Guest Post – AI Fatigue and Vocational Awe in Academic Libraries – The Scholarly Kitchen

Guest Post — AI Fatigue and Vocational Awe in Academic Libraries

• By Greyson Pasiak, Mar 4, 2026, Time To Read: 7 mins

Today’s post is by Greyson Pasiak from the Rochester Institute of Technology. Grey is the liaison librarian for the students, faculty, and staff of the Golisano College of Computing and Information Sciences, as well as the Student Success Librarian, at RIT.

Adapting to new technologies is a part of library culture, as is the notion that the profession requires an infallible desire to go above and beyond work duties to best serve our patrons. Fobazi Ettarh explains this phenomenon in “Vocational Awe and Librarianship: The Lies We Tell Ourselves.” Ettarh’s essay explores the historical relationship between librarianship and ideas of a sacred duty, or sacrifice and struggle, while examining how concepts of efficiency are tied to passion, both institutionally and personally, which she has coined “vocational awe.” Ettarh concludes that the harm done through a self-sacrificing workplace culture leads to burnout. She advocates for the dismantling of the idea that librarianship is a sacred calling that requires struggle, sacrifice, and obedience.

Through the framework of vocational awe, I will explore how an expanded workload created by the implementation of generative AI can lead those in higher education to burnout. Academic librarians are increasingly tasked with creating and implementing new policies and ethical guidelines surrounding generative AI’s role in research and publishing practices. They are called through vocational language to educate on safe, transparent, and responsible use of AI. These new roles and responsibilities are coupled with insufficient time and general support, resulting in faculty and staff feeling fatigued. Many have already addressed how fatigue in higher education can ultimately lead to interruptions in publishing support and academic research if not addressed.

Libraries and Technology

Starting in 2020, there was a shift in digital and online culture that was the perfect forerunner for the rise of generative AI tools in libraries. During COVID, institutional and public libraries alike saw a change in patron needs that resulted in communities utilizing the libraries’ internet access, digital collections, virtual programs, and electronic services in droves. This pushed libraries to expand access and advanced discussions around digital equity and access to information. Libraries showcased their adaptability and community support systems in a way that hadn’t been seen in 30 years. Librarians experienced an arguably tenable shift in expected responsibilities surrounding tech support for patrons and digital access to materials as well. The call and response is not new, as libraries have been at the forefront of new technologies as early as 1964, when the Library of Congress introduced computers.

However, generative AI is remarkably different from previous technology waves in many ways. It not only hits all the traditional trigger points for librarians, such as privacy, access to information, misinformation, digital equity, copyright, transparency, and accountability, but it is also notably one of the fastest-evolving technologies we have seen to date. In addition, the tools themselves are faulty and still works-in-progress. Society, and librarians in particular, are scrambling to stay up to date with functionality while working to create policy or best practices around using generative AI for scholarly knowledge discovery, production, and dissemination.

It makes sense that academic libraries are at the forefront of learning about, teaching about, providing access to, and advocating for safe use of AI in higher education based on the profession’s historical relationship with technology and the evolution of needs-based services. As many librarians are also educators on topics such as misinformation and credibility, they recognize that their expertise is important here, leading to an increase in vocational language signaling to fellow librarians that it is our moral responsibility to address these challenges. However, all that it entails — learning and teaching about generative AI — falls outside the scope of many of our current day-to-day activities.

AI Fatigue

Information fatigue is not new; in fact, this sentiment dates back to the 16th century. In 1545, Conrad Gesner warned of an abundance of books. In 1945, Vannevar Bush discussed a fear of an unproductive information explosion. The term information overload was first used in the 1960s by librarians, information studies, and management scholars. In 1984, Craig Brod introduced the term technostress, which refers to the negative relationship between mental health and the introduction of new technologies. In a 2025 Forbes article, Bryan Robinson, Ph.D., argued that app and platform switching causes digital tool fatigue. What these terms all express is mental exhaustion and confusion leading to diminished focus, creativity, and/or declining mental health due to an ever increasing access to information and technology.

Editor’s Note: Read the rest of the story, at the below link.

Source: Guest Post — AI Fatigue and Vocational Awe in Academic Libraries – The Scholarly Kitchen