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.
The term AI Fatigue follows suit and is summed up in a workplace context in an article from TechTarget by Rosa Heaton. “AI fatigue is the feeling of mental exhaustion and overwhelm due to continuous — and increased — exposure to AI technologies.” Heaton explains that “employees overwhelmed by the rapid increase of tools and systems may feel more stressed and anxious in the workplace, leading to lower job satisfaction. The expectation that AI will improve performance will likely place further pressure on employees.” Librarians, like employees referenced above, are struggling to learn new vocabulary, new technologies, and new work procedures, all with limited support or instruction. In academia, the exposure is two-fold; in addition to navigating a new technology, we are rethinking how to approach information discovery and reliability, as well as research and publishing practices without corresponding resources and support.
Vocational Awe
The emergence of generative AI in higher education has been so swift that creating channels of support for faculty, staff, and students feels like it was put on the back burner. Promotion and integration have been the focus, leaving many to question the intent and value of such an accelerated incorporation. People who are unwilling or unable to learn about AI are being characterized as disobedient, doing the bare minimum, and/or ostracized by their peers and supervisors for not being committed to the future of the institution, its faculty/staff, and students. The rhetoric, “get on board or get left behind,” is prevalent at academic conferences and in articles. This motivational style does nothing but preemptively induce feelings of inadequacy.
Although championed less loudly, anti-AI sentiment has taken large strides in the last several years. Violet Fox, blogger for ACRLog, wrote a post highlighting new movements towards libraries refusing AI. Kay Slater published “Against AI: Critical Refusal in the Library,” which discusses the political and ethical issues surrounding generative AI integration in libraries. Although learning about AI isn’t comparable to a life-or-death medical intervention mentioned by Ettarh, many librarians are facing moral and ethical dilemmas surrounding the use of the technology. Privacy, bias, transparency, and other concerns that librarians have against using AI are also among the reasons that others are advocating for its inclusion in academic education.
Recent conversations around AI in higher education highlight how librarians are feeling an obligation to learn more about AI while at the same time being left out of important institutional conversations. It’s an uphill battle to argue for our inclusion at the policy table. An example of this call for librarians to insist upon inclusion is Michael Hanegan and Chris Rosser’s book, Generative AI and Libraries: Claiming Our Place in the Center of a Shared Future. Hanegan and Rosser advise libraries to embrace their “unique position as ethical stewards and trusted guides” to become a positive influence in policy and general use. They highlight the library’s historical approach to reorganizing spaces and practices to support new ways of accessing technology and information.
In the introduction, the authors use vocational phrases such as foundational presence, essential character, and fundamental societal role to describe librarianship. This type of virtue signaling as a call to action is widespread in current library literature and think pieces surrounding AI. When faced with the concerns generative AI raises combined with the rhetoric of personal and professional inadequacy, it’s easy to see how vocational awe can be weaponized.
Taking a step back from the theoretical, AI has added stress to an already demanding workload due to its rapid evolution and the lack of support for staff. A lack of institutional support and increased workloads in higher education have led to burnout — the implications of which are reduced productivity, teaching quality, and research integrity. Reference librarians are trying to find hallucinated articles and books, create new lesson plans with AI literacy, and educate those who are uploading licensed and copyrighted materials into training data. We are navigating AI fallout while learning alongside our faculty and students.
Personal Reflection
While this post is written through the lens of an academic reference librarian, I was a public librarian when OpenAI introduced ChatGPT in 2022. My first workshop on generative AI was in 2023, and I had spent weeks preparing and creating an educational and practical session. At my current institution, I began by voluntarily joining teams and conducting surveys while noticing that AI education and integration trials had become a regular and expected part of my role. Through conversations with colleagues, I have learned that this is quite a common experience. Learning about AI is a huge undertaking and has begun to blur the line between adapting to patron needs and job creep. Ettarh explains that job creep is the slow, or sometimes not so slow, as we are seeing with AI, expansion of work requirements. What was previously considered outside of the job description has become the new normal. Learning, using, and teaching about generative AI is simultaneously a volunteer and expected endeavor.
In 2017, while discussing a collaborative digital library effort, Brewster Kahle, founder of the Internet Archive, wrote, “Our community has been fractured by disagreement about the path forward, with ongoing resistance to some approaches that strike many as monopolistic. Indeed, the library community seems to be holding out for a healthy system that engages authors, publishers, libraries, and most importantly, the readers and future readers.” I highlight this article and quote above because it is reminiscent of the conversations higher education is facing today about a way forward with generative AI.
In Kahle’s call for a digital collection, he mentions three main problems obstructing the initiative. Money, technology, and legal clarity. What he doesn’t mention is labor. A healthy system should not only engage different communities but also include a network of support, training, and compensation for the staff involved. I would argue that it should also include a rejection of criticism aimed at those who are unable to participate. What is common when vocational language is invoked is an erasure of labor. When thinking about creating a healthy ecosystem for AI, there needs to be structural support in place and a deeper understanding of the connection between job creep and updated performance plans.
It seems we are past the point of slowing down integration. What we need now isn’t another class on how to use CoPilot but an institutional reflection on the emotional and mental cost of quickly advancing these tools in higher education. AI in academia is making a perfect cocktail for burnout, and we are already fatigued.
Author’s note: During the editing stage of this draft, I learned of Fobazi Ettarh’s passing after a long battle with sickle cell anemia. She was a passionate educator and critical thinker whose research and advocacy will leave a lasting influence on me and the profession at large. She encouraged me to be a “bad librarian,” and for that, I will be forever grateful.
