When AI gives you good advice and your answers improve, do you end up trusting it more than you should?

This explores whether good AI advice still leaves people more confident than the improvement justifies, either in the AI's answer or in their own ability. The corpus points to yes, with one caveat: no note here measures accuracy and confidence side by side in the same study. The answer is pieced together from adjacent findings, and it comes out looking like a mismatch between what earns trust and what earns accuracy.

Start with what people actually track. Users in every language studied trust confident-sounding AI output whether or not it is right, because they follow the confidence signal, not the accuracy record (Do users worldwide trust confident AI outputs even when wrong?). Explanations work the same way. Reasoning traces and post-hoc justifications raise acceptance of AI answers whether they are correct or not (Do explanations actually help users spot AI mistakes?). So when AI advice does improve accuracy, the user's high trust is mostly a coincidence. Confidence is set by style, and the advice just happens to be good this time. That is why the trust looks calibrated when the advice is right and turns costly when it isn't.

The sharper version of your question is about people's confidence in themselves. Here the corpus says that helpful AI is exactly what causes the inflation. When AI output is seamless and fluent, users read the polish as evidence of their own competence, because they experience ease of processing as a sign of skill (Does processing ease mislead users about their own competence?). They then fold the AI's output into their sense of what they can do, and end up believing they have skills they don't (Do AI-assisted outputs fool users about their own skills?). Four mechanisms feed this: it is unclear who did what, output is fluent, thinking gets handed off to the AI, and the pipeline is opaque. The note describes them as multiplicative, each amplifying the others (How do AI tools trick users into overestimating their own skills?). The better the AI's output, the stronger the illusion, so accuracy gains and overconfidence can rise together.

Accuracy gains can also hide the cases that matter. In medical triage, legal interpretation and financial planning, confident wrong answers cluster in rare cases where surface heuristics clash with unstated constraints, and strong overall accuracy makes those cases invisible (Why do confident wrong answers hide in standard accuracy metrics?). A user who has seen the AI be right nine times has good reason to trust it, but that trust carries over to the tenth case, where the AI is confidently wrong. One note argues that this kind of trust is a compounding cognitive trap. Confusing the AI's map with the territory, mistaking intuition for reasoning, and seeking confirmation each distort judgment, and they get worse when they occur together (Why do people trust AI outputs they shouldn't?).

Two designs in the corpus try to keep the accuracy gain without the false confidence. Showing arguments for and against an answer is the only explanation format that actually helped users tell right AI answers from wrong ones. That is the difference between calibrated trust and a nudge toward agreeing. A second approach has the AI point out which parts of the input matter and leave the decision to the human. That removed anchoring bias and helped on hard cases people couldn't handle alone, while keeping responsibility with the person (Can AI guidance reduce anchoring bias better than AI decisions?). The pattern is that advice which comes with an argument or a pointer gives users something to check against, while a confident, fluent answer with nothing to check invites trust.