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Artificial intelligence in higher education database (AIHE V1): Introducing an open-access repository
Journal article   Open access   Peer reviewed

Artificial intelligence in higher education database (AIHE V1): Introducing an open-access repository

Fadhil Ismail, Joseph Crawford, Shannon Tan, Jurgen Rudolph, Eunice Tan, Pauline Seah, Fiona Xiaofei Tang,, Florence Ng, Laura Visser Kaldenbach, A Anand Naidu, …
Journal of applied learning and teaching, Vol.7(1), pp.140-148
2024
DOI: https://doi.org/10.37074/jalt.2024.7.1.35
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Published536.26 kBDownloadView
CC BY V4.0,  Open Access

Abstract

Teaching and Learning
Generative artificial intelligence (GenAI) has fired the world’s imagination. The higher education sector is not immune from the GenAI hype, panic, and mania. The emergence of artificial intelligence, in its newest form, into curriculum, student life, and learning has created an entanglement of technology, people, and learning. Yet, there is still a lack of cohesive accounts of the emergent literature used to inform practical learning and teaching decisions. Our manuscript responds with the deployment of a previously published systematic literature review to create the first version of the Artificial Intelligence in Higher Education Database (AIHE V1). Published in conjunction with this article, we pioneer an open-access resource to support learning and teaching scholars to gain timely access to pre-examined literature on AI and higher education. This first version documents 160 manuscripts published between 30 November 2022 and 31 December 2023. Using a rigorous systematic review method, engaging in the PRISMA approach, we offer a first glance at the metadata of articles published on AI and higher education during the first year of ChatGPT.

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