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Cited by 294

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education

Open access
Dai, Yun, Liu, Ang, Lim, Cher Ping·2023·Procedia CIRP
DOI 10.1016/j.procir.2023.05.002
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Abstract

Higher education is poised at the precipice of the changes and challenges brought about by ChatGPT. This paper addresses some of the most fundamental questions about the role, position, and implications of ChatGPT and generative artificial intelligence (AI) tools amidst the evolving landscape of higher education and modern society. By linking technological affordances with educational needs, we conceptualize ChatGPT as a student-driven innovation with rich potential to empower students and enhance their educational experiences and resources. However, this empowerment comes at a price. It requires collaborative efforts among the stakeholders to address the new and emerging challenges regarding student training, higher education curricula and assessment, and technology development and governance. It also implies new directions for educational research and theories.

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Metadata

Authors
Dai, Y., Liu, A., & Lim, C. P.
Venue
Procedia CIRP
Publisher
Elsevier BV
Published
2023
Volume
119
Pages
84-90
ISSN
2212-8271 (ISSN-L)
DOI
10.1016/j.procir.2023.05.002
OpenAlex
W4383648115
Cited by
294
References
19
FWCI
69.09
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. https://app.cochat.ai/papers/10.1016/j.procir.2023.05.002
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Dai, Y., Liu, A., & Lim, C. P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. Procedia CIRP. https://doi.org/10.1016/j.procir.2023.05.002

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.