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AI Literacy Toolkit

The AI literacy toolkit provides librarians, faculty, and other academic instructors to address AI literacy topics in their courses. Within are sample lesson plans, activities, presentation slides, and worksheets that can be adapted for specific instructi

AI Competencies for Students

  1. Understanding AI in the Information Ecosystem 

AI Competency  

Mapped ACRL Frame(s) 

Rationale 

Explain what artificial intelligence is and how it shapes the creation and organization of information. 

Information Creation as a Process 

Helps students understand AI-generated content (e.g., text, images, summaries) as a new form of information production with unique affordances and limitations. 

Recognize the types of tasks AI tools are designed for and how they influence access to and interpretation of information. 

Information Has Value 

Encourages awareness of AI’s role in reshaping access, ownership, and labor in information systems. 

 

  1. Critical Use of AI Tools in Research and Learning 

AI Competency  

Mapped ACRL Frame(s) 

Rationale 

Use generative AI tools to brainstorm, draft, and revise—while evaluating the reliability, originality, and limitations of AI-generated content. 

Research as Inquiry  
Information Creation as a Process 

Promotes AI as a tool in iterative research and writing while emphasizing human judgment and revision. 

Develop effective prompts to improve the quality and relevance of AI responses. 

Research as Inquiry 

Encourages students to treat prompt engineering like formulating good research questions. 

Distinguish between appropriate and inappropriate uses of AI in academic work. 

Information Has Value  
Authority Is Constructed and Contextual 

Reinforces academic integrity and disciplinary norms. 

  1. Evaluating AI-Generated & AI-Mediated Information 

AI Competency 

Mapped ACRL Frame(s) 

Rationale 

Critically assess AI-generated content for bias, misinformation, or fabricated sources. 

Authority Is Constructed and Contextual  
Information Has Value 

Encourages students to question how authority and truth are constructed in algorithmically generated content. 

Identify when and how AI systems might reinforce or challenge existing power structures. 

Authority Is Constructed and Contextual 

Engages students in considering the social and political implications of information technologies. 

  1. Ethical & Inclusive Use of AI 

AI Competency 

Mapped ACRL Frame(s) 

Rationale 

Reflect on the ethical implications of using AI in academic and personal contexts, including issues of privacy, bias, and labor. 

Information Has Value  
Scholarship as Conversation 

Frames AI as part of broader discussions about justice, equity, and power in knowledge creation. 

Select AI tools that support accessibility and inclusive practices. 

 

Information Has Value 

 

Encourages intentional tool use aligned with universal design and social responsibility. 

 

  1. Participating in the Conversation about AI 

AI Competency 

Mapped ACRL Frame(s) 

Rationale 

Contribute to informed conversations about the role of AI in academic, professional, and everyday contexts. 

Scholarship as Conversation 

Positions students as participants in evolving societal discussions about AI’s impacts. 

Seek out diverse expert perspectives on AI-related issues and evaluate them for credibility. 

Authority Is Constructed and Contextual  
Research as Inquiry 

Develops habits of questioning and cross-checking across varied sources of authority. 

Core Competencies

The following list outlines the core competencies that can be used to gauge learners' understanding of key AI literacy concepts.

  1. Basic AI Literacy - establish a baseline understanding of AI concepts, laying groundwork for future discussion of generative AI.
    • Individuals will be able to recognize different types of AI. This will enable them to evaluate the potential benefits and risks, understanding how AI can impact them or their business processes.
  2. Knowledge of GenAI Tools - provides a peripheral understanding of the workings of generative AI.
    • Despite the extensive media coverage, ChatGPT lacks widespread recognition among diverse demographics, particularly among certain age groups and professions. Even among those who have heard of it, usage remains limited.
  3. Knowledge of the Capacity and Limitations of GenAI Tools - equips individuals with the proficiency to assess the capabilities and constraints of genAI tools.
    • The output of ChatGPT and other LLMs can often contain a mixture of facts and completely false and fabricated statements, and awareness of this is fundamental.
  4. Skill to Use GenAI Tools - promotes practical proficiency to effectively leverage generative AI tools in diverse contexts.
    • A user who needs to generate images should start to learn and practice several tools for this, such as Midjourney, Adobe Firely (available in Photoshop), DreamStudio, and others.
  5. Ability to Detect AI-generated Content - teaches the skill of discerning AI-generated content.
    • When seeing a video that looks questionable, people should be able to verify, through AI detectors and official sources, whether the video is authentic or fabricated. A recent example from 2023 is the case of a viral AI-generated video of the Pentagon on fire, which created some panic.
  6. Ability to Assess the Output of Generative AI Tools - provides the ability to critically assess output quality, relevance, and potential biases.
    • Individuals using LLMs to write essays on specific topics should check the facts presented in the output, to verify that they are not hallucinations of the model, and present these facts in a way that corresponds to their needs.
  7. Skill in Prompting Generative AI Tools (Prompt Engineering) - nurtures the creative aspect of working with generative AI, allowing individuals to tailor personalized outputs to specific objectives or creative efforts.
    • In the context of image generation, the utilization of descriptive language, understanding the trade-offs between creativity and specificity, the possibility of segmenting longer prompts into smaller units, or the incorporation of negative words, can all lead to outputs that are different from the conventional results.
  8. Ability to Program and Fine-tune - Provides the technical know-how necessary for the customization and optimization of generative AI models to suit specific needs.
    • This ability is valuable for developing generative AI in specialized domains such as medical writing or dealing with languages other than English. Developers can adapt these models to equip them with a deeper understanding of domain-specific vocabulary and intricacies, leading to more impactful and tailored content generation or for particular linguistic nuances, cultural references, and colloquialisms.
  9. Knowledge of the Contexts Where Generative AI is Used - Understand the diverse applications and limitations of generative AI across situations, institutions, and professions to assess the appropriateness of using generative AI tools.
    • Several universities have developed guidelines for the use of LLMs in assignments [26]. This differs from campus to campus, with some of them prohibiting LLMs altogether, and others allowing them for certain types of coursework. Students should learn these guidelines, and behave accordingly.
  10. Knowledge of the Ethical Implications - Augments technical proficiency with ethical considerations and inculcates a sense of responsibility by making individuals aware of the ethical considerations tied to the use of generative AI.
  11. Knowledge of Legal Aspects - Addresses legal dimensions, ensuring individuals operate within the bounds of intellectual property and other legal frameworks associated with the use of generative AI.
  12. Ability to Continuously Learn - Promotes a mindset of continuous learning to stay updated with evolving generative AI technologies, methodologies, and ethical considerations.
Competencies previously defined by:
Annapureddy, R., Fornaroli, A., & Gatica-Perez, D. (2025). Generative AI Literacy: Twelve Defining Competencies. Digit. Gov.: Res. Pract., 6(1). https://doi.org/10.1145/3685680