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Artificial Intelligence

This guide is dedicated to the concept of Artificial Intelligence: what it is, how it works, debates and discussion, and potential uses in the disciplines.

What is Artificial Intelligence?

AI generated image of human head.

[AI generated image]

Oxford Reference defines artificial intelligence as "The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages."

Artificial Intelligence (AI) alternately may refer to a subject of study, a scientific discipline, a tool, or competition for human endeavors.

 AI encompasses a number of areas: communications, learning, reasoning, understanding, vision, and robotics.

Drawbacks of AI currently include a lack of explainability (behaving as a black‐box model) and a number of ethical issues.


  • AI prompt engineer: Intermediary that writes text-based prompts to optimize desired outputs for users.
  • Artificial Intelligence: Attempts to reproduce human behavior / learning. The term "artificial intelligence" is often used to refer to subsets of AI. Developed in the 1950s.
  • ChatGPT: A Generative Pre-training Transformer that uses natural language and advanced deep learning to make predictions
    • ChatGPT is a type of software--an AI chat bot. Latest version 4.0 is able to connect to the Internet (although this poses security and privacy threats). It can be downloaded as a mobile app for iOS and Android devices. Version 4.0 is accessible only to paid subscribers.
    • The version 3.0 is free, has an information cutoff (last update) of September 2021, and does not connect to the Internet.
  • CLEAR Framework for prompt engineering (Leo Lo: 
    • Concise
    • Logical
    • Explicit
    • Adaptive
    • Reflective
  • Deep learning: Avoids the need for human operators using comparatively larger datasets and a hierarchy of concepts built from simpler concepts.
  • Generative AI: Sophisticated text prediction inspired by human neural networks; uses a subset of machine learning with datasets to generate new content.
  • Hallucinations: Refers to erroneous ("fake") AI-generated results.
  • Large Language Model: A model that uses massive amounts of training data to teach algorithms without human instruction.
  • Machine learning: A subset of AI used to teach computers to learn and act without programming that uses comparatively smaller datasets.

The AI Family Tree

Graphic of AI Family Tree that branches out from Data Science, Artificial Intelligence, and Machine Learning.


Wheatley, A & Hervieux, S. (2020). The AI family tree [diagram]. The LibrAIry.


References Used to Create this Guide

Choice-ACRL webinar. (2023, September 28). How artificial intelligence drives scientific research. Springer Nature.

Flierl, M. (2023, August 15). AI, higher education, and medical librarianship: Opportunities and risks. NNLM Region 6 Spotlight Speaker webinar.

JoVE Webinar. (2023, September 20). ChatGPT & libraries: Applications and implications for teaching and learning.

Lo, L.S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4), 102720.

McGraw-Hill Masterclass Webinar. (2023, September 19). Artificial Intelligence in Medicine.

Oxford University Press. (n.d.). Artificial intelligence. In Oxford Reference. Retrieved 17 Nov. 2023, from

Webinar sponsored by State University of New York at Albany. (2023, November 29). The role of Generative AI in LIS publishing: Opportunities and challenges.

Wheatley, A. & Hervieux, S. (2023, October 11). ChatGPT and Generative AI: Benefits, concerns, and opportunities for health science libraries. Medical Library Association, Midwest Chapter Workshop.