Most education platforms show you a landing page, a pricing table, and a “trust us” button. We’re going to do something different: show you exactly what the product looks like, with real interface mockups, so you can decide for yourself whether this is how you want to learn.
Update: Since this article was published, the primary AI Uni experience is now the web classroom at app.aiuni.tech — with interactive components, explorable explanations, and prescribed lesson flows. The Claude connector remains available for students who prefer Claude’s native interface.
It’s a conversation, not a classroom
There are no lecture videos at AI Uni. No pre-recorded slides. No passive watching. Every session is a live conversation between you and an AI tutor in a chat interface. The tutor asks questions, presents exercises, evaluates your work, and adapts to how you’re doing — all in real time.
Here’s what that looks like. This is Session 4 of AI-101 (AI Fluency), where you learn to evaluate AI-generated content for factual accuracy:
Look at what’s happening here. The tutor doesn’t start with a lecture about AI hallucination. It starts with a recall question from the previous session — forcing you to retrieve what you learned rather than passively re-read it. Then it presents a piece of AI-generated content and asks you to evaluate it. The student has to identify specific claims, figure out which ones are verifiable, and flag the suspicious ones.
This is Session 4 of the first course every student takes. No coding. No special tools. Just critical thinking applied to AI output — the single most important skill for anyone working in an AI-powered economy.
Six ways your tutor teaches
The AI tutor isn’t running a script. It shifts between six teaching modes depending on what you need at that moment:
- Socratic — Asks questions that lead you to the answer. The default for new concepts.
- Scaffolded — Gives you structure (templates, hints, checklists) that fades as you get stronger.
- Review — Evaluates your work against specific criteria. Real feedback, not “good job.”
- Retrieval — Quick recall questions at session start. Keeps old knowledge sharp without flashcards.
- Direct — Sometimes you just need to be told how something works. Used sparingly.
- Reflective — “What did you learn? What was hard?” Builds the skill of thinking about your own thinking.
In the session mockup above, the tutor opens in Retrieval mode (the warm-up recall question), then shifts to Review mode — presenting AI-generated content and asking the student to evaluate it against a specific framework. The student doesn’t notice the shift. It just feels like a good teacher who always knows the right move.
Your portfolio grows with every course
AI Uni doesn’t give you a certificate when you finish. It gives you something better: a portfolio of real projects that employers can actually evaluate. Every course adds a project to your portfolio site. By the time you graduate, you have real portfolio projects — not a PDF credential, but proof you can do the work.
Here’s what a student portfolio looks like mid-program:
The featured project here is a bug triage dashboard — a capstone from the Software Development major. Below it: an AI workflow audit (Foundations capstone) and a Slack bot pipeline (Major course). These aren’t hypothetical assignments. They’re deployed, working projects with real data visualizations, real API integrations, and real code.
The portfolio site itself is a project too — students build and deploy it in Session 2 of the first course, then add to it as they complete each course. By graduation, an employer visiting alexchen.dev sees real projects with live demos, not a line on a resume that says “completed AI bootcamp.”
Everything else: Library, Academic Record, Dashboard
The classroom and portfolio are the core of AI Uni, but four other surfaces round out the experience:
Dashboard. Your home base. Shows your current course, progress percentage, which session is up next, and recent completions. It’s a personalized greeting — “Welcome back, Alex” — not a generic menu.
Reading Library. Between sessions, your tutor generates personalized reading briefs — short documents that recap what you covered, preview what’s coming next, and link to curated articles relevant to your progress. They show up as an inbox with unread indicators, organized by course. Not homework — reinforcement. Read them during your commute or over lunch.
Academic Record. This isn’t a checklist of completed sessions. Each completed session expands to show evaluation criteria (which you met, which you didn’t), the tutor’s written assessment, and links to your submitted work. Think of it as a real academic transcript — specific, detailed, and useful for demonstrating what you actually learned.
Course Map. A full view of every session in your current course — what’s completed, what’s current, what’s locked. Gives you a clear view of the path ahead so you always know where you are in the program.
What it costs
AI Uni starts free. Your first two sessions are on us — no credit card, no commitment. After that, AI Uni Pro is $39/month for full access to all live courses. Bring your own Claude subscription (~$20/mo) and you’re learning for about $59/month total.
For context: a four-year university costs $100,000+. A coding bootcamp runs $7,000–$16,000 for one skill. AI Uni costs less per month than most people’s streaming subscriptions — and you graduate with real portfolio projects, not a certificate that sits in a drawer.
This isn’t about being cheap. It’s about being honest: the AI economy rewards people who can demonstrate skills, not people who can show they sat through lectures. A portfolio beats a diploma every time.
See it for yourself
Everything in this article is live in the product right now. Start with 2 free sessions — you’ll be in the classroom within 5 minutes, working through a real exercise with your AI tutor. No credit card. No sales call. Just sit down and learn.
Try 2 Free Sessions