An integrated workspace where reading, planning and writing are allowed to happen in one place. The work becomes its own evidence of learning, and the process exposes the misuse of AI.
Coursework was built on an assumption: that a finished document reflects the mind that produced it. That cracked with the internet, diploma mills and plagiarism — and tools like Turnitin emerged to stop academic misconduct. Universities now face their second major challenge: spotting and stopping AI misuse. The institutional response is failing — detection is an unwinnable arms race, and the fallback of exam halls throws away everything authentic about true academic writing.
No tool can reliably tell whether a polished document came from a person or a machine, and the false positives erode trust in universities and damage tutor–student relationships. So Rheton doesn't try to police the output; it captures the whole process of producing the work.
When the journey is visible and attributed, the work becomes its own evidence. There's no quiet gap left for a chatbot to fill, and nothing to detect after the fact.
Rheton brings the complete research-and-writing lifecycle into a single collaborative workspace. Students read and annotate sources, plan together, draft in the open, critique each other's work, and submit — with every contribution attributed and every tutor comment held in context, right where it belongs.
Every contribution is captured as it's made, and that record is presented to the marker when they grade the work — the proof of collaborative learning, built in.
Peer and tutor critique runs through every stage — in the margins of the reading, across the plan, alongside the draft, and into the mark — mirroring the habits of a real research community.
Rheton records who wrote what. Tutors see real contribution at a glance, with individual dashboards for each student. This reveals the often messy, confusing question of who carried the argument and who went quiet. Contribution data and AI support point the marker straight to the passages that deserve a closer look.
Group tasks share one space. Students annotate the same sources, build on each other's notes, and critique each other's drafts. The everyday habits of real scholarship are made visible and assessable.
Keyword auto-linking weaves individual notes into a shared knowledge map, so a class's thinking connects. The result is a community of practice where students learn to read critically and respond to each other — exactly the skill AI was eroding.
AI turns from threat into instrument. It never writes or grades the essay — it directs the marker's attention to the most significant passages, turning contribution and process data into an individual marking dashboard for each student.
When a tutor's mark diverges beyond a set threshold, the work auto-flags for second marking — keeping judgement with the tutor while making it faster, fairer and more consistent.
Every institution is rewriting its assessment policy right now — under pressure, on deadline, without good tools. Detection has lost credibility. Reverting to exams doesn't scale to authentic work, and nobody wants it.
Rheton is the constructive third option: keep ambitious coursework that reflects real-world academic practice, and make it AI-proof by design. The category that replaces "detect-and-punish" is being defined this year. The window is open now.
Per-institution annual licensing, priced per active student or per cohort. Built on LTI 1.3, so it drops into Canvas, Moodle, Blackboard and the rest with no rip-and-replace. Grades flow back automatically into the LMS, and rosters provision themselves.
Land with a single department that's feeling the assessment pain most, prove it, and expand across the institution. Standards-based integration keeps switching costs low for the buyer and retention high for us.
An idea-stage company with working proof on the pieces most likely to break. Three functional prototypes validate the core, and the production architecture is specified.