10 Glossary: Generative AI Terms for Educators

Core Concepts

• Generative AI (GenAI)
  A subset of AI that creates new content—text, images, code, audio, etc.—based on patterns in training data. Tools include ChatGPT, DALL·E, Midjourney, and others.

• Large Language Model (LLM)
  A type of AI trained on massive amounts of text to generate human-like language. Examples: GPT-4, PaLM, Claude.

• Multimodal AI
  AI models that process and generate multiple types of data, like text and images together (e.g., GPT-4 with vision, Gemini).

• Training Data
  The data used to teach an AI model. Its composition directly affects the AI’s outputs, capabilities, and biases.

• Prompt Engineering
  The practice of designing inputs (prompts) to get desired outputs from AI systems. A crucial skill in AI-assisted pedagogy.

Pedagogical Terms

• AI Literacy
  The ability to understand and use AI tools.

• AI Critical Literacy
  The ability to understand, use, and critically assess AI tools. This is a growing component of digital literacy in higher education.

• AI-Enhanced Learning
  Teaching and learning experiences enriched by AI tools—for personalized feedback, content generation, tutoring, etc.

• Assessment Integrity
  Ensuring that evaluations of student learning remain valid and fair, especially when students may use AI tools.

• AI Disclosure Policy
  A course or institutional policy detailing how students may or may not use AI tools in assignments and assessments.

• Co-Creation
  A teaching strategy where students and AI collaborate on content creation, with students critically evaluating AI contributions.

Technical & Ethical Considerations

• Bias in AI
  Systemic distortions in AI outputs due to skewed training data or model design—important in social sciences, humanities, and ethics discussions.

• Hallucination (AI)
  When an AI generates plausible-sounding but false or misleading information—a critical concept for academic rigor.

• Explainability / Interpretability
  The degree to which AI decisions or outputs can be understood by humans—relevant in STEM and AI ethics curricula.

• Data Privacy
  Concerns around storing and using personal or student data when interacting with AI tools (FERPA, HIPAA, GDPR compliance).

Tools & Platforms

• Chatbots / LLM Interfaces
  Interfaces like ChatGPT, Claude, or Gemini that allow users to interact with large language models for a variety of academic and creative tasks.

• AI Writing Assistants
  Tools integrated into platforms such as Microsoft Word or Google Docs that help with grammar, tone, summarization, and other writing functions.

• AI Image & Media Generators
  Applications that create visual, audio, or multimedia content from text inputs—used in courses on digital media, journalism, and design.

• Detection Tools
  Programs that attempt to detect AI-generated text. These tools are unreliable, often biased against non-native English speakers and other populations,. Their results can be inaccurate and should not replace thoughtful instructor judgment or institutional policy.

• Prompt Libraries
  Collections of AI prompts designed for specific disciplines or assignments, often shared among faculty to support effective AI use in the classroom.

Emerging Terms

• PedagAI
  A term sometimes used to describe pedagogical practices specifically designed for or with generative AI tools.

• Synthetic Media
  Any media (text, video, images) generated by AI. Increasingly relevant in digital media, communications, and ethics courses.

• Zero-Shot / Few-Shot Learning
  How LLMs perform tasks with no or minimal examples in the prompt—important for understanding model behavior in classroom use.