The same anticipation of great things happening preceded the arrival of widely available internet, but all we really got was cat videos initially, and doomscrolling more recently. I don’t have much hope for great things anymore.
I saw a Microsoft talk decades back, that was a dispirited "the people of India could be buying educational materials and... but no, all the money is in ringtones". For some kinds of business perspective, ok I guess. But for others, and for civilizational change, what's going on in the tail can matter a lot. Does China become a US engineering/science peer in early 21st C absent an internet/WWW?
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.
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?)
> Systematic reviews are rigorous, transparent, and reproducible research studies that synthesize all existing evidence on a specific topic to answer a focused question and minimize bias. Unlike narrative reviews, they use predefined eligibility criteria, comprehensive searching, and critical appraisal to evaluate primary literature, often employing meta-analysis for quantitative results. [goog ai overview, edited]
