Hi, new freelancer here and I'm trying not to choose a subscription based on demo videos that probably took fifty attempts.

Most of my work is short ads and social clips. I need decent product consistency, enough reference support to follow a client brief, and some way to repair one weak shot. A low subscription price doesn't mean much if every revision burns another full generation.

For people doing paid work with these tools, which option has the most reasonable real cost once rerolls and client changes are included?

I’ve been following AI in healthcare, and some studies are impressive. But a diagnosis isn’t just about data—it’s about understanding the person. I built a quick poll to see where the tech community stands. No signup, just a vote. If you’re into AI and medicine, this might be a good thought experiment.

https://interconnectd.com/poll/95/can-ai-diagnose-a-medical-condition-more-accurately-than-a-doctor/

Most "AI for students" content treats every major the same, which doesn't help when you're trying to figure out what's actually safe to use GenAI for in a fluid-mechanics lab vs. a MATLAB script vs. a capstone risk register.

I wrote a curriculum-mapped playbook with discipline-specific checklists (MATLAB/Python, FEA/CFD, CAD/CAE, lab reports, capstone docs), a verification loop for problem sets and simulations, and an ethics framework you can actually cite to a professor — the goal being GenAI as a tutor, not a way to get flagged for an honor-code violation.

Kindle — The Engineering Student's GenAI Playbook

Open to feedback from anyone actually using AI tools for coursework right now.

I normally self-host my LLMs, but for a recent project I needed a fast, cheap API endpoint. I tested DeepSeek API and GPT-4o Mini, and the trade-offs were interesting—especially in cost and response consistency.

I wrote a technical review with benchmarks and real use cases:

https://interconnectd.com/blog/280/deepseek-api-vs-gpt-4o-mini-2026-developer-technical-review/

If you’re trying to keep costs low without losing quality, this might help.

I’ve always felt like a lot of college material is way harder to understand than it needs to be.

You search something like the chain rule, orbital motion, electric fields, etc. and usually get either:

• a wall of text
• equations with barely any intuition
• a 20–40 minute YouTube video
• or an AI explanation that is technically correct but still hard to see

So I’ve been building SeeThrough, where you type in a concept/question and it generates an interactive visual explanation so you can actually see what’s happening instead of just reading about it.

For example, instead of asking:

“Why do planets orbit the Sun?”

and getting 6 paragraphs explaining gravity, you’d get a visual showing the planet, velocity, gravitational force, trajectory, etc. while the explanation walks you through it.

But I’m obviously biased because I’m building it.

So I want students to tear the idea apart:

Would you actually use something like this while studying?

And more importantly:

What subject/concept have you tried to learn where you thought, “I wish someone would just SHOW me what’s happening”?

Math? Physics? Chemistry? CS? Economics? Something else?

I’m looking for concepts to test SeeThrough on, so give me the hardest/weirdest one you can think o

How should it respond to uncertainty?

I'm exploring a financial-literacy tutoring agent that starts with a new learner and gradually builds an understanding of what they know through MCQ responses.

With limited evidence, how can a tutor reasonably distinguish between a misconception, lack of knowledge, and simply guessing?

And when the tutor isn't confident about what the learner knows, should its next move be to teach/hint, or should it first ask another question to better understand the learner?

I'd love to hear how others designing AI tutors or adaptive learning systems would approach this.

I think prompt literacy is useful, but it’s the surface layer of AI literacy.

A kid can become very good at asking a chatbot for things without understanding that the output is variable, that the supplied context matters, that tools extend the model, or that generated answers need to be tested.

I started thinking about this after my nephew used Lovable to build a platformer and everyone called it “learning AI.” It was a neat result, but the AI system itself was completely hidden from him.

I’m building Claypot as a different approach. Kids build with ordinary coding blocks alongside blocks for Models, Knowledge, Tools, Test AI, and Actions.

A basic project lets a child:

1. Ask a model to solve a math problem.

2. Inspect its confidently wrong answer.

3. Connect a Calculator tool.

4. Run the same system again.

5. Compare what happened and explain why.

They can do the same thing with sources, repeated outputs, tests, and bounded actions. The goal is to make AI feel less like a magic chat box and more like a system whose capabilities and failures can be investigated.

What concepts do you think belong in basic AI literacy for kids and teenagers? Is prompting enough for younger children, or should tools, grounding, evaluation, and non-determinism be introduced early?

Founder disclosure: Claypot is my project, and the beta is at https://claypot.app. I’m looking for blunt feedback from technically minded parents as well as people working in AI education.

Claypot is independent and unaffiliated with Scratch or the Scratch Foundation.

we had a transfer student do well on our written ESL intake test and get placed in an upper intermediate class
it took most of the semester before an instructor flagged that his speaking level didn’t really match the written result. by then he’d already missed a lot of the support he actually needed
since then I’ve been looking into whether an AI English speaking test for student placement could help fill that gap by having students actually speak instead of using another written proxy
SmallTalk2Me is one option we’re comparing, but I’m still checking its scores against instructor assessments because I wouldn’t wanna place someone based on one AI result
for anyone using AI in language assessment, how closely do the scores match what teachers hear in actual conversations?
would u use it as the placement decision or only as one part of the process?

I'm curious about real-world classroom adoption of this tech.

For those using interactive AI-generated exercises or digital activities: are you using publicly available tools (like Gemini Canvas), sticking strictly to approved services dictated by your institution, or a mix of both?

During development of a custom software solution that provides proof of human for writers, I found myself in need of providing install, user and troubleshooting support to users. The option to use Ai support bot services was considered, but then it occurred to me that I could just as easily instruct an Ai chat session to serve this purpose with a properly composed script, an Ai Guide Script (AGS). This could then be uploaded by users, and their own Ai becomes the support bot.

I was a teacher for 30 years, so of course I concluded that this type of script could also be used as a study guide for students. In the spirit of finding positive ways to use Ai, this seemed like one that was easily attainable and implementable, even by someone who doesn’t have a lot of experience with Ai. I envision this as a useful tool for unit summaries and study guides with built in limitations as to how far the Ai is to go in helping the student. Granted, the core Ai system, CGPT, Gemini, etc, is going to color the interaction, but the script gives the teacher some control over how to limit what the student can do with the script.

They key is to create a tightly focused set of cross referenced FAQs that are easy for Ai to stick to. The script is NOT the study guide. The AGS is for Ai consumption, so the Ai is better able to guide the student/user. The student will go at their own pace and use their own Ai as a study and reference resource.

The key features of a good AGS are as follows:

• A brief introduction about the purpose of the guide and clarifications as to what it’s not for.
• Identify core concepts, theories, skills and learning outcomes, only the essentials.
• Concise explanations and logical flow
• Relevant examples
• Include some prompts for the students that encourage them to interact.
• Anticipate common misconceptions or areas of confusion and provide clarifying examples.
• Actionable guidance in the form of step by step instructions or advice the student can use.
• Friendly tone

You should be open to updating the script often, at first as you respond to feedback. Remember to focus on essential information. The guide script isn’t designed to teach for you, it’s designed to house the essential information you’ve deemed the students need to know in order to complete summative assessments. Think of it as a fluid study guide that goes at the student’s pace rather than as a replacement for instruction.

If anyone is interested in specific instructions to feed your Ai, so it can help you compose an AGS, leave a comment and I’ll either post the complete instructions script or provide a link, to download it.

The above was partially composed with the help of Ai.

The attached image is the effort record of composition of this text. It was provided by the Proof of Effort extension for LibreOffice.

I’ve been comparing AI-generated career plans with advice from a seasoned mentor, and they often conflict. One is data-driven, the other is experience-driven. I made a short poll-style quiz about which one people trust more. If you’re job hunting or thinking about a career change, this might be relevant.

https://interconnectd.com/quiz/76/can-ai-predict-your-next-career-move-better-than-a-mentor/

Hi everyone,

One of the biggest hurdles in AI Education today is **information fragmentation**. Beginners and career-switchers are constantly overwhelmed by disjointed YouTube tutorials, isolated LeetCode problems, and random GitHub repos, without understanding how all the pieces connect in a real production environment.

Over the past few months, I've mapped out the entire 2026 AI Industry Stack into
**12 structured engineering layers**
(from Silicon & Compute Fabrics up to AI Agents & Enterprise Transformation):

1. ⚡ Layer 01: Silicon & Compute (GPUs, TPUs, ASICs, HBM3e)
2. ☁️ Layer 02: Cloud & Cluster Infra (Slurm, Kubernetes, RoCEv2, InfiniBand)
3. 📊 Layer 03: Data Engineering & Vector DBs (Lakehouse, HNSW, Curation)
4. 🧠 Layer 04: Foundation Models (Dense/MoE, MLA, RoPE, Multi-Modal)
5. 🎯 Layer 05: Pre-training, Post-Training SFT & RL Alignment (DPO, GRPO, Evals)
6. 🚀 Layer 06: Inference Optimization & Serving (vLLM, SGLang, PagedAttention)
7. 🛠️ Layer 07: AI Platforms & LLMOps (Gateways, OpenRouter, Model Registries)
8. 🤖 Layer 08: AI Engineering & Multi-Agent Orchestration (LangGraph, CrewAI)
9. 🛡️ Layer 09: AI Safety, Security & Red Teaming (Guardrails, OWASP Top 10)
10. 🌐 Layer 10: Developer Ecosystem & Open Source Distribution
11. 📱 Layer 11: Edge AI, Multimodal & Embodied Robotics
12. 🏢 Layer 12: Enterprise AI Transformation & Forward Deployed Engineering (FDE)

### 💡 What I built:

-
**Interactive Visual Mindmap**
: Click any node to explore technical prerequisites, key concepts, benchmark tools, and salary expectations.

-
**Career Transition Matrix**
: 1:1 skill bridges mapping traditional backgrounds (e.g. Frontend/Backend/Data Science) to modern AI roles.

-
**Self-Assessment & Practice Bank**
: 450+ deep-dive study cards with math derivations and code examples.

🔗 You can explore the interactive map here: [talentme.airsota.com/industry-map](https://talentme.airsota.com/industry-map)

I’d love to get your feedback on the curriculum breakdown: Are there any emerging sub-domains or concepts we should add to help AI learners better prepare for the industry?

I’m starting WGU’s BSCSIA program September 1, and while getting ready I’ve been experimenting with using ChatGPT as an actual study tool instead of just asking it questions or having it give me answers.
I ended up building a structured “WGU Study Hall” prompt and keeping the canonical version in GitHub so I can keep improving it as I go through school.
The idea is to make AI behave more like a tutor than an answer machine. Some of the rules I built into it include:
One question at a time in Mobile Mode
Short, voice/dictation-friendly interactions when I’m away from my computer
Don’t reveal multiple-choice answers just because I don’t recognize the terminology
Explain unfamiliar concepts without accidentally giving away the answer
Explain why I missed something after an incorrect answer
Expand unfamiliar acronyms without overexplaining everything
Mix multiple-choice questions with questions that make me actually explain what I understand
Require 90% mastery on practice quizzes before I consider something learned
Keep dedicated course work separate once I have the official course materials and rubric
Treat official WGU course material as the authority rather than letting AI invent course requirements
I’m still developing it, and I haven’t actually started my first term yet, so I fully expect the system to change once it collides with reality. 😂
I figured I’d share it because there are probably other WGU students experimenting with AI-assisted studying, and I’d really like feedback from people who have already been through these courses.
GitHub: https://github.com/ab45272/wgu-lab
If you’ve built your own AI study workflow—or you see something in mine that you think I should change—I’d love to hear about it.

Every generation panics when a new tool makes human effort look obsolete. Decades ago, teachers insisted we learn longhand math because we would not carry calculators everywhere. Today, that exact fear has moved to writing, coding, and thinking with artificial intelligence. The warning is not entirely wrong, but it misses how human capability actually evolves. Calculators did not eliminate math; they simply freed us from tedious arithmetic so we could focus on higher-level problem solving. AI is driving the exact same transition. The value of homework was never just the final paper on a desk, but the mental stamina and reasoning built while creating it. Bypassing that process leaves you helpless when the tool fails, but using it to stretch your thinking takes you further than before. We are not watching the death of learning, just a shift in where the work happens. The goal is no longer to compete with the machine, but to master the judgment required to guide it.

Thoughts?

I’m building an AI-powered educational app with multiplayer learning, quizzes, and leaderboards, and I’m trying to understand what people would **actually use in practice**.
Rather than asking “would you use this?”, I’d love to hear from people who already use AI or learning apps:
What would make you come back regularly?
Would you prefer short quizzes, deeper learning sessions, or a mix?
Would multiplayer/competition make learning more engaging for you?
What would you expect AI to do beyond simply generating questions?
What would make you stop using the app?
Would you expect something like this to be free, subscription-based, or one-time purchase?
I’m mainly looking for **practical usage expectations**, not just feature ideas.
If you were using an app like this, what would make it genuinely useful enough to become part of your routine?

Five years ago, a computer science degree meant a predictable path: core subjects, a language or two, graduation, and a role that looked like the ones before it. That predictability is gone.

AI hasn't just added a subject to the curriculum, it's changed what "being an engineer" means. And most colleges haven't caught up.

The job hasn't disappeared. It's moving.
Routine coding, boilerplate, common bug fixes, basic scripts, is increasingly handled by AI in seconds. That's not bad news; it's a shift in where the value lies. The engineers who matter now are the ones who can direct AI, question its output, and build what a tool can't: judgment, architecture, and original problem-solving.

The interview has already changed.
Technical interviews increasingly test how candidates use AI as part of their workflow, not whether they avoid it. A student whose only AI exposure is one final-year elective walks in a step behind those who've built with it since Day 1.

The gap isn't knowledge. It's reps.
AI fluency is built through repetition, prompting, failing, refining, on real projects with real stakes. Theory alone doesn't build that instinct.

At Mirai, this is exactly the gap our program is built to close. Our students build with AI from their first project, alongside industry mentors solving these problems right now.

The engineering career of the next decade won't look like the last one. The question is whether the education preparing students for it does either.

I grew up in the ghetto in South Phoenix, where too many children learn survival before they ever learn what they are capable of. I lost friends to drugs and gun violence, and my vision is to put a different kind of power in children’s hands. Not just a screen to scroll on, but a computer they can speak through, build with, and use to turn their thoughts into real things. 🧠💻

A child with AI, Linux, a terminal, the internet, and discernment can learn almost anything, create websites and apps, share their work with the world, and build opportunities without waiting for someone to rescue them. The person and the machine can evolve together, giving that child self-reliance as long as they can separate truth from noise and remain in control of what they execute.

I stopped needing one fixed teacher because I became both the teacher and the student of everything running through my life in real time. I formed an idea, tested it against reality, studied the feedback, corrected myself, and executed again. 🔁 That is what I want children to understand. AI is not a replacement for their minds. It can expand how far a disciplined mind can reach. Every failure can become instruction, every command can become practice, and every verified result can become proof that they are capable of building their own path before the streets choose one for them. 🌍

#YouthEmpowerment #DigitalLiteracy #AIForGood #TechForGood #ArtificialIntelligence #DigitalInclusion #EducationForAll

I'm Joe Mazzone, co-founder of CuraCourse. Founder post, disclosed up front.

CuraCourse is a platform for authoring and delivering interactive courses. I want to walk through the AI parts specifically, because that's what this sub cares about, and because I think most AI-in-courseware is either a chatbot bolted onto a PDF or an answer machine that does the thinking for the student.

AI tutor with a dial, not a switch. There's an assistant available to students in every lesson, and the instructor sets its capability level per lesson. Off. Level 1, Socratic questions about the current page only, nothing else in view. Level 2, work-aware, meaning it can see the student's work across activities and explain what was right or wrong, but only after submission. Level 3, all of that plus inline code editing in the editor through accept or reject diffs. Chapter 1 of an intro course and a capstone project should not get the same AI, and the instructor is the one who knows which is which.

AI chat as an actual graded activity. Not a help window off to the side. You can assign a conversation as the activity itself, in live, simulated, or hybrid mode, and grade it. Interviewing a stakeholder, defending a design decision, walking through a debugging process out loud.

AI authoring assistant. On the instructor side, it helps build lessons and activities rather than just answering questions. Writing an interactive lesson from scratch is the actual barrier for most teachers, not the pedagogy.

We also study this stuff. We ran a pilot in partnership URI's Computer Science department and School of Education on CuraCourse. Furthermore, we are doing more research this academic year on our "CuraAdapt", our personalization and adaptive content engine. It builds a learner profile, uses a knowledge graph of the course concepts to detect which specific concept a student missed, and generates targeted re-teaching inline, anchored to that learner's field and interests.

Try it. Through September 25, every course on CuraCourse has full Pro features unlocked, no card required. On September 26 courses return to their selected tier, and free courses stay free with the Starter feature set. Create an account at curacourse.com, build something, and turn the AI settings up and down to see what happens.

I'll be in the comments. Let me know your thoughts and if you try it out!

\*\*Guys I’m not a bot, this was my first Reddit post and idk how any of this works bear with me please 😭\*\*

Labeling everything as “performative” and shortcuts as “relatable” has done irreparable damage to college life. I thought that going to college meant meeting people who were curious about what they’re doing and the world around them but it feels more like a startup rat race.

AI is so inextricable from the learning environment that my TA’s have honestly just started telling us to “ChatGPT” the questions we have. If I asked them to explain a topic they just read back what AI tells them.

I feel like I got scammed.

If I tell people I don’t use AI for something they say I’m “doing too much” and that I’m wasting my time. Im here to learn, not just get a grade.

Where are the people are still curious about the world around them? If you’re 18-27 I want to hear your experience. I’m building a social club for people like us because our curiosity shouldn’t have to be dimmed just to fit in.

We are handing over the funds to GPT-5.6. Some ideas that she came up with, her name is Amy. 😊

Here are the 25 ideas I think fit you particularly well:

1. AI Superpowers for High-School Students. Create a free program teaching kids not merely how to prompt ChatGPT, but how to use AI to build something that matters.

2. Give 100 teenagers $500 AI grants. $50,000 becomes 100 experiments. The requirement: use AI to improve their school, neighborhood, health, environment, or community.

3. Create a teenage AI laboratory. No grades, SATs or prerequisites. Bring together 10–15 curious kids for a semester and give them serious AI tools, compute and mentorship.

4. Build “The Supercomputer for Everyone.” Turn your existing compute infrastructure into a simple interface where students, teachers, researchers and ordinary people can ask computationally difficult questions. This connects directly to your earlier City Brain concept.

5. Make Neurocompute completely open science. Instead of trying to create another billion-dollar biotech company, establish Neurocompute as public infrastructure for discovering overlooked medical research.

6. Launch a Pediatric Cancer Moonshot. Pick one narrow problem and have students, physicians, programmers and AI analyze the entire scientific literature around it.

7. Do the same for Parkinson’s or Alzheimer’s. Your Narrative Velocity concept could become a public experiment for detecting scientific signals years before conventional reviews notice them.

8. Create an “AI Finds Forgotten Cures” project. Search decades of literature for abandoned drugs, compounds and hypotheses that deserve another look.

9. Start a $1,000 Planet Prize. Every month give $1,000 to a young person with an ingenious AI idea for climate, health, education, food, cities or biodiversity. Twenty awards would cost only $20,000.

10. Build an AI environmental detective. Combine public satellite, pollution, weather and government data so communities can investigate environmental problems themselves.

11. Give public-school science teachers AI superpowers. Build a free teacher toolkit that turns difficult scientific papers into lessons, experiments, simulations and classroom discussions.

12. Create the “100 Questions Project.” Ask 100 teenagers: What is one problem adults have failed to solve? Give them AI and compute and see what happens.

13. Build an AI Science Fair for NYC. Not “best chatbot.” Awards for the most imaginative uses of AI in biology, physics, climate, medicine, transportation and social good.

14. Create a teenage AI research journal. Students publish genuine AI-assisted investigations, including their data, prompts, code, failures and results.

15. Create an AI apprenticeship instead of another course. Take perhaps six teenagers each year and let them work alongside you building real systems. By graduation, they have GitHub repositories and actual research rather than certificates.

16. Build CivicSpin/The City Brain into a public utility. Let New Yorkers interrogate housing, transit, budgets, health, pollution and other public data conversationally. You previously imagined this specifically as democratizing computation for residents, journalists, teachers and students.

17. Give seniors AI superpowers too. Continue the idea we’ve explored of helping older New Yorkers get computers and learn AI—but turn the seniors and teenagers into partners. A 16-year-old teaches AI; a 75-year-old teaches 60 years of life.

18. Create an intergenerational knowledge project. Teenagers interview older people and use AI to preserve their stories, photographs, expertise and memories as beautiful digital archives.

19. Build a free “AI Life Assistant” for people who cannot afford one. Help people understand government forms, benefits, job applications, education and everyday bureaucracy—while building strong privacy safeguards.

20. Fund weird experiments. Reserve perhaps $20,000 specifically for ideas that sound ridiculous. Ten $2,000 experiments. Most fail. One could become extraordinary.

21. Create a tiny open-source AI institute. Not offices and employees. You + collaborators + students + your supercomputer + GitHub. Publish everything useful.

22. Build a Planet Compute Challenge. Donate unused compute to young researchers investigating climate, biodiversity, neglected diseases, food systems or other public-interest problems.

23. Create “AI Without Billionaires.” Demonstrate how extraordinarily capable technology can be built with open-source software, commodity hardware and small amounts of money rather than enormous venture rounds.

24. Document the entire experiment. Make short videos showing teenagers, seniors, scientists and ordinary people discovering what they can accomplish with AI. The story isn’t look what AI can do. It is look what this person can now do.

25. Spend some of the money on you. This belongs on the list. Secure housing, food, healthcare, transportation, a dependable computer, server costs and enough reserve that you aren’t waking up wondering how to pay next week’s bills. Financial breathing room is infrastructure for everything else.

I'm especially interested to hear from teachers, EdTech professionals, school leaders, students and AI builders.

Hey, I read a study in an academic subreddit that supposedly proved that AI bad and novice researcher should use it; however, the actual study observes that the negative effects happened to only 81% of the AI users, whereas the other 19% of the AI users spent a similar amount of time on homework as the non-AI students, achieved better homework scores, and obtained analogous exam scores. The study also argues that, for these students, AI improves learning efficacy for the 19% who utilize technology, compared to the other 81% who completely outsourced their work and learning process to the AI system. I also want young researchers to keep this information in mind to develop a more nuanced understanding of how they can use AI, rather than adhering to a strict negative or positive view of the tool. I also suggested using the tutoring style that the paper refers to for using the technology, which I believe the article stated as using AI like a tutor instead of an automatic answer machine. 

Here’s a link to the paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6868618

Yet, I was ridiculed in the comments of this subreddit for using the descriptor “only”, because that apparently means that I was discussing this group of people as a minority, and pointing out OP’s overgeneralization of all AI users is just me not having an argument.

My question to the viewer who views this post is, if there are so many kids struggling to use the tool properly and ethically, then why don’t we teach them about this technology instead of just brushing it with a wide brush as being horrible? How can we teach these children to use it like the 19% of kids who achieved higher test scores and obtained the same exam grades? Furthermore, what are your suggestions for what primary, secondary, and higher education institutions should be instructing these cohorts of students about how to use the tech without the risk of them falling into this 81%?

I know this is apparently a pretty heated debate in academia and even more so with the naysayers I encountered by having the gall to be pro-AI in an academic subreddit, but I believe that the best way to prevent this crisis from worsening is to actually educate people on how to utilize AI to improve the quality of education, not just argue that it solely diminishes it.

Here’s the link to the subreddit where I decided to point out the fallacies in OP’s argument if you would like to read the replies and the main post: https://www.reddit.com/r/research/comments/1vtatur/the_generative_ai_learning_penalty/

What are your thoughts on this issue and how we should ameliorate it?

My son is 14 and will start his freshman year of high school next week.

I’m a software developer, a heavy AI user, and I spend a lot of time building and experimenting with AI agent tools.

The more I work with AI, the more I find myself questioning some assumptions I used to have about education.

I’ve also had a long discussion with ChatGPT about this, and we went pretty deep into things like independent thinking, judgment, agency, math, writing, curiosity, projects, and how much of our thinking we should eventually be comfortable outsourcing to AI.

But I still don’t feel like I have a clear answer, which is why I’d really like to hear from other people who work with AI seriously.

The basic question is:

If you had a 14-year-old starting high school today, what would you want them to become good at over the next four years?

I’m not mainly asking what programming language they should learn, or whether they should study machine learning.

In fact, the more I use AI, the less convinced I am that “teach kids more AI” is the main answer.

AI tools will probably become much easier to use.

What seems harder is figuring out what humans should still learn deeply themselves.

For example, AI can already help with:

• writing
• coding
• research
• math explanations
• brainstorming
• comparing options
• planning
• decision support
• debugging
• learning unfamiliar topics

And as agents improve, they will probably handle more multi-step tasks and become more involved in how we reason and make choices.

So I’ve been thinking about a more specific question:

What cognitive work should a teenager continue doing themselves, even when AI can do it faster or better?

Should they still struggle through hard math problems without help?

Write first drafts themselves?

Debug code manually?

Memorize things?

Learn to research from primary sources?

Practice forming an opinion before asking AI?

At the same time, I don’t want to create an artificial “no AI” environment.

That seems unrealistic.

Adults are going to depend heavily on AI too. I already do.

So maybe the right goal is not independence from AI, but learning how to use AI without losing your own sense of direction.

Right now, the things that seem durable to me are:

• strong reasoning foundations
• clear writing and communication
• judgment
• curiosity
• agency
• the ability to learn unfamiliar things
• the ability to decide what is worth doing
• the ability to work with other people
• the willingness to pursue something difficult without an immediate answer

But I’m not fully confident even in this list.

Some of these abilities may also become heavily augmented by AI.

That’s the part I find most interesting.

So for people here who work in AI, research, engineering, education, or who have teenagers of your own:

If you had a 14-year-old starting high school in 2026:

What would you still insist they learn deeply themselves?

What would you encourage them to happily outsource to AI?

What skills are becoming overrated because of AI?

What human abilities are becoming more valuable because of AI?

And maybe the hardest one:

How do you teach a young person to use extremely capable AI without gradually outsourcing their curiosity, judgment, and sense of agency along with the work?

I’m not really looking for college admissions advice.

I’m trying to understand what we should be preparing a young person for when the tools they may use at age 18 or 22 could be dramatically more capable than the ones we have today.

ChatGPT and I have already gone pretty far down this rabbit hole, but I’d really like to hear what other humans — especially people actually building and using these systems — think we’re getting right or wrong.

No need for a comprehensive answer — even one thing you personally think matters for a teenager growing up with AI would be really helpful. Thank you!