Syllabus Design and Planning with Artificial Intelligence and the Potential of Generative AI

Murat G. Kozhanov, Amir Hosein Mosavi, Azamat Amirov, Dinara Kaibassova, Valéria Póser, Aliya Shakhatova, L. L. La, György Eigner

2024

Closed access Metadata (CSL-JSON)

DOI 10.1109/cinti63048.2024.10830766

Data confidence 70 medium

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This article introduces a methodology to identify the fundamental studies carried out using artificial intelligence for syllabus design and planning. A taxonomy of the most relevant studies is presented based on the method and applications. The main studies in the field are reviewed and their contributions are emphasized. The meta-analysis of the results presents the extent and applicability of artificial intelligence in syllabus design and planning. The result shows the essential role of machine learning in the development and improving the process of syllabus design and planning.