[mncharity]

Ah, perhaps I should have said something like "educational materials, and apps, and other useful things" (disapproving judgement in the original).

> Well, the thing is that the educational materials are largely free.

A triumph and fruition of these last decades of massive effort. Now we just need to deal with their quality (with commercial as bad as free). AI may help, by reducing barriers to content creation - you might for example, now more easily author an intro astronomy textbook, one that doesn't reinforce top-30 common misconceptions, something the most used (US; commercial) texts still don't manage.

[fivestones]

I’m pretty curious. What are those too 30 misconceptions in US commercial astronomy texts? Is there a list somewhere? Or can you name some?

[mncharity]

Sigh. One impact of AI will hopefully be more readily available systemic survey papers. [1] might-or-not be a good place to start... but it's paywalled (by the National Science Teacher Association no less), and I don't quickly see preprints/scihub/etc. Here's an old unordered list for browsing[2], and a more recent one[3]. Trumper did a series of papers asking the same few questions of various populations, to give a feel for numbers - like half not knowing day-night cause. Most lists are on subsets of astronomy, and most info on frequency on short lists. So... it's a mess. As are textbook reviews. Key phrases are "astronomy education research" and "misconceptions".

The one bit I explored was 'what color is the Sun (the ball)'. Asking first-tier astronomy graduate students became a hobby, as most get it wrong (except... for those who had taken a graduate seminar covering common misconceptions in astronomy education). So I libgen'ed the 10-ish most used intro astronomy textbooks in US according to some list. IIRC, it broke down roughly into thirds of: correct (white); didn't explicitly say but given surrounding photos, or "yellow" (as classification without clarification), there's no way students won't be misled; and explicitly incorrect (yellow). Hmm, bulk evaluation of textbooks against some criteria is another thing multi-modal models could help with.

(A musing aside re AI for systemic reviews. Creating one is a structured process. They have been very manpower intensive, so they aren't refreshed as often as is desired, nor consistently available. And at least in medicine ("X should be done in condition Y"), there's a potential for impact. I imagine close reads of papers isn't quite there yet. But maybe a human-AI hybrid process?)

[1] https://www.per-central.org/items/detail.cfm?ID=14009 [2] https://web.archive.org/web/20070209033543/http://www.physic... [3] appendix A of https://digitalcommons.library.umaine.edu/etd/2200/ [4] https://www.oranim.ac.il/sites/heb/SiteCollectionImages/pers...