AI literacy

Submitted by Daniel Nichiata on July 28th, 2026
Short Description: 

A 5-minute, method-first activity: students verify three references from their own draft bibliography before submitting - resolving DOIs and searching exact titles - then classify each as found-and-matches, found-but-differs, or not-found, and correct entries from the real record. Motivated by the rise of fabricated ("hallucinated") citations that look perfectly formatted yet point at nothing. Teaching point: "not found" does not automatically mean "fabricated".

Learning Outcomes: 

By the end of this activity, students will be able to: (1) verify a reference by resolving its DOI at doi.org or searching the exact title in a scholarly database; (2) classify each reference as found-and-matches, found-but-differs, or not-found; (3) correct bibliographic entries using the real source record; (4) explain why a confidently formatted reference that resolves to nothing anywhere carries the signature of a machine-invented citation, while "not found" alone does not prove fabrication.

Individual or Group:

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

Designed as a 5-minute opener for any course whose assignments include references: information literacy one-shots, first-year writing, research methods, or graduate seminars. Best run shortly before a submission deadline, when students have a draft bibliography in hand. No preparation needed; works in person or online. Materials: the student's own draft bibliography and a browser.

Additional Instructor Resources (e.g. in-class activities, worksheets, scaffolding applications, supplemental modules, further readings, etc.): 

Student-facing script: (1) Pick three references from your bibliography: one from a source you actually read, one that was suggested or produced by any writing tool, and the one you are least sure about. (2) Try to reach each work. If it has a DOI, paste it at https://doi.org. If not, search the exact title in quotes in Google Scholar or your library's discovery search. (3) Classify each reference: Found-and-matches (authors, year, and venue agree with your entry); Found-but-differs (a real work exists, yet your metadata disagrees); Not-found (the DOI resolves to nothing and no database returns the title). (4) Act on it: for found-but-differs, correct your entry from the real record; for not-found, try one more route (a second database, the journal's own site) before drawing any conclusion.

Teaching point: "not found" does not automatically mean "fabricated" - older books, regional journals, and grey literature are genuinely under-indexed. But a reference that is confidently formatted, carries a plausible identifier, and resolves to nothing anywhere is the classic signature of a machine-invented citation - treat it as unusable until the real source is located.

Optional discussion statistic: in one recent 30-day window, 47,098 references were run through a public reference checker and 27.2% triggered a fabrication signature - a conservative flag rate, not a validated prevalence estimate. See the relevant links for the write-up and the open CC BY 4.0 dataset.

This activity is adapted from the CC BY 4.0 classroom resource by CiteMe (citeme.app). Adapt freely with attribution. A Portuguese (PT-BR) version is available from the source.

Potential Pitfalls and Teaching Tips: 
Suggested Citation: 
Nichiata, Daniel. "The 5-Minute Reference Check: Verify Citations Before Submission." CORA (Community of Online Research Assignments), 2026. https://projectcora.org/assignment/5-minute-reference-check-verify-citations-submission.
Submitted by Katie Perry on June 8th, 2026
Short Description: 

Practical strategies for teaching AI-assisted lit reviews while keeping human analysis and critical thinking central.

Attachments: 
AttachmentSize
Copy of AI Might Summarize, But It Can’t Think_ Teaching Toolkit.docxdisplayed 163 times12.9 KB
Learning Outcomes: 

By the end of this session, students will be able to:

  1. 1. Critically evaluate AI-generated research summaries by identifying strengths, limitations, missing perspectives, and potential inaccuracies.
  2. 2. Assess the credibility and authority of information by verifying sources, citations, and claims presented in AI-generated outputs.
  3. 3. Develop and refine research strategies by using AI tools to generate keywords and then applying those terms in scholarly database searches.
  4. 4. Use citation tracking techniques (backward and forward searching) to identify influential sources and trace scholarly conversations within a research area.
  5. 5. Compare AI-generated research overviews with database search results to identify gaps, omissions, and differences in coverage.
  6. 6. Analyze scholarly literature for themes, patterns, and gaps using close reading techniques.
  7. 7. Synthesize information from multiple sources to support explanatory or argumentative literature reviews.
  8. 8. Apply lateral reading strategies to evaluate the credibility, relevance, and accuracy of AI-generated information.
  9. 9. Select and use AI research tools appropriately based on their strengths, limitations, and intended purposes.
  10. 10. Describe the role of AI as a research support tool rather than a substitute for scholarly inquiry and critical analysis.

Individual or Group:

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

There are several activities in the document - they don't have to be used all together! Pick and choose activities to get students to think critically about LLM outputs. 

Suggested Citation: 
Perry, Katie. "AI Might Summarize, but it Can't Think: AI-assisted lit reviews." CORA (Community of Online Research Assignments), 2026. https://projectcora.org/assignment/ai-might-summarize-it-cant-think-ai-assisted-lit-reviews.

Teaching Resource

Over 60 practical assignment prompts and ideas across disciplines to assist with teaching skills for using ChatGPT (and other AI tools), including prompt engineering, evaluating output, analyzing texts, writing, generating content, studying, and c

Teaching Resource

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
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 1689 times3.75 MB
Algo_Bias_UCLA_Fall_23_Lesson Plan.pdfdisplayed 1392 times83.65 KB
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.

Individual or Group:

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.
Submitted by Sarah Hartman-Caverly on December 20th, 2023
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: 
AttachmentSize
HiddenLayer_LessonPlan_CCBYSA_HartmanCaverly_2023.pdfdisplayed 1970 times117.63 KB
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

Individual or Group:

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.
Submitted by Raymond Pun on February 12th, 2023
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 2569 times29.71 KB
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:

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.
Submitted by Shelby Hallman on June 9th, 2022
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.

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. 

Individual or Group:

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.
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.