Uedu is an AI-mediated learning platform running in production across higher education in Taiwan. This page is the plain description of what you can run in your own course: no pricing table, no trial period, and no code to write.
Most AI teaching tools stop after the first box. The reason this platform exists is the third one.
In ordinary sentences, in your own language. Attach your own readings so the assistant answers from them and cites the page.
Not a separate chatbot on the side — it sits alongside the quizzes, worksheets, forums and recordings that make up the course.
Dialogue is classified by cognitive level across the semester. That is a different question from how often students logged in.
You describe the teaching behaviour you want in plain language. There is nothing to install and no code to write.
Each course gets its own assistant. You write its persona and guidance rules as free text — how it should answer, what it should refuse, which sources it should prefer. Students join with a course code.
Upload PDFs, slides and documents to a course. The assistant retrieves from them and cites the file and page it used, so students can check the claim against your material rather than trusting the model.
Optional modes for a course. In Socratic mode the assistant leads with questions instead of answers; in debate mode it argues a position so students have to respond to it. Both work on topics you set for your own material.
Any student can create their own assistant with the same editor — writing the instructions, not just consuming them. Several courses use this as the assignment itself.
The assistant is not a standalone chatbot. It sits inside the coursework students already have to do.
Generate drafts from your material, then edit them. You decide what ships — nothing is published to students without your review.
Course and campus-wide boards, with an optional scoring scheme that weighs semantic quality and peer response rather than post count.
In-platform surveys with scheduled opening and closing. Research consent, where it applies, is versioned and withdrawable.
Record audio, screen, or webcam during class; transcripts are generated afterwards. Sharing with students is off unless you turn it on.
Aida works across a student's whole programme rather than one course, and is deliberately Socratic: it asks rather than answers. Course assistants default to answering directly — the two are meant to feel different.
The part most tools skip. Usage counts tell you who showed up; these tell you what kind of thinking happened.
Dialogue is classified by cognitive level and tracked across the semester, per student and per class. This is the measure behind several of our published results.
A per-student view across conversations, assessments and participation, exportable for the student's own use.
Twenty-three research-grade cognitive tasks — attention, working memory, executive function — with millisecond timing, for courses and studies that need a measured baseline.
Three open psychometric instruments (Holland RIASEC, IPIP Big Five, OEJTS) that students take for themselves. Results belong to the student, not to your gradebook.
The decisions that usually get made for you by a vendor.
A free default is provided. A course can instead use its own API key for OpenAI, Anthropic or xAI models — your key, your account, your usage. Background analysis never runs on your key.
A teaching activity where students compare two anonymous models on the same question and vote before the identities are revealed. The resulting rating is student preference, not a capability benchmark, and is labelled that way to students.
The platform interface is translated into 10 languages. Students converse in whatever language they write in.
Research export is de-identified on the way out, logged, and gated on an ethics approval. There is no second route and no informal copy.
A public read API and an MCP server expose low-sensitivity data (public courses, institutions, publications) to your own tools. Student work is never exposed this way.
A self-built physiological sensing headset (EEG, fNIRS, PPG). Currently in design and fabrication — no data has been collected yet.
Retrieval scoped to a single student-created assistant, isolated from courses and from other channels. Planned, not yet shipped.
The platform is free to use and is not a commercial product. Model usage is covered either by your institution's own API key or by donation-funded capacity.
Run a course on the platform. You get the assistant, the coursework tools and the analytics for your own class. No fee, and no obligation to take part in any study.
Analyse de-identified data through the export route, or design a new study on top of a running course. Co-authorship is the normal arrangement.
Usually not — and this is the part that surprises people. The platform holds an umbrella research-ethics protocol, and data governance under it is the platform's responsibility rather than yours. Three cases, so you can tell which one you are in.
De-identified export of data already collected on the platform runs under the platform's umbrella protocol (NTU-REC 202507EM058). Consent, de-identification and the export route are the platform's responsibility, not yours. We provide a provenance statement you can cite in your methods section.
A new instrument, a new sensor, an external questionnaire, or any question put to students that the protocol does not already cover requires an amendment before it starts. Amendments are batched, so tell us early rather than at the point you need the data.
Some institutions require their own registration or approval regardless of where data was collected, and most journals require an ethics statement. Those obligations sit with you — but they are usually satisfied by citing the umbrella protocol rather than by running a fresh review.
Research participation is always separate from platform use, and never affects a student's grade. Students who decline or withdraw keep full access to their courses. Full policy: research project governance.
Worth reading before you invest a semester in it.
One paragraph is enough: the course, roughly how many students, and what you want the AI to do. We will tell you honestly whether the platform fits — including when it does not.
Uedu is developed by Chia-Kai Chang, Assistant Professor at the Center for General Education, National Central University, Taiwan, and is adopted by institutions beyond it. Research on the platform runs under an umbrella ethics approval.
International overview · Publications · Developer API · uedu.tw