Generative AI & the Evolution of Academic Librarianship

During my first week as an academic librarian, many faculty discussions on campus were regarding the issue of generative AI software, such as ChatGPT. A majority of the faculty at a panel discussion held on campus about AI expressed concerns over plagiarism, copyright, academic integrity, etc. Those on the panel, however, commented on how beneficial using AI was. When asked more specifically on what faculty should do to combat potential cheating from using generative AI, the panel seemed in agreeance on an answer: educate your students on how to responsibly use AI.

I will admit; prior to starting my career as an academic librarian, I had never used generative AI. Of course, I saw generative AI blasted all over the news and saw updates on sites and apps like Snapchat, but I never understood what generative AI was. I did not have any interest in learning about it either. After attending the panel discussion, however, I was reminded of a book I read called Who Moved My Cheese? by Dr. Spencer Johnson. I was assigned to read Who Moved My Cheese? by a professor in graduate school and often refer back to it (I highly recommend reading it if you have not already done so). The book explains how change can happen unexpectedly, and when it does, it is better to adapt and move forward than be left behind. Feeling like I was being left behind while other faculty embraced generative AI, I decided to learn as much as I could about it.

Although I read numerous articles and watched hours of YouTube videos, I was still confused as to how generative AI worked. Near the end of August, my dean notified the library faculty of a course offered through ALA’s eLearning platform. The course was titled Exploring AI with Critical Information Literacy and taught by Sarah Morris. I enrolled in the course and learned about the development and usage of generative AI and machine learning, current discussions around AI, opportunities and challenges for AI usage in higher education, and how to engage AI as an academic librarian. Throughout the course, we examined AI through a critical lens and discussed strategies for AI to be incorporated at our own institutions. I enjoyed the course and found the lesson on prompt engineering to be the most intriguing.

One of the ways in which academic librarians can enter the generative AI realm in higher education is through teaching faculty and students prompt engineering. Prompt engineering is strategizing your generative AI input to obtain your desired output. While one can simply ask ChatGPT a standard question, prompt engineering recommends telling ChatGPT through what lens to answer the question. For example, if I was wondering how to craft a lesson for my class on implicit bias, I could plainly input:

“What lesson on implicit bias could I give my college class?”

Using prompt engineering, a better input would be:

“Act like an Academic Librarian teaching a college course on critical thinking. Design a lesson about implicit bias. Include topics for the class to discuss in small groups.”

While the results appeared similar, the detailed prompt elicited a result more applicable to my course by covering topics such as bias in information sources and media literacy.

Another way academic librarians can educate faculty and students on generative AI is on responsible use. More specifically, we can create lessons and workshops around copyright, academic integrity, and the reliability of the output. I tried this with my critical thinking class. I first introduced the university’s academic integrity policy, including definitions of cheating and plagiarism. Because the majority of my class was unfamiliar with generative AI, I briefly explained how generative AI worked. Afterwards, I had the students discuss the potential benefits and challenges of using generative AI. Using my personal account (my university does not support the use of ChatGPT), I asked ChatGPT and had the students read the output. I stressed that when used responsibly, ChatGPT can be a great resource for brainstorming; however, I cautioned my students from using it for writing assignments due to plagiarism, copyright infringement, and incorrect information. To illustrate this point further, I informed my students of the two attorneys in New York who acquired case law through ChatGPT. The attorneys did not fact-check the case law, and the judge discovered that the case law actually did not exist. The cases ChatGPT cited were made up. Overall, the lesson was a success. Many students chose to explore generative AI in more depth for the final projects.

By embracing generative AI, academic librarians can increase their skillset and become a useful resource for faculty and students navigating the rapidly evolving world of AI. It will be interesting to learn about how varying universities respond, if they have not done so already. I imagine we will see new policies implemented on campus, positions established, and roles altered.

2 thoughts on “Generative AI & the Evolution of Academic Librarianship”

  1. A better question to ask ChatGPT concerning “implicit bias” might be: “List top 20 critiques of implicit bias research.”

    “How did we reach this place where public understanding of what the race IAT reveals
    about racism and how to combat it diverges so greatly from that of the scientifically well-
    informed? And where do we go from here?”
    https://twitter.com/PTetlock/status/1735309300355010623

    “the evidence available to date doesn’t even come close to proving that most of us walk around with unacknowledged and unconscious biases in our heads”
    https://direct.mit.edu/books/oa-monograph/5625/chapter/4283491/Rethinking-Unconscious-Bias

    “12 Reasons to Be Skeptical of Common Claims About Implicit Bias”
    https://www.psychologytoday.com/us/blog/rabble-rouser/202203/12-reasons-be-skeptical-common-claims-about-implicit-bias

    “A brief intellectual history of implicit bias”
    https://persistentastonishment.blogspot.com/2023/01/a-brief-intellectual-history-of.html

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