AI Tools for Universities and Higher Education

The problem with most "AI for universities"

Most AI tools pitched at higher education are generic chatbots with a campus logo bolted on. They answer from the open internet, not from your syllabus, your lecture recordings, or the paper your lab actually published. So the answers sound confident and are frequently wrong for your course, your citations, your material. For a department teaching real classes or a lab building on real results, that's worse than no tool at all.

Scholarly takes the opposite approach. It's a source-grounded AI study and work workspace: people bring their own PDFs, lecture recordings, notes, slides, videos, and websites, and everything the AI produces — answers, flashcards, quizzes, study guides, notes, podcasts, video lectures, slides, research, meeting notes — is grounded in that material and cited back to it. Nothing is generated from thin air. If it's in the answer, it came from a source you gave it.

That single design choice is what makes Scholarly usable in higher education, where being right about a specific reading matters more than sounding fluent about everything.

Where source-grounded AI actually helps on campus

Higher education isn't one workflow — it's several groups doing very different work with the same underlying material. Scholarly is built to serve all of them from one place.

Students and cohorts turn a semester of readings and recorded lectures into things they can actually study from. Upload the lecture, and Scholarly can produce flashcards, quizzes, practice exams, and a clean study guide — all pulled from what the professor actually said, not a generic textbook. Struggling with a dense chapter? Generate an AI video lecture that walks through your PDF at your pace, or a podcast episode you can listen to on the walk to class. The point isn't to memorize sample sizes or page numbers — it's to genuinely understand the concepts and how they connect.

Instructors and teaching teams build course material faster without losing the plot. Drop in the readings for a unit and get a first-pass study guide, a set of practice questions grounded in the assigned text, or slides that follow your own lecture notes. Because everything traces back to the source, you can check it against what you meant to teach in seconds rather than rewriting from scratch.

Research labs move faster through the literature. Deep Research runs a structured investigation across the papers and notes you've loaded, synthesizing a cited answer instead of a plausible-sounding paragraph you then have to fact-check line by line. When the lab meets, AI Meeting Notes captures the discussion and turns it into structured, searchable notes — so the decisions and open questions from a two-hour meeting don't evaporate the moment everyone closes their laptops.

Departments get a shared, grounded knowledge base. Course packets, reading lists, recorded seminars, internal handbooks — loaded once, usable by everyone, with answers that cite the exact document. New TAs and incoming grad students stop asking the same questions in Slack because the material can now answer for itself.

The common thread: every output is built from your material. That's the moat. Scholarly doesn't win on streaks, notifications, or gamification — it wins by being genuinely correct on the real sources a university runs on.

One plan for a department, lab, or cohort

Individually subscribing and expensing an AI tool for thirty people is a nightmare of receipts, mismatched plans, and half the group stuck on a free tier. Scholarly Teams is built to end that. It puts a whole group — a lab, a department, a class, a cohort — on one plan with one bill.

When someone accepts their invite, every paid feature turns on for them automatically. Flashcards, quizzes, practice exams, notes, podcasts, AI video lectures, slides, Deep Research, AI Meeting Notes — all of it, immediately. There are no per-person paywalls and no upgrade prompts nagging your students or postdocs mid-task. They join, and they're working from your shared materials right away.

The best models, chosen by an admin

Teams unlock the top tier of AI models that Scholarly reserves for Enterprise — Opus 4.8, Fable 5, Sonnet 5, and GPT-5.5 — across every feature that uses AI. Admins decide which model tiers the team is allowed to use, so a department can dial in the balance between raw capability and cost that fits its budget. Everyone benefits from that decision at once, without touching a single individual setting.

Credits that are yours, every week

Teams run on weekly AI credits, and the model is deliberately simple: every member gets their own 450 credits per week. They're per person, not a shared pool — one teammate's heavy research week never eats into anyone else's, and everyone resets on the same weekly cadence. Credit costs track the real cost of the work, so a quick chat question is about one credit while a full AI video lecture is around 25. Nobody has to ration on behalf of the group.

Controls that make it manageable

Everything an admin needs to run the group lives in one place:

• Invite by email or link, with Member and Admin roles.
• Shared source libraries, so the whole team studies and works from the same materials.
• Team-wide model access controls that apply to every member at once.
• One central Stripe bill, with per-member usage broken down by feature and model so you can see exactly where the value is going.

For a lab PI, a course coordinator, or a department administrator, that means you set it up once and stop thinking about licenses.

What it costs

Scholarly Teams is $45 per seat / month, or $324 per seat / year — a 40% saving over paying monthly. Teams start at 3 seats and are self-serve up to 29, which comfortably covers most labs, seminars, and course teams. Need more — a whole department, multiple schools, a district, or invoicing instead of a card? Email hello@scholarly.so and we'll set you up. You can also compare tiers on the pricing page.

A realistic first month

Start small and let it prove itself. A single research lab or one course team makes an ideal pilot:

1. Start a team. Sign up, choose My team or business, set your seats, and invite people by email or link.
2. Load the real material. Put the semester's readings, the recorded lectures, the lab's key papers, and any internal handbooks into a shared library. This is the whole game — grounded AI is only as good as the sources behind it.
3. Let each group use what fits them. Students generate flashcards, practice exams, and video lectures from the lectures. The lab runs Deep Research across the papers and captures every meeting with AI Meeting Notes. Instructors spin up study guides and quizzes from their own notes.
4. Watch the usage. As an admin, check the per-member, per-feature breakdown to see what's actually delivering value — then expand seats or adjust model access with that evidence in hand.

Because it's all grounded in your own material and cited back to the source, the trust curve is short. People stop second-guessing the answers once they see the citation pointing at the exact reading they were assigned.

Why grounding beats a bigger model

It's tempting to think the answer to "AI for higher education" is just a more powerful model. But a frontier model that answers from the open web will still confidently misstate your specific course's framework, cite a paper your lab didn't use, or invent a definition your professor never gave. In academia, that's not a rounding error — it's the difference between a useful tool and a liability.

Scholarly's bet is that correctness on your real sources matters more than raw fluency, and that the way to get there is to ground every output in material the user actually provided — then hand your team the most capable models on top of that foundation. Understanding over memorization, sources over vibes, tools over engagement loops.

If you run a department, a lab, or a cohort and want AI that's actually right about your material, start there.

Ready to put your group on one plan? Scholarly for Teams unlocks every feature, the top-tier models, and weekly credits for each person — grounded in your own papers, lectures, and notes.