Was learning easy to look up facts ever slam dunk useful?

It had its place in the 80s when looking up involved at least finding the right book. But that time was already on the way out in the 00s. My impression was more that education likes to ask for facts because they are easy to test for.

What you really want to teach are concepts and broad strokes. Nobody needs to know on which weekday the Soviet Union collapsed, but an understanding of the causes of the collapse is useful. Similarly, you don't need to know the speed of light to more than one digit of precision, but knowing the role of the speed of light as the speed at which information can travel through space is useful

That kind of knowledge also continues being useful in the age of AI. It allows you to see the connections in the world, and know which questions you even have to ask

>> Why bother learning to retain any information when AI knows everything and can recall it in a split second.

> This is a real question though.

Is it? Why bother to learn how to add 2 numbers when you can just use a calculator.

> Learning means something different than it did in decades previous.

Not really. This is just the acquisition of a new tool. It can possibly be incorporated into education like how calculators were used in the 00s/90s, but you must know how to do the work without the tool.

Don't agree. If you know no facts, you'll be unable to think even the most basic thoughts. The more you know, the deeper your thinking can be as well as the more subtle.

For every fact you learn there is a necessary context and numerous exceptions when it's applied in other contexts. If you expect AI to spit all relevant facts at you every time you're clueless, you will have no idea when AI is wrong or lying. You will have to trust everything AI (or its owner) tells you.

I think the shift is in part from learning behavior indicating an ability to apply (‘I wanted to do something so bad I went to the library/college/etc.!’), to the free availability of knowledge leading to a delinkage of knowledge v.s. the ability to apply knowledge and get a desired outcome.

AI can really help the outcome part (by just doing it for you), so for better or worse I see that delinkage continuing.

People who bother to actually learn while using AI will have an edge on those shovel slop, but there’s clearly a difference from previous eras.

> If the folks wielding these tools are not using a tool effectively

I struggle with this argument when the subject ("the folks") turns out to be "many people", perhaps even a majority of people. If one teacher is an outlier that uses it badly then sure, the teacher is to blame and be judged. But if a collective is using it badly, then perhaps the capability of the collective should have been evaluated before handing the tool to them.

I don't have an answer - it is bad to withhold a tool from the hands of people who will use it badly. But the overall outcome has to also count.

> In what world is a square not a rectangle?

In the world of object-oriented programming: https://en.wikipedia.org/wiki/Liskov_substitution_principle#...

I read their comment as the teacher (who was correct) and a tutor (who used AI) disagreed, not two tutors.

With the amount of blatantly false information I've seen LLMs spew out in response to my very simple questions, I can very easily imagine one would get this basic information wrong somehow, although I did just Google it (as Gemini, and particularly the on-search-result-page version of it, seem to be the absolute worst of the common models in my experience), and it did get the answer right.

Most charitably, it was not a story of recent AI usage, but a few years ago when such a mistake would be more believable.

LLMs have had a reasoning setting for a while now. The reasoning is what allows them to go through and test each output to see which output/answer ends up aligning with known context the best (whether that be constraints that steer it away from some outputs, or information/hints that steer it towards the desired output).

The problem with the tutor's question is that they're going to AI to answer this question and not asking underlying questions like "what makes a square a square" and "what makes a rectangle a rectangle", then doing their own thinking to determine if a square's qualifications also fit inside the qualifications to be a rectangle.

Heck, even if you just feed the underlying into AI it'll reason the answer in a logical manner:

> In standard geometry, A square is a quadrilateral with 4 equal sides and 4 right angles (90° each). A rectangle is a quadrilateral with 4 right angles. Its opposite sides are equal and parallel, but all four sides do not have to be equal. So the key distinction is: Rectangle: four right angles. Square: four right angles plus four equal sides. > > That means every square is a rectangle, but not every rectangle is a square.

Asking LLMs to just give you answers is the real antipattern. But if you remember that the only thing it's good at is finding patterns given context/constraints, it can be an extremely good tool.

Also, I have a feeling the vast majority of the populus is using free versions of Claude or ChatGPT. Thankfully recently ChatGPT Free is GPT-5.6 Luna, but before the response qualities and capabilities of the free chat output were less than stellar.

With all due respect, this example is obsolete anecdata. I doubt the cruise ship was using Astra, for example, or Fable. Or really anything past GPT 3

If they are going to be put out of work by AI then they will be cheap to hire.