“Future education trend learned from the COVID-19 pandemic: take artificial intelligence online course as an example,”
Open accessAbstract
COVID-19 Pandemic facilitates the development of online courses on a large scale. As a supplementary teaching in a special period, online courses play a gratifying role in enabling students to learn repeatedly and master effectively. However, it cannot be ignored that there are still some problems, and we need to find better solutions. This article takes the “Artificial Intelligence” course as an example, through a set of data analysis, puts forward the improvement plan of the network course, and discusses the “before and after class” teaching mode in blended learning. Through this way we could have a glimpse of the future education, which makes full use of online courses and uses online courses as an icing on the cake for traditional classrooms.
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Online teaching platforms make it easier to incorporate web-based content, smart interactive elements and asynchronous review sessions to keep students more engaged.
“Online teaching platforms also make it easier to incorporate web-based content, smart interactive elements and asynchronous review sessions to keep students more engaged (Kexin et al., 2020; Pantelimon et al., 2021).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Kexin et al., 2020
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Artificial Intelligence (AI) applications for COVID-19 pandemic
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.012 · Cited by 1,502 · see citing works
The Past, Present, and Future of Orthopedic Education: Lessons Learned From the COVID-19 Pandemic
Stambough, J. B., Curtin, B. M., Gililland, J. M., et al. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.04.032 · Cited by 163 · see citing works
Lin, C.-Y., Huang, C.-K., & Ko, C.-J. (2019) · Australasian Journal of Educational Technology · doi:10.14742/ajet.4446 · Cited by 73 · see citing works
Algorithm of Video Semantic Classification Based on IAGA and Deep Convolution Neural Network
Wang, M., Liu, K., Wei, M., et al. (2018) · Cited by 2 · see citing works
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Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
- Authors
- Kexin, L., Yi, Q., Xiaoou, S., & Yan, L.
- Venue
- 2020 International Conference on Artificial Intelligence and Education (ICAIE)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 108-111
- OpenAlex
- W3109195939
- Cited by
- 25
- References
- 4
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 6 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 1
- FWCI
- 12.88
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Topics
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Education › Education during COVID-19 pandemic
- Social Sciences › Education › E-Learning and COVID-19
- Keywords
- COVID-19 pandemic, online courses, artificial intelligence in education, blended learning, future of education, supplementary teaching, classroom teaching
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