AI Literacy

Understand AI's capabilities and limitations to become a more effective, responsible, and critical AI user

Explore Established Frameworks Apply in Practice

AI Literacy as Information Literacy Extension

AI literacy builds upon established information literacy principles that UM students and faculty already know. Rather than replacing traditional information literacy skills, AI literacy extends and contextualizes them for the AI era.

UM's Information Literacy Foundation

The University of Montana has a well-established information literacy curriculum developed by Mansfield Library faculty. This curriculum provides the critical thinking foundation that AI literacy extends:

Source evaluation and verification
Understanding bias and perspective
Ethical use of information
Critical analysis of claims
Understanding information systems
Academic integrity principles

Building on Scholarship & Expertise

This guidance integrates established AI literacy frameworks from UNESCO, Barnard College, and other institutions with UM's existing information literacy curriculum developed by Mansfield Library faculty expertise.

Learn More: Explore UM's complete information literacy curriculum and resources at the Mansfield Library AI Research Guide.

Established AI Literacy Frameworks

Here are some established frameworks you can follow for developing AI literacy skills.

UNESCO AI Competency Framework for Teachers

UNESCO's globally recognized framework defines the knowledge, skills, and values educators need to integrate AI effectively and ethically. It emphasizes a human-centered approach that protects teacher agency and promotes inclusive, equitable education.

Five Core Dimensions

Human-Centered Mindset: Focusing on human agency and social responsibility
AI Ethics: Understanding ethical implications and responsible use
AI Foundations: Technical understanding of AI systems and applications
AI Pedagogy: Integrating AI tools into teaching and learning
Professional Learning: Using AI for continuous professional development

Three Progression Levels

Acquire: Basic understanding and initial skills
Deepen: Enhanced competency and critical application
Create: Innovation and advanced implementation

UNESCO Principles

Protecting teacher rights and agency
Enhancing human capabilities
Promoting sustainability and equity
Ensuring transparency and accountability

Source: UNESCO AI Competency Framework for Teachers (2024). View Full Framework

Barnard College AI Literacy Framework

Developed by Barnard's academic technology team and published in EDUCAUSE Review, this framework adapts Bloom's Taxonomy for AI literacy education in higher education contexts.

Four-Level Pyramid Structure

1. Understand AI: Basic terms, concepts, and capabilities
2. Use & Apply AI: Practical application and prompt refinement
3. Analyze & Evaluate AI: Critical assessment of outputs and limitations
4. Create AI: Building and customizing AI solutions

Key Framework Principles

Scaffolds learning from basic awareness to advanced application
Maintains technology neutrality - literacy includes choosing not to use AI
Meets people where they are in their AI knowledge journey
Emphasizes critical thinking over technical implementation

Academic Context Focus

Specifically designed for higher education institutions, emphasizing academic integrity, critical evaluation, and pedagogical considerations.

Source: Hibbert, M., Altman, E., Shippen, T., & Wright, M. (2024). A Framework for AI Literacy. EDUCAUSE Review. View Article

Essential AI Literacy Competencies

Drawing from established frameworks, these are the core competencies for responsible AI use in academic settings.

Understanding AI Systems
Foundational knowledge of how AI works, its capabilities and limitations, and the difference between various types of AI systems.
- Understanding machine learning vs. generative AI
- Recognizing AI capabilities and limitations
- Understanding training data and model biases
Critical Evaluation
Applying information literacy skills to assess AI outputs, identify biases, verify information, and understand the implications of AI-generated content.
- Fact-checking AI-generated information
- Identifying potential biases in outputs
- Recognizing hallucinations and fabricated content
Ethical & Responsible Use
Understanding the ethical implications of AI use, respecting privacy and intellectual property, and maintaining academic integrity in AI-assisted work.
- Maintaining academic integrity standards
- Respecting privacy and data protection
- Understanding intellectual property implications

Applying AI Literacy in Academic Work

See how established AI literacy principles guide responsible AI use in real academic scenarios.

Research & Writing

Information Literacy Approach:

Verify AI-suggested sources independently
Cross-reference claims with authoritative sources
Understand the provenance of AI training data
Apply source evaluation criteria to AI outputs

Ethical Considerations:

Acknowledge AI assistance transparently
Maintain original thinking and analysis
Respect copyright and fair use principles
Follow institutional AI policies

Data Analysis & Interpretation

Critical Evaluation:

Question AI interpretations of data patterns
Validate statistical claims independently
Understand potential biases in AI analysis
Apply domain expertise to AI suggestions

Human-Centered Approach:

Use AI as a tool to enhance, not replace, analysis
Maintain human oversight of conclusions
Consider multiple perspectives and interpretations
Document AI assistance in methodology

Questions for Responsible AI Use

Before Using AI:

Is AI appropriate for this task?
What are the privacy implications?
How will I verify the results?
Does this align with course/institutional policies?

After Getting AI Results:

How can I independently verify this information?
What perspectives or voices might be missing?
What can I add from my own expertise?
How should I acknowledge AI assistance?

Continue Building Your AI Literacy

AI literacy is an ongoing journey. These established resources will help you continue developing critical thinking skills for responsible AI use.

Grounded in Scholarship

Effective AI literacy education builds upon established information literacy principles and draws from evidence-based frameworks developed by experts in information sciences, educational technology, and AI ethics. This approach ensures responsible, critical, and effective AI use that serves educational goals and human flourishing.