Developing AI literacy at your institution

Librarians are indispensable in shaping a future where AI-generated information is not only abundant but also credible, reliable, and accessible to all.?

?AI literacy concepts can be taught in many of the traditional literacy avenues. Remember, you don’t need to be a computer expert to create or attend AI literacy workshops. The crux of AI literacy lies not in technical expertise but in fostering critical thinking.?

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What is AI literacy??

• Knowing, understanding, using, and evaluating AI, as well as considering the ethical issues (Ng, et al, 2021)?
• Understanding fundamental AI concepts like machine learning, natural language processing, and neural networks?
• Capability to critically evaluate AI technologies?
• Discover more: Key components of developing AI literacy at your institution

AI Terminology:

• Machine Learning (ML): Enables computers to learn from and make decisions based on various forms of data including images and text.?
• Language Models: Algorithms for understanding human language. Can enhance library information retrieval, text summarization, and support user assistance chatbots.?
• Generative AI: Algorithms, such as ChatGPT, trained on a model of language (text), audio or visual data that creates new text, image, or audio outputs, based on user instruction (prompts).

Evaluating AI with the ROBOT test?

Developed by two librarians at McGill University, Amanda Wheatley and Sandy Hervieux to offer a structured framework for individuals new to AI, the ROBOT test evaluates new information related to AI technology.?

ROBOT, an acronym for reliability, objective, bias, ownership, and type, which represent key criteria for assessing information about AI tools (Wheatley and Hervieux, 2020).?

A complete outline of the ROBOT test and the questions it prompts the user to ask can be found on the McGill Library AI Literacy website.??

Explaining AI

Explainability is another method of AI evaluation when implementing a new AI tool. Balasubramaniam and colleagues (2023) proposed a framework outlining the key components of explainability: ?

• Addresses (Ask yourself to whom the explainer tries to explain?)????
• Aspects (What to explain?)??
• Context (What are the contextual situations requiring explanation?)??
• Explainer (Who explains?)?

These can be used and shared with students and faculty to help understand the tools they use.

Getting started at your library

Dr. Borui Zhang, nicknamed the AI librarian, offers the following ideas for how libraries can begin incorporating AI technology in their services:???

• Learn and assess the AI-related enhancements already appearing in your existing research applications. "I see a lot of liaison librarians helping students and faculty with literature searches, and you can see those search databases have new AI features."??
• Investigate applying available tools to current tasks. For other types of academic library research support, or even the traditional liaison role, they can begin to adopt AI into their existing domain. “The power of AI can very likely be suitable in all these areas."??
• Introduce students and researchers early by including discussion of AI research techniques in library orientation programs.
• Learn more about the role of AI in library services

References:?

Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K., & Kujala, S. (2023). Transparency and explainability of AI systems: From ethical guidelines to requirements.?Information and Software Technology,?159, 107197.?

?Hervieux, S. & Wheatley, A. (2020). The ROBOT test [Evaluation tool]. The LibrAIry. https://thelibrairy.wordpress.com/2020/03/11/the-robot-test?

Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues.?Proceedings of the Association for Information Science and Technology,?58(1), 504-509. https://doi.org/10.1002/pra2.487?