Image Credits: Generated with Gemini Most schools and workplaces treat AI literacy as a one hour workshop on how to write better prompts. The training shows people how to ask for clearer answers, specify tone, add constraints, and refine outputs through iteration. That is useful, but it is also the smallest piece of what AI literacy should mean. Real AI literacy is the ability to decide when to use AI, what to trust, what to verify, what to disclose, and what risks you are ta

Image Credits: Generated with Gemini Most universities responded to generative AI by updating their honor codes and adding a line about ChatGPT to the syllabus. The updates sound thorough enough. But the enforcement is scattered, and the underlying problem is still there, which is that the old definition of academic integrity assumed you could separate help from cheating by drawing a line between tools and outsourcing. That line does not exist anymore. Academic integrity used

Most people see ‘$X per 1M tokens’ and assume it will never matter. It matters the moment users show up, because every prompt and every answer is metered. A token is not a word. It is a chunk of text the model processes, and it can be as short as a character or as long as a full word, with spaces and punctuation counted too. One simple rule of thumb is that 1 token is about 4 characters or about 0.75 words in English, which means 100 tokens is roughly 75 words ( OpenAI, 2026b