A course is content; teaching is what happens when the work comes back. This chapter is about that loop: the assignment you attach to a lesson, the hand-in a student sends, the feedback the AI writes within seconds from your own material, the Light the student raises when they are stuck, and the board that tells you who to call. It is the most human chapter in the manual, and the one with the fewest tools: your assistant can set assignments up, but reading a hand-in and answering it is yours.
A path such as Customers → Mentor Board names a group in the admin’s navigation and the page under it.
Concepts
An assignment asks for work. It is attached to exactly one scope — a lesson, a module or the whole course — and that placement decides where the student meets it and what it counts for: a lesson’s assignment sits on the lesson page and is part of finishing that lesson; a module’s gets its own page after the module’s lessons; a course’s shows on the course home. The assignment carries rating criteria: one text that both the AI and your staff read when they judge a hand-in. It also decides whether every hand-in must carry a Light and whether staff are emailed on each hand-in. New assignments are drafts until you publish them.
A hand-in is text, a file, or both. PDF, image, audio, video, or a plain-text file, up to 500 MB. A student can hand in more than once; each submission is rated on its own. Hand-ins are private to the student and your staff.
The rater answers first, from your teaching. When a hand-in lands, the AI rates it at once against the criteria. It reads what the student wrote and attached — text, PDFs and images directly, audio and video through their transcripts — and it reads your material in the assignment’s scope: lesson descriptions, text blocks, checklist items, uploaded files and the transcripts of your recordings. It also reads the student’s participant folder (the next chapter), so feedback remembers them. It never reads your mentor notes and never quotes them. The student sees the AI’s feedback immediately, before you have; that is deliberate, so nobody waits on you for a first answer.
Check-ins and Lights. An assignment that asks for a check-in light turns into a recurring self-report: with every hand-in the student says how it is going — green (clear and moving), yellow (slowed or unsure), red (stuck, needs help). The Light is the student’s own word, never derived from their text or progress; the AI acknowledges it and never grades it. A weekly check-in is a habit your program keeps, not a schedule the platform enforces. When you have read a Light you confirm it — one click that records who saw it and when — and an unconfirmed red is the thing the board exists to show you.
Finishing a lesson includes handing in. Completion is derived from content: a lesson with an assignment is complete when the student has opened it, ticked its checklist, watched its videos, and handed in. On a sequentially gated course that is what opens the next module. Adding an assignment later adds a requirement for students not yet done; it never un-completes anyone.
Hands-on
Ask for work
Mira says: “Add an assignment to the Week 1 lesson: hand in your time audit. Rate it on whether they listed at least five repeated tasks with a time each and picked one to automate. Ask for a check-in light. Publish it.”
omumu_assignment_create attaches it to the lesson with the description and criteria, omumu_assignment_update publishes it (the Courses & Content chapter has the tools). In the admin, the course overview’s Assignments card lists them; New Assignment takes the title, description, rating prompt, where it is attached, the check-in light switch and Email staff when a student hands in (on by default), and lands on the list as a draft; Edit has the Status switch that publishes.
Below the form, What the rater can read lists every item in the assignment’s scope — lesson descriptions, text, recordings, files, checklist items, links — as readable or not, with the reason: a recording with no transcript, a scanned PDF, a file linked from outside Omumu. Recordings can be transcribed from there; the page says how many of your plan’s minutes it will use before it starts. If the rater’s feedback misses your method, this list is where to look.
What the student does
On the lesson page, under the content and the checklist, the student sees the assignment’s brief, the Light choice if asked, a text box and a file field; Submit. The file uploads at once and shows its name. After submitting, the AI’s feedback appears on the submission page within seconds, with every earlier submission underneath. A student with a Light can hand in again each week and watch the thread grow.
What the AI does with a hand-in
Say Mira’s rating prompt asks for encouragement and a concrete next step. A student hands in a yellow check-in with a list of five tasks and a note that she is unsure which to automate. The feedback names the tasks, points at the easiest first win, acknowledges the yellow without arguing with it, and suggests a next step. A file-only hand-in of a PDF or a screenshot is read like text; a video is read through its transcript. The rater writes sourced facts into the student’s folder as it goes, so the next feedback starts from what it already knows.
Read, confirm, write
Mira opens the hand-in and reads the AI’s feedback above the form. She writes her own — Feedback and an optional score from 1 to 10 — and Save feedback; her words show above the AI’s. If the hand-in carries a Light, Confirm I have seen this Light records that a human has read it. Two separate actions today: writing feedback does not confirm the Light by itself.
Getting to the hand-in: Customers → Users, the student, Submissions, and the entry you want — each marked seen or not seen. There is no page yet that lists hand-ins waiting for feedback across students; the staff email on each hand-in is the queue in the meantime.
Run the board
Customers → Mentor Board shows every participant with their latest Light and its date, whether it is seen, days since their last hand-in, and progress, filtered by course. An unconfirmed red at the top of the list is who you call this week; a long gap since the last hand-in is who has gone quiet. Mark seen confirms a Light from the board.
Who hears what
Staff are emailed on every hand-in, with yellow and red called out in the subject — a per-assignment switch, on by default. The student is emailed when a staff member writes feedback (not for the AI’s), a site-wide setting under Settings → Business Details → Notifications that is on by default and can be turned off. For anything else — a tag when work comes in, a sequence after a review — the Submission Received, Rated and Reviewed triggers in the Automations chapter do it.
What you cannot do yet
Stated plainly: there are no tools for hand-ins — your assistant can create and publish assignments but cannot read a submission, write feedback or confirm a Light. There is no review queue across students; no due dates; no quizzes inside a course (quizzes live under Sales and marketing and are for finding out who someone is, not for checking what they learned). Delete on the people list disables the account rather than erasing the person’s data; permanent erasure is coming. And the Mentor Board counts you, the Headmaster, as a participant of your own courses.
Reference
No MCP tools cover this chapter’s capabilities yet.