利用层次分析法评估大流行病中基于人工智能的高等教育数字化学习系统

Vikrant Vikram Singh, Nishant Kumar, Shailender Singh, Meenakshi Kaul, Aditya Kumar Gupta, P. K. Kapur
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引用次数: 0

摘要

2020 年在全球范围内流行的 COVID-19 病毒造成的破坏性影响引发了大范围的封锁和限制,这种影响将持续数十年。疫情在教育工作者及其学生中造成的影响最为显著,这促进了各种基于人工智能的学习系统在教育系统中的有效性。本研究根据高等教育机构中基于人工智能的数字化学习平台的各种特点和用途,对这些系统进行了研究。本研究从现有文献中获取了基于人工智能的数字化学习系统的几个重要方面,并选取了重要文章,以正确研究多所高等院校使用的基于人工智能的数字化学习平台的各种特点和功能。根据关键因素及其子因素,采用层次分析法(AHP)对多个基于人工智能的学习系统进行排序。研究结果揭示了各高等教育机构在开发数字化学习系统时有效使用了哪些人工智能系统。
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Assessment of artificial intelligence-based digital learning systems in higher education amid the pandemic using analytic hierarchy
The devastating effects of the 2020 worldwide COVID-19 virus epidemic prompted widespread lockdowns and restrictions, which will continue to be felt for decades. The repercussions of the pandemic have been most noticeable among educators and their students, which boosts the effectiveness of various AI-based learning systems in the education system. This study examines the AI-based digital learning platforms in higher education institutions based on various characteristics and uses of these systems. Several significant aspects of AI-based digital learning systems were obtained from the available literature, and significant articles were selected to properly examine various characteristics and functions of AI-based digital learning platforms used by multiple higher education institutions. The analytical hierarchy process (AHP) is employed to rank multiple AI-based learning systems based on key factors and their sub-factors. The study's outcome revealed which AI systems are effectively used in developing digital learning systems by various higher education institutions.
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