Teaching and Learning
Curated external and Columbia resources for students and future practitioners
- Columbia Generative AI Policy, Office of the Provost
- SPS Use of Artificial Intelligence Guidance for Students
- Best Practices for Responsible AI Use at Columbia, CUIT
- Columbia AI Services, CUIT
- Academic Integrity Policies, Center for Student Success and Intervention
- AI at Columbia SPS
- AI Teaching & Learning Resource Guide, Academic Affairs at SPS
- Teaching and Learning in the Age of AI, Center of Teaching and Learning
- AI capabilities and limitations, Claude Academy, 13 lessons on next-token prediction, knowledge, working memory, and context limits
- AI Guide tutorial, AI Pedagogy Project at Harvard metaLAB, three-part interactive tutorial
- TensorFlow Playground, browser experiment with a simple neural network
- What Is ChatGPT Doing and Why Does It Work?, Stephen Wolfram
- Attention Is All You Need, Vaswani et al., 2017, the transformer paper
- Neural Networks series, 3Blue1Brown, visual explanations
- Prompt Engineering Guide, Learn Prompting, the same tutorial the SPS faculty guide assigns
- A Columbia Prompt Library for Effective Academic AI Use, CUIT
- Context Engineering 101 and Advanced Context Engineering, CUIT
- Context Engineering Guide, DAIR.AI
- AI Fluency: Framework and Foundations, Claude Academy, four hours on delegation, description, discernment, and diligence
- Columbia University Libraries research guides and Ask a Librarian service
- SPS Library Research Guide
- Critical Student Use of AI and Critiquing AI Output, Columbia CTL
- AI in Education Canvas page, University of Sydney, examples of AI in the learning process
- Hugging Face LLM Course, free, 12 chapters from transformers through fine-tuning
- Hugging Face Agents Course, free and certified, covering smolagents, LangGraph, and LlamaIndex, with a final project scored against a public benchmark
- Hugging Face MCP Course, free, on the Model Context Protocol
- OpenAI Academy, free self-paced courses including AI Foundations, Applied AI Foundations, and Agents and Workflows
- Practical Deep Learning for Coders, fast.ai
- Kaggle Learn, short applied data and ML courses
- Google Skills, free Introduction to Generative AI path with a badge
- AI Index Report 2026, Stanford HAI, ninth edition across nine chapters covering research, performance, investment, regulation, and public sentiment
- Future of Jobs Report, World Economic Forum
- LinkedIn Learning AI paths, available free to Columbia affiliates through CUIT
- Industry certification paths through Microsoft Learn, Google Cloud, and AWS Skill Builder
- Recommendation on the Ethics of Artificial Intelligence, UNESCO
- OECD AI Principles
- AI Ethical Guidelines, EDUCAUSE
- Ethics of Artificial Intelligence and Robotics, Stanford Encyclopedia of Philosophy
- Generative AI's Environmental Impact, MIT News
- AI Incident Database, which recorded 362 documented incidents in 2025, up from 233 in 2024
- AI Risk Management Framework, NIST, voluntary framework built around Govern, Map, Measure, and Manage
- The EU Artificial Intelligence Act
- ISO/IEC 42001, the AI management system standard, cited by 36 percent of organisations surveyed for the AI Index
- AI, Large Language Models and Data Protection, Irish Data Protection Commission
Workshops & Training
The resources on this page are created and maintained by organisations outside Columbia University. The SPS AI Lab has no affiliation, partnership, or sponsorship arrangement with any of them. We share their teaching material because we believe they are useful to students and practitioners.