LearningFirst for Educators
AI Literacy
A teaching program that builds genuine AI literacy — not just how to use AI tools, but how they work, what they can and cannot do, and what changes when intelligence becomes infrastructure. Students develop the analytical framework to evaluate AI claims, use AI tools effectively, and think clearly about AI's implications for careers, society, and knowledge itself.
An educator-led course, supported by structured reading and the LearningFirst learning platform.
About the program
This course covers seven modules on AI literacy: the foundations of how AI systems work (without requiring coding), the capabilities and limitations of current models, practical skills for working with AI tools, the economics and labour-market implications, ethical frameworks, and the deeper questions about intelligence, creativity, and knowledge that AI raises. Educators guide students beyond surface-level AI hype and fear toward genuine understanding. Each session combines guided reading with hands-on exercises, critical analysis of AI claims, and structured discussion.
Curriculum & takeaways
Built on the core textbook AI Literacy
What learners take away
- 5mechanism explainers — neural networks, transformers, token prediction, training, and embeddings explained through what they compute
- 5structural failure analyses — hallucination, bias, brittleness, sycophancy, and why the next version will not fix them
- 4historical analogies evaluated for what they capture and conceal — electricity, printing press, photography, and what no metaphor covers
- 5industry structure maps — the AI stack, open vs closed, US-China competition, business models, and power concentration
- 3literacy frameworks — the minimum viable mental model, three postures that fail, and what remains irreplaceably human
Curriculum structure
7 modules · 10 key concepts01 What AI Actually Is
The actual mechanisms — neural networks, language models, and what 'intelligence' means when a machine does it.
02 How AI Thinks
Pattern matching, statistical inference, and what 'learning' means for machines versus humans.
03 Where AI Fails
Structural limitations — hallucination, bias, brittleness, and sycophancy as features, not bugs.
04 AI Through the Lens of History
What electricity, the printing press, and photography reveal about AI — and what no metaphor captures.
05 The AI Industry
Who builds AI, how it is structured, where the power sits, and who gets left out.
06 AI and Human Intelligence
What AI clarifies about how humans think, learn, and create.
07 Thinking Clearly in an AI-Saturated World
What genuine literacy looks like — the mental model, the failed postures, and what remains irreplaceably yours.
Core understanding
- AI is a prediction engine, not an understanding engine — large language models generate text by predicting the next token, and the mechanism that makes them articulate is the same mechanism that makes them fabricate.
- AI's failures are structural, not bugs — hallucination, bias, brittleness, and sycophancy are consequences of how these systems work, not defects to be patched away.
- Historical analogies illuminate and conceal in equal measure — AI resembles electricity (infrastructure), the printing press (knowledge access), and photography (creative displacement), but no single metaphor captures what happens when a technology operates on the cognitive layer itself.
- The AI industry is concentrated, expensive, and geopolitically contested — training frontier models costs hundreds of millions and depends on supply chains that only a few entities control.
- AI clarifies human cognition by formalising its shortcuts — hallucination mirrors confabulation, sycophancy mirrors social desirability bias, and studying AI failure modes is a surprisingly effective way to understand one's own.
- Genuine AI literacy is a mental model, not a skill set — it means holding an understanding accurate enough to reason with: what the technology does, what it cannot do, who controls it, and what incentives drive its development.
Questions students will explore
- What does a large language model actually do when it generates text — and why does the same mechanism that makes it articulate also make it fabricate?
- Why are hallucination, bias, and sycophancy structural features of how AI works rather than bugs to be fixed?
- What do comparisons to electricity, the printing press, and photography get right about AI — and what does every historical analogy miss?
- Who builds AI, who profits from it, and why does the concentration of capability in a few companies and two countries matter?
Key concepts
- Function Approximation
- Next-Token Prediction
- Hallucination as Structure
- Training Data as Worldview
- The AI Stack
- Open vs. Closed Models
- Pattern Matching vs. Causal Reasoning
- The Metaphor Problem
- Sycophancy and Performed Judgment
- The Literacy Threshold
Course-specific outcomes
Students will be able to:
• Explain how large language models and other AI systems work at a conceptual level
• Distinguish between genuine AI capabilities and marketing claims
• Use AI tools effectively and critically, understanding their strengths and failure modes
• Evaluate AI's impact on specific industries and career paths
• Apply ethical frameworks to AI deployment decisions
• Articulate informed positions on AI governance and societal impact
Assessment & accreditation
LearningFirst connects course learning, assessments, results, and student records in one system. Where included, examination and accreditation services are supported by Extracurricular Activity Excellence International Pte. Ltd. (EAE), whose registered Singapore business activity is “Examination and accreditation agencies for education services” (SSIC 85502). Any award remains subject to the course’s stated requirements.
Demonstrate understanding
Students take LearningFirst’s course assessments to show what they have learned.
Earn a certificate
Students who meet the required standards receive a certificate identifying its awarding entity and assessment standards.
Record the achievement
A learning transcript documents assessment results as part of the student’s learning record.
Lasting value, beyond the course
Achievements students can carry forward
Certificates, earned credits, and learning transcripts become lasting assets in each student’s learning portfolio—evidence of knowledge, effort, and achievement they can build on and present throughout their lives.
- Further study
- Professional development
- Future careers