How Students Can Fact-Check AI Answers

Chatbots such as ChatGPT, Gemini and Claude generate text by predicting likely words, not by looking facts up in a verified database. That is why they can produce a wrong date, a merged quote or a citation that looks real but was never published, errors often called hallucinations. A well-known example came in 2023, when lawyers in the New York case Mata v. Avianca were sanctioned after filing a brief containing court decisions that ChatGPT had invented. The most reliable habit for students is lateral reading, a technique researchers at the Stanford History Education Group observed in professional fact-checkers. Instead of studying one page closely, fact-checkers leave it and open new tabs to see what other trustworthy sources say. Mike Caulfield's SIFT method packages this into four moves: Stop, Investigate the source, Find better coverage, and Trace claims to their origin. Applied to AI, that means pausing before copying, asking where a claim could have come from, searching for independent coverage, and following citations to the original document. Source triangulation adds a rule of thumb: an important claim should be confirmed by at least two independent, credible sources, ideally including a primary source such as a government dataset, the original study or the text of a law. Two websites repeating the same press release do not count as independent. Confidence checks help decide where to spend effort. Specific numbers, names, dates, quotes and citations are the highest-risk parts of an answer. Recent events are risky because a model's training data has a cutoff. A common misconception is that tools with web search never err; they reduce some errors but can still misread or misattribute the pages they cite. Another is that the AI can check itself. Asking it whether it is sure is not verification, because it may simply repeat or reverse its answer.