AI (artificial intelligence) literacy

Teaching Resource

TRAIIL: Teaching Repository for AI-Infused Learning

A collection of openly licensed, peer-reviewed strategies that utilize generative AI tools to support learning in higher education. Includes the following categories:

Submitted by Shelby Hallman on March 26th, 2024
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Short Description: 

Algorithms are everywhere, and they have increasing power over what we consume (Amazon, Netflix, TikTok), who we date (“the apps”), and how we understand the world (Google, ChatGPT). So, what are algorithms, and how did they become so powerful? Who are the humans that create them, and why does it matter?

In this workshop, we will explore how algorithms can perpetuate bias and discrimination, and discuss some preventive strategies. It is open to learners of all backgrounds and experience.

Workshop Instructors: Shelby Hallman, Physical Sciences and Engineering Librarian; Ashley Peterson, Research & Instruction Librarian, Media and Data Literacy; Alexandra Solodkaya, Rothman Family Food Studies Librarian

Credits: This workshop was derived from LMU's Rise Against the Machines: Understanding Algorithmic Bias workshop. 

Attachments: 
AttachmentSize
UCLA_SRW_Fall23 Algorithmic Bias Workshop Slide Deck.pdfdisplayed 820 times3.75 MB
Algo_Bias_UCLA_Fall_23_Lesson Plan.pdfdisplayed 701 times83.65 KB
Relevant links: 
Breaking the Code: Fall 2023 Slide Deck
Learning Outcomes: 
  • Students will be introduced to algorithmic bias concepts, focusing on machine learning and AI.
  • Students will understand the causes and implications of bias within algorithm development and use. 
  • Students will discuss strategies to cope with or critically engage with algorithms.
Discipline: 
Computer ScienceLibrary and Information Science

Individual or Group:

Ability Level:

Tags:

Course Context (e.g. how it was implemented or integrated): 

This workshop was held virtually, via Zoom. 

Assessment or Criteria for Success
Assessment Short Description: 
Formative assessment was conducted via the in-session activities and participation. Summative assessment was conducted via an end of session survey form.
Suggested Citation: 
Hallman, Shelby. "Breaking the Code: Understanding Algorithmic Bias." CORA (Community of Online Research Assignments), 2024. https://projectcora.org/assignment/breaking-code-understanding-algorithmic-bias.
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All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License
Submitted by Sarah Hartman-Caverly on December 20th, 2023
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Short Description: 

The Hidden Layer Workshop introduces key generative AI (genAI) concepts through a privacy lens. Participants probe the possibilities and limitations of genAI while considering implications for intellectual privacy, intellectual property, data sovereignty, and human agency. In the centerpiece activity, participants engage in a hidden layer simulation to develop a conceptual understanding of the algorithms in the neural networks underlying LLMs and their implications for machine bias and AI hallucination. Drawing on Richards’s theory of intellectual privacy (2015) and the movement for data sovereignty, and introducing an original framework for the ethical evaluation of AI, Hidden Layer prepares participants to be critical users of genAI and synthetic media.

The workshop is designed for a 60-minute session, but can be extended to fill the time available.
Includes workshop guide, presentation slides, learning activities, and assessment instrument.

Attachments: 
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HiddenLayer_LessonPlan_CCBYSA_HartmanCaverly_2023.pdfdisplayed 891 times117.63 KB
Relevant links: 
[workshop guide] Hidden Layer: Intellectual Privacy and Generative AI[slides] Hidden Layer: Intellectual Privacy and Generative AI[activity] AI Playground[activity] Perplexity.ai[activity] ChatGPT[activity] Google Bard[activity] Microsoft Copilot[activity] Hidden Layer Simulation[activity] How AI Reduces the World to Stereotypes - Rest of World[assessment] Hidden Layer Workshop Reflection
Learning Outcomes: 

Facilitator learning objectives

During this workshop, participants will

  • Apply prompt engineering techniques to elicit information from text-to-text generative AI (genAI) platforms

  • Appreciate a range of intellectual privacy implications posed by genAI, including: 

    • personal data;

    • intellectual property (copyright, patent, proprietary and sensitive data); 

    • AI alignment (social bias, content moderation, AI guardrails, censorship, prompt injection); 

    • synthetic media;

    • AI hallucination and mis/dis/malinformation; and

    • data sovereignty and data colonialism.

  • Engage in a simulation to develop a conceptual understanding of how the hidden layer in the neural networks underpinning large language models works

  • Synthesize their knowledge of genAI intellectual privacy considerations to analyze an ethical case study using the Agent-Impact Matrix for Artificial Intelligence (AIM4AI).

Participant learning outcomes

During this workshop, participants will

  • Interact with genAI to explore its possibilities and limitations

  • Discuss the intellectual privacy implications of genAI, including intellectual property considerations

  • Evaluate the ethics of genAI for its impact on human agency

Discipline: 
Library and Information Science

Information Literacy concepts:

Individual or Group:

Ability Level:

Tags:

Suggested Citation: 
Hartman-Caverly, Sarah. "Hidden Layer: Intellectual Privacy and Generative AI." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/hidden-layer-intellectual-privacy-and-generative-ai.
Copyright icon to go along with copyright rules.
All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License
Submitted by Raymond Pun on February 12th, 2023
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Short Description: 

ChatGPT is an generative artificial intelligence chatbot released in November 2022 by OpenAI. What are the opportunities in using this tool to teach library instruction? This document highlights various ways to engage with learners in critically analyzing ChatGPT (version GPT-3) and its responses through the ACRL Frame: Information Creation as a Process. 

Attachments: 
AttachmentSize
Activity- Using ChatGPT For Library Instruction- Information Creation as a Process.pdfdisplayed 1792 times29.71 KB
Relevant links: 
ChatGPT
Learning Outcomes: 
  • Learn how to connect library research and instruction with ChatGPT
  • Critically analyze ChatGPT and its responses through dialogue and research
Discipline: 
Education

Information Literacy concepts:

Individual or Group:

Ability Level:

Tags:

Suggested Citation: 
Pun, Raymond. "Using ChatGPT For Library Instruction: Information Creation as a Process." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/using-chatgpt-library-instruction-information-creation-process.
Copyright icon to go along with copyright rules.
All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License
Submitted by Shelby Hallman on June 9th, 2022
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Short Description: 

Algorithms are not neutral but this does not mean they are not useful tools for research. In this workshop on algorithmic bias, student learn how algorithms can perpetuate bias and discrimination and how to critically evaluate their search results.

Attachments: 
AttachmentSize
Presentation_Rise Against the Machines_Algorithmic Bias Workshopdisplayed 1286 times3.65 MB
Lesson Plan_ Rise Against the Machines_ Understanding Algorithmic Bias Workshop.pdfdisplayed 879 times149.88 KB
Algorithm_Intervention_Spectrum_Activity.pdfdisplayed 628 times116.41 KB
Relevant links: 
Rise Against the Machines: Understanding Algorithmic Bias Workshop SlidesRise Against the Machines: Understanding Algorithmic Bias LibGuideActivity: Survival of the Best Fit GameSpring 2023 Workshop Slides
Learning Outcomes: 

•Students will be introduced to the machine bias inherent in algorithmic decision making, with a focus on information systems.

•Students will discuss the effects of algorithm bias in order to articulate how some individuals or groups of individuals may be misrepresented or systematically marginalized in search engine results.

•Students will develop an attitude of informed skepticism in order to critically evaluate search results. 

Discipline: 
Computer ScienceLibrary and Information Science

Information Literacy concepts:

Individual or Group:

Ability Level:

Tags:

Course Context (e.g. how it was implemented or integrated): 

Stand-alone workshop; co-curricular workshop. 

Assessment or Criteria for Success
Assessment Short Description: 
Formative assessment was conducted via the in-session activities. Summative assessment was conducted via an end of session survey form.
Collaborators: 
Susan Archambault
Elisa Acosta
Suggested Citation: 
Hallman, Shelby. "Rise Against the Machines: Understanding Algorithmic Bias." CORA (Community of Online Research Assignments), 2022. https://projectcora.org/assignment/rise-against-machines-understanding-algorithmic-bias.
Copyright icon to go along with copyright rules.
All assignments contributed to this site are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License