When It's Just Literacy: Intersections of Information, Data, and AI Literacy
Type of Presentation
Individual paper/presentation
Conference Strand
Ethics in Information
Target Audience
Higher Education
Second Target Audience
Higher Education
Relevance
With the rise of open, generative artificial intelligence, there have been corresponding cries for increased and deliberate AI literacy efforts that enable educators, students, and librarians to understand how AI works, how to evaluate it and how to best adapt it for their disciplines and learners. This presentation directly addresses this concern by discussing how existing literacy education efforts related to information and data literacy underpin all forthcoming AI literacy educational practice.
Proposal
As artificial intelligence (AI) has come to prominence , the conversation has naturally turned to AI literacy and the overall need to educate individuals on the conceptual understanding, use, ethics, and critical perspectives on AI. Missing from these emerging conversations is an acknowledgement that information is data and data is information. In the context of AI, particularly when looking at those trained on large language models, this is foundational to understanding the next normal of literacy, which is not based only on the ability to read, write, or do math, but understand complex amounts, types, and contexts of information.
There are currently attempts to combat AI bias and algorithms, such as Human-in the-loop, but it is extraordinary labor intensive and is not always able to address concerns around bias, specifically, there are no markers to support the people doing this work both in practice, but more specifically in understanding their level of literacy in data and AI in a library context. Our goal with this presentation is to make a clear connection between Information literacy and data literacy, and begin to discuss how this may be a new foundation for AI literacy. This presentation will touch on how the ACRL Framework (2016), can inform conversations around AI literacy, as represented through the six guiding frames. Fundamentally academic libraries need to begin to adapt information literacy to be inclusive of data so as to move into the next phase of information generation. This presentation will include some suggestions on how librarians can adapt current information literacy approaches to teach AI literacy, and ideas for navigating this in a professional context.
Short Description
Information is changing - increasingly a single tool can create, revise, and disseminate information. How does information still hold value that helps individuals negotiate and understand the world when mediated through an algorithm, and how, as librarians do we navigate this in the context of data and information literacy.
Keywords
AI, Data Literacy, Information Literacy, STEM
Publication Type and Release Option
Presentation (Open Access)
Recommended Citation
Mercer, Kate Dr. and Weaver, Kari D., "When It's Just Literacy: Intersections of Information, Data, and AI Literacy" (2024). Georgia International Conference on Information Literacy. 26.
https://digitalcommons.georgiasouthern.edu/gaintlit/2024/2024/26
When It's Just Literacy: Intersections of Information, Data, and AI Literacy
As artificial intelligence (AI) has come to prominence , the conversation has naturally turned to AI literacy and the overall need to educate individuals on the conceptual understanding, use, ethics, and critical perspectives on AI. Missing from these emerging conversations is an acknowledgement that information is data and data is information. In the context of AI, particularly when looking at those trained on large language models, this is foundational to understanding the next normal of literacy, which is not based only on the ability to read, write, or do math, but understand complex amounts, types, and contexts of information.
There are currently attempts to combat AI bias and algorithms, such as Human-in the-loop, but it is extraordinary labor intensive and is not always able to address concerns around bias, specifically, there are no markers to support the people doing this work both in practice, but more specifically in understanding their level of literacy in data and AI in a library context. Our goal with this presentation is to make a clear connection between Information literacy and data literacy, and begin to discuss how this may be a new foundation for AI literacy. This presentation will touch on how the ACRL Framework (2016), can inform conversations around AI literacy, as represented through the six guiding frames. Fundamentally academic libraries need to begin to adapt information literacy to be inclusive of data so as to move into the next phase of information generation. This presentation will include some suggestions on how librarians can adapt current information literacy approaches to teach AI literacy, and ideas for navigating this in a professional context.