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Institutional Efforts to Help Academic Researchers Implement Generative AI in Research

Forthcoming. Now Available: Just Accepted Version.
Published onFeb 26, 2024
Institutional Efforts to Help Academic Researchers Implement Generative AI in Research


The scale and speed of the Generative AI revolution, while offering unprecedented opportunities to advance science, is also challenging the traditional academic research model in fundamental ways.  The academic research model and academic institutions are not set up to be nimble in the face of rapidly advancing technologies, and the task of adopting such new technologies usually falls on individual researchers. Excitement about the opportunities that Generative AI brings is leading to a rush of researchers with various levels of technical expertise and access to resources to adopt this new technology, which could lead to many researchers “reinventing the wheel” and research outcomes lacking in ethics, rigor and reproducibility. This problem not only applies to Generative AI, but could also be true for other upcoming and similarly disruptive technologies. We argue that the current norm of relying on individual researchers for new technology adoption is no longer adequate. It is time that academic institutions and their research organizations such as our own (the Michigan Institute for Data Science) develop new mechanisms to help researchers adopt new technologies, especially those that cause major seismic shifts such as Generative AI. We believe this is essential for helping academic researchers stay at the forefront of research and discovery, while preserving the validity and trustworthiness of science. 

Keywords: institutional transformation, best practices, training, academic researcher, rigor and reproducibility, institutional support

02/26/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

©2024 Jing Liu and H. V. Jagadish. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

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