AI-Powered CO/PO Design
Upload the syllabus you were already given. Approve the draft in three minutes.
You are not being asked to author a new document. The syllabus exists and the university prescribed it. Udgam reads it and produces the course outcomes, the Bloom's mapping and the CO–PO matrix — for you to correct, not to write.
- Under 5 minutes per course
- Every draft cites its source
- You hold every approval gate
Try it yourself
Pick a subject. Watch it draft.
Pick a subject and watch what happens.
Syllabus you already have — B.Tech CSE · Semester 3
- Unit 1 — Arrays, linked lists, stacks and queues. Representation and operations.
- Unit 2 — Sorting and searching. Complexity analysis, best/average/worst case.
- Unit 3 — Trees and graphs. Traversal, spanning trees, shortest path.
- Unit 4 — Hashing, collision resolution, design of efficient structures.
Nothing drafted yet. Choose a subject above.
A demonstration using pre-written content. In the product this runs against your own uploaded syllabus, and nothing reaches an official record without your approval and your HOD’s.
Your part in it
You appear at the end, and only where judgement is needed.
Upload what you already have
The university-prescribed syllabus, as PDF, DOCX or pasted text. Subject metadata takes about a minute. You are not authoring a new document.
Review the drafts
Course outcomes, the cognitive level of each, and a pre-filled outcome-to-programme matrix. Inline editing with a verb dropdown. Approve one at a time or approve all.
Send it for approval
Your head of department signs off before anything affects a calculation. Every change is versioned with an editor and a reason.
Target time for the whole loop: under five minutes per course. The demo above is the honest version of it — including the part where a draft cites its source so you can reject it in seconds.
Before anything is activated
Five checks run on every outcome.
Bloom's verb alignment
The action verb has to match the level claimed. Mismatches are flagged, not silently accepted.
Measurability
A higher-level outcome cannot rest on a vague verb like 'understand' or 'appreciate'.
Scope
Not so broad it describes the whole programme, not so narrow it describes a single lecture.
Duplication
Flags any outcome substantially identical to another in the same programme.
HOD review gate
For new courses, or significantly changed outcomes, your head of department approves before activation.
Version history
Every change is logged with a timestamp, an editor and a reason. Nothing changes silently.
The CO–PO matrix
Pre-filled, not blank. You are correcting, not constructing.
| PO1 Knowledge | PO2 Analysis | PO3 Design | PO4 Investigation | PO5 Tools | |
|---|---|---|---|---|---|
| CO1 Apply sorting algorithms | 3 | 2 | 1 | — | 2 |
| CO2 Analyse structure efficiency | 2 | 3 | 1 | 2 | — |
| CO3 Evaluate algorithm complexity | 2 | 3 | 2 | 3 | — |
| CO4 Design optimal structures | 1 | 2 | 3 | 2 | 3 |
| CO5 Solve graph traversal problems | 2 | 3 | 1 | — | 2 |
| CO6 Implement tree operations | 2 | 2 | 3 | 1 | 3 |
The workflow
- AI generates suggested weights from the outcome text and the PO descriptions
- You review and adjust the cells you disagree with
- Your HOD approves before it affects any attainment calculation
- Every version is retained, with who changed what and why
Inter-rater calibration
If a colleague in your department maps the same topic differently, the platform flags the discrepancy and asks your HOD to resolve it. This is the mechanism that stops two sections of the same course producing incomparable attainment numbers.
If you are affiliated to a state university
You are not being asked to overwrite a prescribed curriculum.
University outcomes
Prescribed by your affiliating university, imported from a library or entered once by your institution admin. These cannot be modified — only supplemented. They are what gets reported upward to the university.
Institution outcomes
Supplementary outcomes your department defines for the same course — local industry context, elective depth, laboratory or project components. These are what gets reported to NBA and NAAC.
Both layers contribute to attainment, are tracked separately, and are combined in the accreditation dashboard. Nothing you do at Layer 2 puts your university affiliation at risk.
The same principle, everywhere else
Four more things AI drafts and you approve.
| What you provide | What comes back | What you do |
|---|---|---|
| A course outcome, a level and a topic | Five to ten questions written at exactly that cognitive level | Pick the ones that fit, edit the rest, discard what doesn't work |
| A course outcome, a level and an assessment type | A full rubric with four to six criteria and performance descriptors | Adjust descriptors, add or remove criteria, approve |
| An attainment gap and a root cause | Three evidence-based corrective actions, drawn from what worked elsewhere on the platform | Choose one, set a date, attach evidence when done |
| A finished assessment | A balance report showing the distribution across cognitive levels | Rebalance if more than 70% of marks sit at the lowest two levels |
target time for you to review and approve a full set of course outcomes
course outcomes per course, configurable — five to six is the default
stream-specific outcome libraries, from engineering and management through to law, pharmacy and agriculture
AI eliminates burden; humans retain authority. AI is a first-draft engine. Authority remains human.
10X Faster
Accreditation in a click. Projects in a semester. Placements in a season.
Co-working
Universities and industry, working on the same problems at the same time.
Placements
Not more drives. Better matches. Lasting placements.
Register interest
Send us one syllabus.
Tell us what you teach and we will show you the course outcomes the platform would draft from it. No commitment, and no obligation to like the result.