Syllabus Design and Planning with Artificial Intelligence and the Potential of Generative AI
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.
Authors
- Мурат Кожанов (ORCID: https://orcid.org/0000-0002-5310-9953)
- L. L. La (ORCID: https://orcid.org/0000-0001-6445-9919)
- Azamat Amirov (ORCID: https://orcid.org/0000-0001-9255-9191)
- Valéria Póser
- Aliya Shakhatova (ORCID: https://orcid.org/0000-0003-4755-5698)
- Amir Mosavi (ORCID: https://orcid.org/0000-0003-4842-0613)
- Dinara Kaibassova (ORCID: https://orcid.org/0000-0002-8410-7758)
- György Eigner (ORCID: https://orcid.org/0000-0001-8038-2210)
Institutions
- L. N. Gumilyov Eurasian National University (KZ)
- Obuda University (HU)
- Abylkas Saginov Karaganda Technical University (KZ)
- Astana Medical University (KZ)
Publication Details
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1109/cinti63048.2024.10830766
- Citations
- 2
- Primary Topic
- Engineering Education and Technology
- Type
- article
- Field-Weighted Citation Impact
- 1.56