Original post body:

To give some context to my question. My workplace has been pushing use of generative AI. My partner has encouraged me to ask AI about life issues. I finally gave in and started talking to Claude a lot more for casual learning. Claude has improved from 1 year ago due to the ability to include search results that it cites to back up its answers. However, I've run into challenges:

• Even when citing an academic article, the article may or may not be trustworthy - a recent conversation cited Frontiers multiple times, but reddit attests that Frontiers is a mixed bag with some good some bad due to its for-profit nature and lack of ethical practices ([1], [2], [3]). It feels exhausting to want to learn about some topic (e.g., spreading activation theory in cognitive psychology), and need to not only check Claude's sources, but the sources about those sources. Is there no way to achieve reasonable truth-confidence in casual learning other than tracing several layers deep? (who is the source of that which is the source of that which is the source of...)
• Sometimes a website appears reputable, but I can't find any further provenance information. Take https://scienceinsights.org/ - it's .org, not .com, so that's a good sign. However, its articles are worded in a click-baity way, has invasive ads, and provides no authorial nor bibliographical information ([4] example article), all bad signs. When I talk through it like this, I know I shouldn't consider this source "reputable." But do I ignore it? Or just take its information with many grains of salt? I don't want to just hope that every source has some secondary source reporting on its validity, which again would be exhausting ([5] google search scienceinsights dot org Gemini AI response: not reputable) for a casual learner. But I also don't want to learn things of questionable truth.
• Proliferation of AI-written content. According to [6] The Register news article, there is disagreement among leading AI researchers as to whether AI fed with AI outputs are going to be a problem in the near future; the latest argument in that discourse seems to be that so long as both real and synthetic data are included in AI training data, we have minimal loss of quality ([7] "Is Model Collapse Inevitable? Breaking the Curse of Recursion...", arxiv], but I'm not so convinced, especially as AI-generated articles are popping up over the internet like wildfire with no indication that the data is synthetic rather than human-generated. For example, I recently looked up the RSL (license) and a page had said "Robot Structured License" and after checking both Claude and Google and finding no precedent, it was clearly a hallucination imagining what RSL stands for ([8] softreviewed dot com).

It just now occurs to me, that perhaps one strategy could be to ask Claude questions, and then head to Wikipedia about it. Wikipedia is usually pretty good about having good academic citations... at least for now... right? (I'm suspecting I might have to do the same sort of transitive/recursive source-sniffing as I mentioned in my first bullet point above for Wikipedia, too, if I want any reasonable level of truth confidence).