Towards Smart Open Education Ecosystems through Generative AI, Blockchain, DAO, MMLA and NFT
12 Pages Posted: 25 Jul 2023 Last revised: 1 Sep 2023
Date Written: July 19, 2023
Abstract
The aim of this research was to explore how DAOs, blockchain, NFTs, generative AI, and multimodal learning analytics technologies can be integrated to create a smart open education ecosystem representing a global, seamless, personalized, and democratic education system. The integration of these technologies can help to overcome the challenges faced by today's education systems and enhance accessibility, affordability, and quality of education worldwide. Educational technologies have never been closer to overcoming the issues of democracy and accessible education, which are among the biggest handicaps of contemporary education systems. Moreover, the successful integration of these technologies can create unique opportunities for personalized learning. Initiatives exemplifying this timely paradigm shift, such as The Open Campus project, have already begun to emerge. In this study, the potential uses of these technologies for a smart education ecosystem were examined with a scoping review of 30 most related studies. Afterwards, contributions, possible task distributions, and integration of these technologies were explained using authentic examples and real-world scenarios. At the end of the study, 12 key benefits of smart open education ecosystem were explained: accessibility, personalization, educational quality, security and transparency, democratic governance, intellectual property and content value, open collaboration and integration, equality and equal opportunity, reusability of educational resources, preservation and promotion of cultural and linguistic diversity, innovation and experiential learning in education, and support for student success and career planning. This article was produced within the scope of Anadolu University BAP Project No. 2207E125.
Keywords: open education, generative AI, blockchain, DAO, multimodal learning analytics, NFT
JEL Classification: I2, I23
Suggested Citation: Suggested Citation