Exploring the Challenges and Future Directions of Big Data and AI in Education

Khanssa Mohammed Elam ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 5, issue 1, 81-93

Abstract: The integration of Big Data and Artificial Intelligence (AI) in education holds transformative potential, promising enhanced personalized learning experiences, improved administrative efficiency, and advanced predictive analytics. However, the adoption of these technologies also presents significant challenges. This paper explores the current landscape of Big Data and AI in education, identifying key challenges such as data privacy concerns, the digital divide, the need for teacher training, and the integration of AI with existing educational frameworks. Additionally, it examines potential future directions, including the development of ethical guidelines, advancements in adaptive learning technologies, and the creation of more inclusive and equitable AI systems. By addressing these challenges and leveraging future opportunities, the educational sector can harness the full potential of Big Data and AI to improve learning outcomes and operational efficiencies.

Keywords: Big Data; Artificial Intelligence (AI); Education Technology; Personalized Learning; Data Privacy; Digital Divide (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://newjaigs.com/index.php/JAIGS/article/view/173 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:5:y:2024:i:1:p:81-93:id:173

Access Statistics for this article

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

More articles in Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 from Open Knowledge
Bibliographic data for series maintained by Open Knowledge ().