Serendipity35

Recently, I received notice from the music service Spotify that "We are updating our Terms of Use to make some updates to the dispute resolution provisions, including the Arbitration Agreement. You can read them  here." It is dense, and like many users, I gave it a cursory read-through and agreed. That is not the smartest thing to do, but I am hardly alone in doing it. In fact, some companies rely on that attitude. When users have been surveyed about their issues with terms of use/service, the same themes seem to always appear.

1. Most ToS run thousands of words in dense legal language. You wonder if that is not deliberate so that you DON'T review them but just quickly agree. One study found average users could answer only 40% of questions about an agreement they had just studied, and about 70% of people don't read them at all because they assume it's boilerplate. This is known as clickwrap, because you must take an affirmative action — click "I agree," check a box, or click "Accept" before you can proceed. Courts like this because there's clear proof you saw the terms

2. Why would you just agree to terms you haven't read? Because you can't negotiate. If you want to use the app, website, or device, you have to click "I agree." With "browsewrap agreements," you may be bound just by continuing to use a site, without ever seeing the terms. A browserwrap (AKA browsewrap) agreement is the most passive kind of online contract. It's the link you see at the very bottom of a website that says "Terms of Use" or "Privacy Policy." The site is claiming something like, "By just using this website — browsing, scrolling, downloading — you agree to our terms, even if you never clicked anything or read them." No "I Agree" button, no checkbox, no pop-up. Consent is implied by continued use.Courts often don't like them. (see note at bottom)

3. One-sided rights where the terms protect the company, not users. That might include language such as this:
Company can change the service, remove features, or delete your content/account at any time.
Purchases (especially digital goods, games, virtual items) are non-refundable and can be altered.
Company keeps broad license to use what you post

4. Some data and privacy items are hidden in the language. You click agree and later learn you consented to data collection, data sharing with third parties, tracking, targeted ads, and AI training on their content. The concern is trading away information and legal rights in a binding contract without realizing it.

5. Further, there may be legal rights you give up without noticing. Some common clauses:
mandatory arbitration and waiver of class-action rights
limits on liability if the service harms you or loses data
choice of law/venue far from where you live

6. Term can also change quietly and some companies reserve the right to update the terms at any time. Users aren't always notified clearly, so the rules you agreed to last year aren't the rules today.

7. Because the language is often deliberately vague, users aren't sure what will get them banned, demonetized, or suspended, or how appeals work. That leads to the feeling that enforcement is arbitrary.

Courts often don't like browsewrap 
because users generally don't know they're in a contract. A website is purporting to bind you by passive conduct unrelated to the terms themselves, like continuing to use the site. For that reason, U.S. courts have generally enforced browsewrap agreements only in limited cases — for example, against knowledgeable businesses — not against everyday individuals who had no reason to know the terms existed. To be enforceable at all, courts now look for two things: 1)  Conspicuous notice — Is the link to the terms placed where a reasonable person would see it, not hidden at the bottom in small light-gray text?  2) Actual or constructive knowledge — Did the user have reason to know the terms applied? This may also apply to what is known as the hybrid "Sign-in-wrap" that might read "By signing up, you agree to our Terms" next to the sign-up button.

 

 

 

 

The use of AI in schools is a big topic right now in the U/S. and probably in most of the developed world.

Estonia is the first country to integrate a customized chatbot nationwide across its high school system, moving away from bans to embrace AI-assisted learning.

The AI Leap Initiative began in September 2025 for 10th and 11th-grade students, reaching nearly 20,000 students and thousands of teachers. They partnered with OpenAI to use ChatGPT Edu and Google.

What is quite different about this version of a chatbot is that ChatGPT Edu uses the Socratic method. Instead of writing essays or giving direct answers, the customized AI acts like a coach. It guides students through problem-solving, reasoning, and critical thinking by asking questions rather than answering their questions.

That is an approach that will seem strange and probably frustrating at first to a user who is used to the reverse model. If the student asks "Why does the sky appear to be blue?" the response may be "What is your best guess at why it appears blue?"

Teachers receive premium access to AI tools for lesson planning, administrative tasks, and reducing workload, alongside professional "study circles" for training.

Estonia may seem to Westerners to be an odd choice for this experiment. I would not be surprised if an American had no idea where Estonia is located. Estonia, officially the Republic of Estonia, is a country in the Baltic region of Northern Europe bordered to the north by the Gulf of Finland across from Finland, to the west by the Baltic Sea across from Sweden, to the south by Latvia, and to the east by Russia. OpenAI noted that Estonia is already among the top 15 countries globally for ChatGPT usage. They have one active ChatGPT account for every four citizens, and aggregated usage data shows Estonians use ChatGPT primarily for tutoring and teaching, computer programming, and writing.

https://openai.com/index/estonia-schools-and-chatgpt/ 

From approximately 1970 through the 1990s, a number of emulated software packages, spanning many sources and with many motivations, portrayed microcomputers and game consoles as thinking machines. These programs attempted to predict the experience of creating autonomous or semi-autonomous virtual entities, or of playing games with the illusion of a contemplative and improvisational opponent. 

Now, those programs are curated for public enjoyment in one place. Introducing the Vintage Artificial Intelligence Collection, now available on archive.org. This curated collection of early computer software claims some aspect of artificial intelligence as a primary feature, allowing early interactions of humans and machines before things got a little strange. The collection includes a variety of programs intended as therapists, conversation partners, and opponents.

 

This was a new challenge for me. A classroom full of adults all with that look -- the drift between eager to learn and anxious about learning.  I was the instructor in  a reskilling program at the Carnegie Library in New Jersey. My job was to teach web development to displaced workers who had spent years in jobs in factories and retail. Their faces told that displacement part well. They were all sitting in the left seat on the runway and they had no idea how to fly an airplane.

I've taught across the learning spectrum — computer newbies who couldn't find the power button, college sophomores writing their first iOS app, certification candidates cramming Open Source Unix for a credential, and mid-career professionals trying to recompile their job opportunities through code. Each group arrives on their stairway landing with different fears and different metaphors they use to make sense of the landscape. The fundamental transaction is always the same: a person on the edge of their competence, looking st a gap, and asking someone they barely know to help them build a bridge

When you teach a true beginner, you can't start with abstractions. You have to find the concrete thing they already understand and build from there. In a Basic Computer Literacy course, I explained differences in database servers by comparing them  to a Diner during the breakfast rush — requests come in, the cook (server) handles them in turn, things get backed up (queues), the grill needs more people cooking (scaling and replication). The metaphor clicked because the Diner experience was shared among them all.  I wasn't just teaching them something new; I was using language and examples they all knew fluently.

AI does something remarkably similar. When I ask it to explain a Kubernetes deployment strategy, it doesn't launch into a whitepaper. It asks what I already know, or it guesses based on context, and it finds the analogical handhold. It's not always right — sometimes the metaphor is strained, sometimes it assumes I know Docker when I barely know what a container is. But the impulse is identical: meet the learner where they are, and move forward.

The intermediate learner is a different creature entirely. They've built something that works, which gives them confidence, but they haven't yet built something that fails spectacularly. Teaching them means letting them wander into the goo of complexity and then helping them get unstuck so they can find their way out. I taught iOS development to students who could get a button to change color but couldn't yet reason about memory management or delegation patterns. The temptation is to hand them the answer, but the actual teaching happens in the struggle —in the moment where they stare at structs and classes and realize the Xcode tool they used to create them now demands the language be understood.

AI struggles here. Not because it lacks the knowledge, but because it lacks the patience for productive frustration. When a student is circling a concept, a human teacher reads the confusion and lets the silence hang. AI, optimized for helpfulness, tends to fill that silence with the answer. It's like having a tutor who can't stand to watch you squirm and teaches very little. Squirming is often where the learning happens.

The advanced learner is easier in some ways and harder in others. By the time someone is studying for an Open Source Unix certification or architecting a web application, they know what they don't know. The teaching relationship becomes collaborative. You're not handing down knowledge from Olympus; you're comparing notes with someone who has a different vantage on the same mountain.

Here AI shines. It has read more documentation than any human could memorize. It can rattle off the difference between FreeBSD's jail system and Linux containers, cite the relevant man pages, and suggest edge cases I hadn't considered. It's like having a colleague who never sleeps and has perfect recall of every technical manual ever written. There is an incompleteness to it.  The advanced learner isn't just looking for information; they're looking for judgment. They want to know whether a particular architectural choice is wise, not just possible. They want someone to say, "We tried that in 2019 and it was a disaster." AI has no 2019. It has no disaster. It has only patterns and probabilities, a knowledge structure without synthesis --a long way away from wisdom.

The reskilling audience taught me the most about what learning actually requires. These were people who had been told by the economy that their skills no longer mattered, and now they were being asked to bet on themselves one more time. The emotional stakes were enormous. A syntax error wasn't just a syntax error; it was confirmation of their fear that they weren't smart enough for this. A working script wasn't just a working script; it was evidence that they could still grow, still adapt, still matter.

No AI can see that fear. No AI can offer the look across a desk that says, "I know this is hard, and I know you're scared, and I'm not going anywhere until you get it." The human teacher is not just a source of information; they're a witness. They bear testimony to the student's struggle and eventual triumph. They create a context in which failure is survivable because someone else is watching and believes it will be okay.

And yet AI is always there. It doesn't get tired, doesn't judge, doesn't carry the baggage of its own bad day into the interaction. A student who is embarrassed to ask a "stupid question" in front of a human will happily ask AI the same thing at 2 AM, phrasing it awkwardly, getting a patient answer, and trying again. There is no shame in the machine. For learners who have been corralled by shame before — by classrooms where they fell behind, by colleagues who made them feel small — AI is a sanctuary.

I've come to think of it this way: human teaching is like running with a partner. They set the pace, they encourage you when you flag, they notice when you're about to redline and suggest a slower target. They share the struggle. AI teaching is like a treadmill. It's always ready, always consistent, never judgmental. It will run with you for as long as you want, at whatever pace you choose. But if you trip, it won't catch you. If you need to stop and cry, it won't hand you a towel and tell you about the time it felt the same way.

The best learning probably happens in the space between — where AI handles the informational heavy lifting, the patient repetition, the infinite availability, and the human handles the witness, the belief, the judgment born of scars. I taught a zillion students over the years, and I remember the ones who were struggling more vividly than the ones who breezed through. I remember the looks of "Aha" in the BCL class when the diner metaphor clicked. I remember the woman in the reskilling program who cried when her first PHP form actually submitted a POST. AI will never remember any of it. But it will be there for the next one, and the next, and the next, patient and tireless and empty.

Perhaps that's the real comparison. We teach because we were taught. The chain of human knowledge is also a chain of human care. AI is a tool — an extraordinary one, a transformative one — but it's not a link in that chain. It's a convenience that returns what we ask of it, without ever understanding why the question mattered.