AI Literacy
Understand AI's capabilities and limitations to become a more effective, responsible, and critical AI user
Why AI Literacy Matters
AI literacy isn't about becoming a technical expert—it's about developing the knowledge and skills to use AI thoughtfully, recognize its limitations, and maintain human agency in an AI-enhanced world.
Make Better Decisions
Understanding AI helps you know when to trust its outputs, when to verify information, and how to combine AI insights with human judgment effectively.
Use AI More Effectively
Knowledge of AI's strengths and weaknesses helps you craft better prompts, choose appropriate tools, and get more valuable results from AI systems.
Promote Equity & Fairness
Recognizing AI bias and limitations helps you advocate for fairer systems and ensure diverse perspectives are considered in AI-assisted work.
Three Essential Concepts
Master these fundamental concepts to become a more informed and effective AI user.
Understanding AI Hallucinations
AI "hallucinations" occur when models generate information that sounds plausible but is factually incorrect, fabricated, or misleading. Unlike human mistakes, AI hallucinations are often presented with complete confidence.
Common Types of Hallucinations
- •Citations to papers that don't exist but sound plausible
- •Incorrect historical dates or facts stated with certainty
- •Made-up quotes attributed to real people
- •Statistics that seem reasonable but are fabricated
- •False biographical information about public figures
- •Non-existent laws, policies, or institutional procedures
How to Detect & Prevent
- Verify important facts through reliable, independent sources
- Be extra skeptical of very specific claims without clear attribution
- Cross-check any statistics with original data sources
- Ask the AI to explain how it knows something
- Use multiple AI systems and compare their responses
- When in doubt, treat AI output as a starting point for research
Academic Integrity Alert
Never cite AI-generated sources without verification. Using fabricated citations or false information in academic work, even unintentionally, violates academic integrity standards. Always verify sources independently.
Recognizing AI Bias
AI systems learn from human-created data, which means they inherit and can amplify existing societal biases. Understanding this helps you use AI more fairly and critically evaluate its outputs.
Types of AI Bias
Gender & Career Bias
AI might assume certain professions are associated with specific genders
Cultural & Language Bias
Better performance with certain languages, dialects, or cultural contexts
Socioeconomic Assumptions
Bias toward certain socioeconomic perspectives or experiences
Geographic Bias
Over-representation of certain regions or countries in training data
How to Test for Bias
- Try the same prompt with different demographic details
- Ask for diverse perspectives on the same topic
- Question stereotypical or one-sided responses
- Consider whose voices or experiences might be missing
- Test AI responses across different cultural contexts
- Compare outputs from different AI models
Bias Testing Exercise
Try asking an AI: "Describe a successful CEO" and then "Describe a successful nurse." Notice any patterns in the language, assumptions, or characteristics mentioned.
Promoting Fairness in AI Use
You can help counteract AI bias by actively seeking diverse perspectives, questioning assumptions, and including voices that might be underrepresented in AI training data.
AI as Human Amplifier
The most effective approach to AI is using it to enhance rather than replace human capabilities. AI should amplify your expertise, creativity, and judgment—not substitute for them.
AI as Enhancement Tool
Brainstorming Partner
Generate initial ideas that you then evaluate, refine, and build upon
Research Assistant
Help organize information and identify patterns you might miss
Writing Support
Improve clarity and structure while you maintain voice and meaning
Learning Accelerator
Explain complex concepts in ways that match your learning style
Maintaining Human Agency
- Always add your own analysis, expertise, and judgment
- Use AI outputs as starting points, not final answers
- Combine AI insights with your domain knowledge
- Make the final decisions yourself based on AI input
- Maintain ownership of your work and ideas
- Apply your values and ethical framework to AI suggestions
The 80/20 Rule
Think of AI as handling 80% of the routine work so you can focus your human expertise on the 20% that requires judgment, creativity, and critical thinking.
Key Principle: You Are the Expert
AI lacks your lived experience, domain expertise, understanding of context, and ability to make nuanced judgments. The most powerful AI applications combine AI's processing capabilities with your knowledge, creativity, and wisdom. Never abdicate your role as the thinking, deciding human in the process.
The UM Critical Thinking Framework
Apply these four steps every time you use AI to ensure thoughtful, responsible outcomes
Question
Define & Assess
What am I trying to accomplish? Is AI the right tool for this task? What are the risks?
Verify
Check & Validate
How can I confirm the accuracy of AI-generated information? What sources support this?
Analyze
Examine & Evaluate
What perspectives might be missing? What assumptions or biases could be present?
Enhance
Apply & Improve
How can I add my expertise, judgment, and unique insights to create better outcomes?
Framework in Action: Quick Reference
Before Using AI, Ask:
- • What specific outcome do I want?
- • Is this appropriate for AI assistance?
- • What information should I NOT share?
- • How will I verify the results?
After Getting AI Results, Ask:
- • Does this sound accurate and reasonable?
- • What perspectives or voices are missing?
- • How can I verify important claims?
- • What can I add from my expertise?
Putting Literacy Into Practice
See how AI literacy principles apply to real academic and professional scenarios.
Research Paper: Climate Change Impacts
Graduate StudentYou're writing a literature review on climate change impacts in Montana for your thesis.
Question:
- •Is AI appropriate for finding sources and summarizing research trends?
- •What information should I NOT share with AI?
- •How will I verify AI-suggested sources?
Verify:
- •Check that all cited papers actually exist in databases
- •Verify statistics and data claims through original sources
- •Confirm that AI summaries accurately represent the research
Analyze:
- •Are diverse geographic regions represented in the research?
- •Does the AI favor certain types of studies or methodologies?
- •What Indigenous or local perspectives might be missing?
Enhance:
- •Add Montana-specific context and local knowledge
- •Include your analysis of research gaps and contradictions
- •Connect findings to local policy implications
Course Design: Introduction to Psychology
Faculty MemberYou're redesigning your intro psychology course to be more engaging and inclusive.
Question:
- •Can AI help brainstorm activities while maintaining pedagogical soundness?
- •Should I share student data or assessment details with AI?
- •How will I ensure activities align with learning objectives?
Verify:
- •Check that psychological concepts are accurately represented
- •Verify that suggested research examples are real and current
- •Confirm activities align with established teaching practices
Analyze:
- •Do suggested examples represent diverse populations?
- •Are there cultural assumptions in AI-generated scenarios?
- •What learning styles or accessibility needs might be overlooked?
Enhance:
- •Adapt activities to your specific student population
- •Add personal teaching experience and pedagogical expertise
- •Include culturally relevant examples and diverse perspectives
Common AI Literacy Pitfalls to Avoid
Over-Trust in AI Authority
Believing AI outputs because they sound confident or detailed
Solution: Always verify important claims and maintain healthy skepticism
Confirmation Bias
Using AI only to confirm what you already believe
Solution: Actively seek diverse perspectives and challenge your assumptions
Automation Bias
Preferring AI suggestions over human judgment, even when wrong
Solution: Remember that you bring irreplaceable expertise and context
Source Amnesia
Forgetting to verify AI-provided citations and references
Solution: Create a verification checklist for all AI-suggested sources
Bias Blindness
Not recognizing when AI outputs reflect problematic biases
Solution: Actively test for bias and seek underrepresented perspectives
Context Collapse
Using AI outputs without considering your specific situation
Solution: Always adapt AI suggestions to your unique context and needs
Continue Building Your AI Literacy
AI literacy is an ongoing journey. These resources will help you continue developing critical thinking skills for AI use.
UM AI Guidelines
Review our complete guidelines for responsible AI use
Approved AI Tools
Explore vetted tools that meet our security standards
Learning Resources
Access training materials and external courses
Privacy & Security
Understand data protection in AI usage
Mansfield Library AI Guide
Comprehensive AI research and ethics resources
Get Support
Contact our team for personalized AI guidance
Remember: AI Literacy is a Skill
Like any skill, AI literacy improves with practice. Start with these fundamentals, apply the critical thinking framework consistently, and continue learning as AI technology evolves. The goal isn't to become an AI expert—it's to become a thoughtful, critical, and effective AI user.