Architecture to Transform Classic Academic Courses into Adaptive Learning Flows with Artificial Intelligence
The literature on adaptive learning suggests that it can provide significant improvements to the educational process and numerous studies have found a necessity for personalised learning, which is one of the strong suits of adaptive learning. Adaptive learning platforms require that content be ef...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editura ASE
2024-02-01
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| Series: | Amfiteatru Economic |
| Subjects: | |
| Online Access: | https://www.amfiteatrueconomic.ro/temp/Article_3298.pdf |
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| Summary: | The literature on adaptive learning suggests that it can provide significant improvements to
the educational process and numerous studies have found a necessity for personalised
learning, which is one of the strong suits of adaptive learning. Adaptive learning platforms
require that content be effective, and lack thereof has hindered large-scale adoption by adding
the cost of content creation to the upfront implementation cost and creating a 'critical mass'
type problem where a platform without content is ineffective and unattractive, leading to lack
of interest from users and lack of funding for developing new content. Artificial intelligence
(AI) technology has the potential to aid in content creation by taking on a significant part of
the workload. This paper aims to explore this possibility and propose an architecture based
on current artificial intelligence technologies that will help teachers and experts transform
classic course materials into adaptive learning flows. The system is not autonomous and will
not replace a human expert but rather will take on some of the more straightforward, but timeconsuming, work. The proposed approach results in a distinct system, independent of the
adaptive learning platform itself, that can help rephrase, restructure and enrich the content,
resulting in an automated digital narrative, or fragment thereof, that can be exported in a
format based on open standards and used within an adaptive learning platform of choice. |
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| ISSN: | 1582-9146 2247-9104 |