Leveraging AI Technologies in Libraries through Experimentation-Driven Frameworks

Abstract

This column aims to explore the frameworks to help libraries foster digital innovation by leveraging AI technologies through continuous experimentation to innovate their services for their patrons. Additionally, the column seeks to highlight the benefits and interplay between the frameworks, providing insights for librarians interested in implementing AI solutions and driving technological advancements in library settings. The column reports two frameworks - The Need-Based Experimentation (NBE) Framework and the Curiosity-Based Experimentation (CBE) Framework based on the author’s professional experiences and empirical observations of 10 university libraries’ experimentation-driven AI technology adoption practices. The NBE framework focuses on experimenting with AI technologies that have the functional capability to address the library’s current business needs. In contrast, the CBE framework explores AI technologies out of curiosity, aiming to gain practical experiences and uncover potential future applications, aligned with the librarian’s interests. These frameworks guide librarians to effectively experiment with AI technology based on their motivations and goals. To the best of the authors’ knowledge, there is no experimentation-driven framework for adopting AI technologies to assist libraries do so strategically. The adoption of AI should be influenced by carefully planned, ongoing experiments, the results of which should be deployed in real to inform adoption decisions.

Keywords:

• Artificial intelligence
• curiosity-based experimentation (CBE) framework
• experimentation
• libraries
• need-based experimentation (NBE) framework
• technology adoption

Acknowledgments

The research’s authors would like to acknowledge the library staff members who took part in the study.

Disclosure statement

The authors declare no conflict of interest.

Institutional review board statement

The conducted study was approved by the Institutional Review Board of Gisma University of Applied Sciences, Potsdam, Germany under protocol number 02/2023.

Notes

1 https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:-28:ed-1:v1:en

2 https://hbr.org/2023/05/ai-can-help-you-ask-better-questions-and-solve-bigger-problems

Additional information

Funding

This research is funded by the European Union’s Horizon research and innovation programme (Project ID: 101061516, Project Acronym: LibrarIN). This publication solely reflects the views of the authors, and the Agency cannot be held responsible for any use made of the information contained herein. The paper was also co-funded by Winning Scientific Management, Portugal (Project ID: WINBUSMOD001).