intelligence.
[5]
National Science and Technology Council. National Artificial Intelligence Research and Development Strategic Plan. [2016-12-28]. https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf.
[6]
Stanford University. Artificial Intelligence and Life in 2030. [2017-05-20]. http://www.donews.com/news/detail/1/2959778.html.
[7]
Circular of the State Council on the issuance of a new generation of AI development plan. [2017-07-26]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.html.
[8]
Li Jiuyu. The United Nations convened the first AI Summit: Let AI contribute to 17 sustainable development goals. [2017-07-01]. https://news.uc.cn/a_148308135966785100/.
[9]
Silver D, Huang A, Maddison C J, et al. Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature, 2016, 529 (7587): 484.
[10]
Silver D, Schrittwieser J, Simonyan K, et al. Mastering the Game of Go without Human Knowledge. Nature, 2017, 550 (767676): 354.
[11]
Dai Lirong, Zhang Shiliang, Huang Zhiying. Present situation and Prospect of speech recognition technology based on in-depth learning. Data acquisition and processing, 2017, 32(2): 221--231.
[12]
Wu Ningchuan. Computer vision is approaching the inflection point. Microsoft algorithm is trying to cross this "one step away". [2016-01-22]. http://www.tmtpost.com/1500298.html.
[13]
Huang Kaiqi, Chen Xiaotang, Kang Yunfeng, Tan Tieniu. Overview of Intelligent Video Surveillance Technology. Journal of Computer Science, 2015, 38 (6): 1093--1118.
[14]
He Bin. e-Learning Emotional Computing Model Design Research. Journal of Distance Education, 2011, 29 (4): 103--110.
[15]
Gu Xiaoqing, Zhang Jinliang, Cai Huiying. Learning Analysis: Emerging Data Technology. Journal of Distance Education, 2012, 30 (1): 18--25.
[16]
Mou Zhijia. Individualized learning path cracking supported by learner data portrait - value assignment of learning computation. Journal of Distance Education, 2016, 34 (6): 11--19.
[17]
Wufati, Mou Zhijia. Research on the framework design of predicting learning outcomes based on learner's personality and behavior analysis. China Audiovisual Education, 2016 (1): 41--48.
[18]
Yang Xue, Jiang Qiang, Zhao Wei, Li Yongfan, Li Song. Diagnosis and Intervention of Online Learning Delay Based on Learning Analysis in the Big Data Era. Research on Audiovisual Education, 2017, 38 (7): 51--57.
[19]
Mou Zhijia. Rethinking and Explaining Individualized Learning Theory in the Age of Artificial Intelligence +. Journal of Distance Education, 2017, 35 (3): 22--30.
[20]
Hork Kang. The Theory, Technology and Method of Promoting Individualized Learning: Learning and Thinking of the Handbook on Educational Communication and Technology Research (4th Edition) of the United States. Open Education Research, 2017, 23 (2): 13--21.
[21]
Yu Liang, Chen Shijian, Wu Di. Diversity, Co-creation and Precision Pushing: New Development of Digital Education Resources. China Audiovisual Education, 2016 (4): 52--57, 63.
[22]
Ye Haizhi, Zhao Yaoyuan, Wu Jinpigeon. Design and application of dynamic evaluation system based on Web. Research on audio-visual education, 2014, 35 (9): 79--84.
[23]
Wang Zhuli. A New View on Knowledge and Learning in the Intelligent Age. Journal of Distance Education, 2017, 35 (3): 3--10.
[24]
Wu Jianzhong, knowledge is mobile. Shanghai: Shanghai Far East Publishing House, 2015.
[25]
Chen Weidong, Zhu Leyang, Yang Li, Ye Xindong. 4D Printing Technology and Its Educational Application Prospects - Also on the Integration of Artificial Intelligence and Education. Journal of Distance Education, 2018, 36 (1): 27--38.
[26]
Yu Shengquan. The Future Role of Artificial Intelligence Teachers. Open Education Research, 2018, 24 (1): 16--28.
[27]
Jia J. Intelligent Tutoring Systems. Encyclopedia of Educational Technology, 2015: 411--413.
[28]
Liu Fukui, Liu Meiling. Research on Ubiquitous Learning. Software Guide, Educational Technology, 2009, (2): 5--7.
[29]
Lu Wenhui. The Connotation, Function and Realization Approach of Intelligent Adaptation Learning Platform in AI+5G Vision. Journal of Distance Education, 2019, (3): 38--46
[30]
Guo Jiong, Zheng Xiaojun. A summary of research and analysis based on big data. Audiovisual Education in China, 2017 (1): 121--130.

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  • (2024)Teaching AI Literacy for Innovation: Work in ProgressProceedings of the 2024 Computers and People Research Conference10.1145/3632634.3655874(1-4)Online publication date: 29-May-2024

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    cover image ACM Other conferences
    ICEIT 2020: Proceedings of the 2020 9th International Conference on Educational and Information Technology
    February 2020
    268 pages
    ISBN:9781450375085
    DOI:10.1145/3383923
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    • University of Thessaly: University of Thessaly, Volos, Greece

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    Published: 23 April 2020

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    Author Tags

    1. artificial intelligence
    2. cross-media intelligence
    3. intelligent teaching system
    4. natural language understanding
    5. ubiquitous learning

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    • (2024)Teaching AI Literacy for Innovation: Work in ProgressProceedings of the 2024 Computers and People Research Conference10.1145/3632634.3655874(1-4)Online publication date: 29-May-2024

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