Architecture to transform classic academic courses into adaptive learning flows with artificial intelligence - Andrei Bobocea; Răzvan Bologa; Lorena Bătăgan; Bogdan-Ștefan Posedaru (Amfiteatru Economic, 2024)
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 time-consuming, 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.
Keywords: A20; I20; I21; 330; AI content generation; artificial intelligence; adaptive learning; learning flows; personalised learning; educational content