Thread

1. radiorental · 1790264419 · focus · HN ↗

   This isn't binary, teachers have limited bandwidth and supplementing real teaching with AI supported research can work.

   Students need to understand what AI is and is not.

   Something got lost as we transitioned from parents telling their kids to 'go play and do not come back until dinner is ready' to us handholding & driving our kids from one social/sport activity to the next.

   While it causes fights with my kids, I blank refuse to do certain things for them (fix their internet connection, repair a toy or bike) I'll give them the tools and they know to ask when they get stuck.

   The act of learning through failure is a dying art these days. It's 'teach to the test', stressed out teachers, parents having too much to do compounded by taking on second, third jobs in this economy.

   Teaching our kids how to leverage AI is something parents should invest in. Its part of a broader education landscape.

   1. sandeepkd · 1790288587 · focus · HN ↗

      AI has its own benefits but its really a bad idea to use it in education as a primary source. LLMs have lots of information, reasoning ability, however they have their own limitations and by the nature these limitations just amplify.

      Few days ago my son was working on a problem which asked if square is a rectangle. Turns out his class teacher and external tutor (who was using AI) had different opinions on it. My wife consulted AI as well and she also came to the conclusion that square is not a rectangle cause AI told her so, AI told the same answer to external tutor. I understand that the context matters and if one probes AI more with additional context it may come up with correct answer, but this depends on the assumption that the person asking AI for something already knows the answer.

      1. bigstrat2003 · 1790298630 · focus · HN ↗

         LLMs have lots of information, but they have zero reasoning ability. That is why they are so bad at giving answers: they can't reason from one thing to another, but instead everything is a statistically likely result. If using an LLM for learning, you have to make sure to check everything it says or you will take in false info, guaranteed.

         1. judge2020 · 1790299580 · focus · HN ↗

            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.

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