When Search Becomes an Answer: What Generative AI Changes About Learning
Generative search can answer before a learner has selected sources, compared evidence or built a synthesis. What does that change about learning?
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#AI literacy
Publications tagged with AI literacy.
OpenAI, ChatGPT, GPT and LLM: What Is the Difference?
OpenAI is the organisation, ChatGPT the product, GPT a model family and LLM the wider category. Learn why those layers matter in practice.
ReadDon't Ask Whether AI Makes Us Dumber. Ask What Kind of Thinking We Stop Practicing
What current evidence says about AI, learning and independent thought, and why ready-made answers and well-designed scaffolding produce different outcomes.
ReadHow to ask AI for a counterargument
A practice for using AI to test your argument without treating the model as the judge, opponent or source of a final verdict.
ReadHow to Check Whether the Model Has the Context It Needs
An exercise for checking whether a model still represents the goal, constraints, sources and earlier decisions accurately - without pretending to measure the technical context window.
ReadHow to check your assumptions with AI
A practice for checking your first reading of a situation: what is fact, what is assumption, what is emotion and what is still unknown.
ReadHow to use AI without handing over the decision
A practice for organizing options, criteria, risks and missing information without transferring responsibility for the choice to the model.
ReadHow not to confuse fluency with truth
A practice for pausing when an AI answer sounds coherent and confident but may hide uncertainty, assumptions, missing sources or overgeneralization.
ReadHow to analyze a message without diagnosing a person
A practice for organizing a difficult message with AI without diagnosing the sender, mind-reading intentions or handing over responsibility for the reply.
ReadHow to ask a model about uncertainty
A practice for asking AI to show assumptions, missing context, caution levels and conditions that can change an answer.
ReadHow to check whether an AI answer has sources
A practice for breaking an AI answer into claims, sources and risk points before it enters an article, presentation, team note or decision.
ReadHow to separate facts from interpretations
A practice for separating visible facts, interpretations, assumptions, judgments and missing questions before a conclusion or decision forms.
ReadHow to use AI as a second reader
A practice for using AI as a second reader: checking clarity, structure, tone and reception risks without handing over authorship.
ReadTrust in the age of ready-made answers
How generative AI compresses the path from sources to answers, and why trust must be calibrated through provenance, uncertainty, disagreement and human responsibility.
ReadA good summary is not the same as a good decision
AI can organize information well, but decisions require criteria, responsibility and an understanding of consequences.
ReadDo not diagnose people from emails
AI can help organize difficult communication, but it should not be used to diagnose people, personalities or intentions from correspondence.
ReadThe model sees text, not the whole relationship
AI can help analyze communication, but it only sees the text and context we provide. It does not know the whole relationship or people's intentions.
ReadFluent does not mean true
A short note on why a smooth and convincing AI answer should not automatically earn trust.
ReadAI does not read people. It helps make sense of the situation.
A case-study based essay on how AI can help organize difficult communication: tensions, missing information, possible readings and response risks.
ReadThe model does not remember. It works with context.
When working with AI, it is easy to confuse context with memory. That mistake can lead to wrong expectations, misplaced trust and poor decisions.
ReadIt is not just about the prompt
The prompt is only the visible part of working with AI. Mature AI literacy also depends on context, cognitive load, trust, verification and responsibility.
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