Presented at LILAC 2024.

1. Bongiovanni, E., Beltran, L., Griego, C., Herckis, L.,
Kawaratani, L., Lan, H., McKee, J., Werlinich, A.
Carnegie Mellon University, Pittsburgh, USA
Instrument: Survey included 19 nominal, categorical, ordinal and open-ended items delivered a Google form, which also collected data
Procedure: Survey was distributed via diverse academic librarianship-related email lists, including including the ACRL Framework for
Information Literacy for Higher Education (ACRLFrame), AI for Libraries, Archives, and Museums (AI4LAM), Code4Lib, among other
subject-area specific lists, such as BUSLIB (Business Librarians), American Society for Engineering Education’s Engineering Libraries
Division (ASEE), and Theater Library Association (TLA) from 9/17/2023 through 10/20/2023
Analysis:
● Descriptive statistics were used to characterize demographic and instruction report information.
● Four Likert scale questions were analyzed by calculating median, mode, and standard deviation of responses. Correlations were identified
between these responses and demographic factors such as career stage using Pearsons Correlation Coefficient.
● Five “Check all that apply” items included an “Other” opportunity for open-ended responses. Open-ended responses were qualitatively
analyzed using a grounded theory approach and then incorporated into the categorical data deriving from these survey items. These were
analyzed for frequency, mean, median, and mode.
Abstract
Academic libraries play a pivotal role in information and digital
literacy education and are well-positioned to integrate Artificial
Intelligence (AI) literacy instruction across higher education. The
scholarly conversation on the role of academic libraries in AI
literacy instruction is new and evolving rapidly, however literature
in this area remains limited.
This study aims to form an understanding of the current status of
AI literacy integration in information literacy instruction across
academic libraries in the United States. A survey was designed to
measure academic librarians’ backgrounds, experiences,
approaches, and attitudes related to integrating AI literacy into
library instruction sessions. Responses present views and practices
of academic librarians across the United States related to
integrating AI literacy into library information literacy instruction.
Survey Respondents
0 5 10 15 20 25 30 35 40 45
Authority Is Constructed and Contextual
Information Creation as a Process
Information Has Value
Research as Inquiry
Scholarship as Conversation
Searching as Strategic Exploration
Not applicable or unsure
"If teaching AI literacy concepts, to which of the
ACRL frames align with those lessons?"
Project OSF site:
https://osf.io/p5acu/
Topics in AI Library Instruction
Limitations Evaluating AI-generated text
Ethics Attribution and citing AI tool use
Plagiarism Efficacy of results across languages
Code generation Problem solving (eg homework)
Validating citations Scholarly communication
IP and copyright Prompt engineering
Discovery and ideation First draft composition
Respondents’ institution affiliation type:
● 69% doctoral universities (N=49)
● 17% masters colleges and universities (N=12)
● 14% other institution types, including baccalaureate colleges
and community colleges (N=10)
Self-identified career status among respondents:
● 55% of respondents indicated they are mid-career
● 31% indicated they are early career
● 14% indicated they are late career
Responses demonstrated a moderate level of experience teaching
AI literacy within library instruction, with a mean of 2.46 on a
scale of 1-5 and a standard deviation of 1.18.
Responses demonstrated a moderate comfort teaching AI
literacy within library instruction, with a mean of 2.87 on a scale
of 1-5 and a standard deviation of 1.23.
Methods
Taking Stock: A Scan of AI Literacy Instruction in Academic Libraries
Survey Results
“I'm figuring it out as we go
mainly, but I'm trying to be the
voice of reason here. So many
just want to ban it but I feel that
is impossible and unethical.”