AI Literacy

Knowing what the machine is doing — and what it's doing to you

AI literacy isn't about learning to prompt better. It's about understanding who built the system, what it optimizes for, and what it costs you to use it.

What This Is

AI literacy is the ability to critically evaluate, effectively interact with, and make informed decisions about artificial intelligence. But "literacy" here means more than technical skill. It means understanding AI as a system of power — who funds it, what it extracts, whose interests it serves, and where it fails.

This Grove gathers everything in the vault about AI from a literacy perspective: the foundational concepts, the educational frameworks, the safety concerns, and the human questions AI raises about identity, agency, and trust.

The Core Tension

AI tools are simultaneously:

The most powerful learning amplifiers available to educators
The most sophisticated extraction systems ever built for attention and data

Teaching AI literacy means holding both truths. Not technophobia. Not techno-optimism. A clear-eyed understanding of what you're working with.

Foundational Concepts

These seeds and evergreens define the landscape.

AI Illiteracy 🌲 — What it looks like when people can't critically evaluate AI: misplaced trust, invisible bias, unchecked automation
Big Five Ideas in AI 🌱 — Perception, representation, learning, natural interaction, societal impact — the educational framework
Frame Problem in AI 🌱 — Why AI can't tell what's relevant and why that matters for everything from self-driving cars to essay graders
Bias in AI 🌲 — Systematic discrimination baked into training data and algorithms, from healthcare to criminal justice
AI and Emergent Behavior 🌿 — How simple rules at scale produce complex behavior — flocking birds, ant colonies, and LLMs
AI Classification Matrix 🌿 — A 2x2 taxonomy: narrow vs. general, basic vs. advanced — helps people place what they're actually using

Human Agency & Boundary Work

The central question: who's in charge — you or the model?

AI-Boundary-Co-Construction 🌲 — Research framework on how humans negotiate control with AI. Distinguishes "orchestrators" (high agency, active boundaries) from "outsourcers" (passive delegation)
AI Boundary Work and Agency 🌱 — Explores friction as a feature, not a bug — active boundary-setting as a practice of agency
Cognitive Amplification with AI 🌱 — Human-in-the-loop design focused on amplifying wisdom, not just speed. The case for productive friction.
AI and Human Identity 2035 🌱 — What happens to empathy, creativity, and agency when AI partnerships deepen? Speculative but grounded

AI in the Classroom

Practical frameworks for teaching with and about AI.

AI Workshop Framework for Educators 🌿 — 3-strand design: understanding AI/ML, designing AI-enhanced learning, AI as cognitive amplifier
AI Summer Workshop for K-8 Teachers 🌿 — 5-day hands-on workshop with PRADA computational thinking framework, emotional anchors, and deliverables
AI Detection and Authentic Assessment 🌲 — Detection tools don't work and create equity problems. Reframes assessment design as the real solution.
Frameworks for Thinking About AI in Education 🌲 — Applies Bentoism (Now Me/Us, Future Me/Us) to AI decisions. Centers structural interests over individual adoption.
AI Pedagogical Mastery - Educational Advantage 🌿 — Educators have a hidden superpower: scaffolding, assessment, metacognition all multiply AI interaction quality
AI Communication Mastery - Linguistic Advantage 🌿 — Linguistic sophistication creates compound advantages in AI interaction through semantic precision
AI Ethics in Education Policy 🌱 — Framework for transparent, trust-based AI use in courses with disclosure and reflection requirements

Safety, Sycophancy & Systemic Risk

Where AI literacy becomes a safety issue.

AI Safety Spending Gap 🌿 — Frontier labs spend billions on capability. All U.S. safety orgs combined: $133M. The structural imbalance driving everything below.
AI Policy and Regulation Beyond Blanket Bans to Nuanced Governance 🌲 — Risk-based regulation, sectoral approaches, and accountability mechanisms. The policy alternative to panic.
AI Geopolitics and the Open Model Question 🌲 — Open vs. closed models as an infrastructure control issue. Who owns the compute?
Best Available Human Standard - Pragmatic Framework 🌲 — Evaluate AI against actual available human resources, not idealized experts. A pragmatic lens.
Cheating Tension - Moral Ambiguity in AI Use 🌱 — Student guilt around AI use. The gap between policy and embodied practice.

Identity, Trust & What AI Means for Being Human

The philosophical undercurrent.

AI and the Question of Self 🌲 — How AI disrupts the self/subject distinction. Identity construction in the age of algorithmic personalization.
Generative AI and Identity 🌱 — How generative tools challenge uniqueness and authenticity. Fluidity of subject positions.
AI Beyond Simple Mimicry 🌿 — Modern AI has internal representations and reasoning, not just pattern matching. What does that mean for how we relate to it?
Trust and Sincerity Detection in AI 🌱 — Can AI help us evaluate trustworthiness of information? Design concepts for a browser extension approach.

Digital Self-determination — The parent framework: agency, context, and power in digital systems
Digital Resilience — Staying strong and sustainable when the tools keep changing
Privacy by Design — Tools that protect by default — the infrastructure AI literacy needs

Potential Forest

This Grove feeds into: Digital Literacy Framework

AI literacy is not a separate domain from digital literacy. It's the newest, most urgent frontier of the same fight: understanding who built the system you're using, what it costs you, and what alternatives exist.

AI literacy is not about keeping up with the technology. It's about refusing to be kept by it.