AI and Machine Learning
This Map of Content explores the rapidly evolving landscape of Artificial Intelligence (AI) and Machine Learning (ML), with a focus on foundational concepts, applications, ethical concerns, and pedagogical implications.
AI is not just a technical domain—it is a social, cultural, political, and epistemological force reshaping education, labor, identity, and power.
Systems, Agents, and Knowledge Infrastructure
Recent notes in this map lean beyond the model itself and into the stack around it:
- AI System Layers - model, harness, and product as a single stack
- AI Model - the trained capability
- AI Harness - prompts, tools, memory, and orchestration
- AI Product - the wider application and business system
- AI System Infrastructure - servers, routing, auth, telemetry, and backend pieces
- Agentic AI - systems that act across steps
- Large Language Model (LLM) - the core language-model form this wiki keeps returning to
- Retrieval-Augmented Generation (RAG) - external retrieval as part of the workflow
- Model Context Protocol (MCP) - a connective layer for tools and context
- Personal AI Stack Design Framework - routing local, cloud, memory, and workflow choices
- AI and ML Glossary Framework - the vocabulary layer that supports the map
🧠 Core Concepts
- Artificial Intelligence 🔹
- Machine Learning 🔹
- Neural Networks 🔹
- Deep Learning 🔹
- Natural Language Processing (NLP) 🔹
- Generative AI 🔹
- Large Language Models (LLMs) 🔹
- Supervised vs Unsupervised Learning 🔹
- Reinforcement Learning 🔹
- Computer Vision 🔹
🛠️ Applications & Tools
- AI in Education 🔸
- AI Writing Tools 🔸
- AI Image Generation Tools 🔸
- Voice Assistants and Smart Devices 🔸
- Facial Recognition Systems 🔸
- Predictive Policing 🔸
- Recommender Systems 🔸
- ChatGPT 🔸
- NotebookLM 🔸
- Google AI Studio 🔸
⚖️ Ethics & Societal Impact
- Bias in AI 🔸
- Algorithmic Accountability 🔸
- AI and Labor 🔸
- Surveillance AI 🔸
- AI and Democracy 🔸
- AI and Misinformation 🔸
- Ethics in AI 🔹
- Algorithmic Oppression 🔸
- Transparency vs Explainability 🔸
- Responsible AI Development 🔹
🔐 Privacy & Security Concerns
- Privacy-Preserving AI 🔸
- Federated Learning 🔸
- Differential Privacy 🔸
- Homomorphic Encryption 🔸
- AI and Data Sovereignty 🔸
- AI and Cybersecurity 🔸
📚 Learning & Pedagogy
- Teaching AI Literacy 🔹
- AI in K–12 Education 🔸
- Ethical AI Curriculum 🔸
- Computational Thinking 🔹
- AI as Cognitive Amplifier 🔸
- AI for Inquiry-Based Learning 🔸
- Media Literacy and AI 🔹
Connected Groves
- AI Literacy - the civic and critical lens on using AI well
- Technology & Society Index - the broader systems-and-impact frame
🌲 Evergreen Notes
- AI-Boundary-Co-Construction 🔹 - Framework for human-AI interaction and boundary work
- What AI Cannot Know 🔹 - Tacit knowledge, embodied learning, and AI limits
- AI and the Question of Self 🔸 - Self vs. subject distinction in the age of AI
- AI Geopolitics and the Open Model Question 🔸 - Open vs. closed models and infrastructure control
- 02 DEVELOP/HITL Pedagogy Toolkit 🔹 - Human-in-the-loop classroom practices and prompts
- AI Detection and Authentic Assessment 🔹 - Beyond detection tools to authentic assessment
- Frameworks for Thinking About AI in Education 🔸 - Bentoism, contingency, and evaluating AI claims
🧭 Critical & Interdisciplinary Perspectives
- Critical AI Studies 🔸
- Feminist AI 🔸
- Decolonizing AI 🔸
- Abolitionist Tech Futures 🔸
- AI and Climate Justice 🔸
- AI Narratives in Popular Culture 🔹
🔗 Related MOCs and Indexes
- AI and Digital Resilience Index
- AI Literacy
- AI System Layers
- AI Model
- AI Harness
- AI Product
- AI and ML Glossary Framework
- Technology & Society Index
- Platform Studies MOC
- Privacy and Security MOC
- Digital Literacy MOC