Because research requires precision. To do background reading, I have to throw out everything that isn't relevant to my particular study. My study is new, so the main driver is my concept of what I want to do. The AI is good at finding things with the same keywords, but not at excluding what isn't relevant to my thought process.

I use Google Scholar for searching for literature. I put in some keywords, then scan through the titles. Most of them are not relevant, but I can click through hundreds of titles in a few minutes to focus my attention where it matters, then use the references/citations of those papers to build out what I need to focus on.

AI will give me a massive block of text that includes 5-6 references, with long explanations of what each is about. It is an attempt to do the same thing, but I have to throw out most or all of them, re-prompt, and then get another similar answer. Sometimes it throws things out that aren't peer reviewed.

It also loses the happenstance of stumbling on something irrelevant but important. That's why I still look at the monthly journal publication lists to see what is happening.

Writing is similar. I need to condense several peer reviewed papers to the core ideas that are relevant to what I am saying, which might mean 1-2 sentences. AI will always give paragraphs that are not really getting to what I want, so I still have to do the work myself anyway. It is just adding steps to do the same process.

when you use AI, its going to hallucinate, it might even make up results at worst, and not cite the SOURCES correctly, very important a research paper, if its summarizing from different sources, (it will be kinda hard to parse all that info). in a science paper, when you cite a source you have to do like a superscript like ^1^ denoting the order of the sources you got it from. it gets messy when someone calls out your paper, and they get the publisher/you to retract it.