AI Essentials

Artificial Intelligence

The simulation of human intelligence in machines that are programmed to think, learn, and act like humans. Artificial intelligence refers to computer systems that perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, making predictions, generating content, and supporting decision-making.

At PRAIRIE, we focus on AI as a tool that enhances human expertise rather than replacing it.

Machine Learning

Algorithms that learn patterns from data and improve their performance without being explicitly programmed.

Examples

• Spam Detection

• Predictive Analytics

• Recommendation Systems

Deep Learning

A subset of machine learning that uses neural networks with many layers to model complex patterns in data.

Examples

• Image Recognition

• Speech Recognition

• Language Translation

Computer Vision

Enables computers to interpret and understand visual information from the world.

Examples

• Self-Driving Cars

• Medical Image Analysis

• Object Detection

Natural Language Processing (NLP)

Allows computers to understand, interpret, and generate human language.

Examples

• Chatbots

• Sentiment Analysis

• Text Summarization

Generative AI

AI models that generate new content such as text, images, code, audio, and more.

Examples

• Text generation

• Image generation

• Code generation

Generative AI refers to AI models that create "new" content.

Instead of just analyzing or classifying existing data, generative AI learns patterns from large amounts of data and uses that knowledge to generate content such as text, images, code, audio, and more.

Think of it as a creative partner that can help you brainstorm, write, design, and solve problems.

How it works (in a nutshell)

1. Learns from data
   The model trains on huge amounts of information.
2. Finds patterns
   It discovers relationships and structures in the data.
3. Generates new content
   Based on what it learned, it predicts and creates something new.
4. Human + AI
   You review, refine, and use the output to achieve your goals.

Examples

• Text
  Write emails, summaries, stories, and more.
• Images
  Create illustrations, concepts, and designs.
• Code
  Generate, explain, and debug code.
• Audio
  Compose music, voice, and sound.

LLM

Large Language Model: AI systems trained to understand and generate human language.

Prompt

The instructions you give an AI model.

Hallucination

When AI generates inaccurate or fabricated information.

Context Window

How much information an AI model remembers in one conversation.

Tokens

Small units of text processed by AI models.

Choose the right AI tool for your needs and unlock smarter ways to learn, create, and work efficiently.

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Building a culture where artificial intelligence amplifies human creativity, strengthens learning, advances research, and serves society responsibly.

The PRAIRIE Initiative promotes a practical, human-centered approach to artificial intelligence—one that balances innovation with ethics, transparency, and accountability. Explore our foundational guidance documents and learn how responsible AI can enhance teaching, learning, research, and professional practice.

Read the White Papers

Responsible AI is More Than Technology

Artificial intelligence is transforming engineering education, research, and industry. To realize its full potential, AI must be implemented intentionally—with clear expectations, ethical considerations, and a commitment to preserving human judgment.

At PRAIRIE, responsible AI means creating systems and practices that:

• Empower people rather than replace them

• Promote transparency and accountability

• Support equitable access to AI technologies

• Protect privacy and institutional data

• Develop graduates who can think critically alongside AI

These white papers establish Nebraska Engineering's framework for responsible AI integration. Together they provide both the conceptual foundation and the practical guidance for using AI across teaching, learning, research, and professional practice.

Responsible AI in Teaching and Learning

As generative AI becomes part of higher education, instructors and students need practical guidance for using these tools responsibly. This white paper presents a framework that encourages meaningful learning while preserving academic integrity, human judgment, and durable skill development.

Rather than asking whether AI should be used, the paper focuses on how AI can be integrated thoughtfully to improve learning outcomes while maintaining educational excellence.

Key Topics

• Purposeful AI Use

• Transparency

• Academic Integrity

• Critical AI Literacy

• Equity & Access

• Privacy & Data Protection

Who Should Read This?

• Faculty
• Instructors
• Teaching Assistants
• Students
• Academic Leaders

Read White Paper

Successful AI adoption requires more than policies—it requires a common vocabulary. This publication establishes a shared language for discussing AI across education, research, administration, and industry collaboration.

By defining consistent terminology and concepts, the framework helps faculty, students, administrators, and partners communicate more effectively about responsible AI implementation while reducing ambiguity across disciplines.

What You'll Learn:

• Common Definitions

• Cross-Disciplinary Communication

• Responsible Decision-Making

• Institutional Alignment

• Research Collaboration

• Future Readiness

Who Should Read This?

• Faculty

• Researchers

• Staff

• Graduate Students

• Industry Partners

• Institutional Leaders

Read White Paper