It's actually worse than that because although LLMs are capable of a lot, they still hallucinate quite frequently and sometimes are just plain stupid.
Recently I was trying to manipulate data in Google Sheets and was using ChatGPT to help. In the beginning is was fantastic, I was very productive because I didn't have to stop and think about formulas, read crappy documentation, or analyze data transformation. ChatGPT just gave me the right answer in a split second, as long as I kept asking the right questions.
Then I stumbled upon a particular issue that wasn't really too complicated but ChatGPT could not give me a correct answer. Unknowingly I spent 3 days trying to fix problems with the solution and every time I got an answer that was slightly wrong, not in subtle ways.
I have 20 years experience as a software engineer and still, I continued to waste time in this loop. After 3 days I decided to apply my engineering skills and solved the matter in 30 min. Now I know the solution and it was way simpler than I thought.
What surprised me was how dumb the whole process was. My questions certainly weren't the problem - as bad as they possibly were, the solution wasn't too far off. Not only ChatGPT had become a crutch but it put me in a situation that no human-tutor would ever put me.
So removing the pain of quick interactions with a tutor has benefits but the technology is not quite ready to be considered as true guidance in forming (or even helping) someone's understanding of a subject.
I've been using ChatGPT a lot for general language but when it requires logical thinking it falls pretty flat.
I've experienced this exact frustration loop before, but I've also got a major counter-example.
I've been using GPT4 as a tool to interrogate lesson transcripts for a language I'm learning and mention in the prompt to specifically focus on things mentioned in the transcript, if it's not in the transcript, check the helper script I update as I move on through the process (which does sadly take up more and more context window) and figure out if my answer is in one of those previous lessons, and to not guess. Hallucinations are quite rare, I don't think I can name an egregious instance of it in the 25 lesson I've done of approximately 20 minutes in length each, though I'm sure it's happened.
It's also pretty good at suggesting drills based on the contents of the lesson, there are probably a whole bunch of lesson plans in the training data.
The end result has been progress at a pace I could only dream of previously, and it doesn't matter if a question is too basic because I'm asking a computer. There is zero concern of any question being embarrassing because it's only between me, GPT4, and the OAI engineer who happens across the conversaiton.
I absolutely have the same overall feeling, when the task at hand is related to text processing (including understanding and spinning off new takes or ideas from it).
But when the task at hand involves logical thinking, that's when I believe the LLMs of today are still a very much work in progress.
So I'm skeptical of trying to use them as tutors for now. I'm sure things will evolve quite quickly from now.
"I spent days trying to solve X by doing Y, then it turned out I could have solved it by doing Z instead" is an experience I've had countless times before LLMs were a thing. Sometimes you really do need these three days of stumbling before you can build up the confidence to do the easy solution.
(Then again, I don't know the specifics of your case.)
In addition, I feel like you build a sort of intuition after a while and detect when the conversation with the LLM has hit a dead end. When to stop and take a step back, think what it is you're trying to do and try to go down a different path. The LLM can even support with that, it's on the human to kick that off, at least at this point in time.
> Although I want to believe this problem is more exacerbated with LLMs than with human-tutors.
Well, yeah, but the central insight here is that LLMs enable a worse-is-better approach with a tighter feedback loop. They're not as good as a regular tutor, but a regular tutor costs money, gets impatient, is only available at set hours, etc.
Part of the appeal of learning-by-LLM is that you can get a flash of motivation at 2 AM and go "hey, I should totally learn about X, that would be cool!", open up ChatGPT, ask some very naive questions, and get just enough to get started.
(It's funny, when I wrote this yesterday I thought "this is an unrealistic example", and yet here I am, at 1 AM, asking ChatGPT a bunch of questions about Google's XLS. I'm pretty sure the answers I got were hallucinations, but at least it helped me formulate the questions for when I go to the mailing list.)
Recently I was trying to manipulate data in Google Sheets and was using ChatGPT to help. In the beginning is was fantastic, I was very productive because I didn't have to stop and think about formulas, read crappy documentation, or analyze data transformation. ChatGPT just gave me the right answer in a split second, as long as I kept asking the right questions.
Then I stumbled upon a particular issue that wasn't really too complicated but ChatGPT could not give me a correct answer. Unknowingly I spent 3 days trying to fix problems with the solution and every time I got an answer that was slightly wrong, not in subtle ways.
I have 20 years experience as a software engineer and still, I continued to waste time in this loop. After 3 days I decided to apply my engineering skills and solved the matter in 30 min. Now I know the solution and it was way simpler than I thought.
What surprised me was how dumb the whole process was. My questions certainly weren't the problem - as bad as they possibly were, the solution wasn't too far off. Not only ChatGPT had become a crutch but it put me in a situation that no human-tutor would ever put me.
So removing the pain of quick interactions with a tutor has benefits but the technology is not quite ready to be considered as true guidance in forming (or even helping) someone's understanding of a subject.
I've been using ChatGPT a lot for general language but when it requires logical thinking it falls pretty flat.