AI Literacy
Understand AI's capabilities and limitations to become a more effective, responsible, and critical AI user
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.
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
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.
Mansfield Library AI Guide
Comprehensive AI research and ethics resources developed by UM library faculty
UM Information Literacy Curriculum
Foundation information literacy resources and frameworks
UNESCO AI Framework
Global competency framework for teachers
Barnard AI Literacy Framework
Research-based framework for higher education
UM Teaching with AI
Faculty resources for AI integration
Get Expert Consultation
Connect with UM experts for AI literacy guidance
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.