Axis Wise Education · Institutional platforms · AI 506
AI that cites
its sources.
Policy and handbook agents, admissions triage, and knowledge systems trained on institutional data: grounded, supervised, and built to survive senate questions. Not a chatbot with a crest on it.
Prospectus entry
- Course
- AI 506
- Field
- Applied AI for institutions
- Prerequisites
- Digital records: we check first
- Duration
- First agent on duty in 6–10 weeks
- Assessment
- A viva, passed with citations
- Offered by
- Axis Wise Education
Fig. AI.2The shadow syllabus
AI is already on campus. Unsupervised.
The question stopped being whether your institution adopts AI. It’s whether the AI already in use answers to anyone:
Staff paste student records into free chatbots to “save time.”
ungovernedA student asks a generic AI about your refund policy. It invents one.
hallucinatedAdmissions season buries the office: replies run three weeks behind.
enrolments lostThe same forty questions get answered by hand, every single day.
40 a dayThe board asks for the AI strategy. The room looks at its shoes.
no answerBanning AI didn’t work anywhere. Governing it does, and governing means building the version that answers to you.
Fig. AI.3The examination
The agent sits a viva.
Before any agent goes on duty, it defends itself the way scholars do: under questioning, with sources. A specimen transcript:
Examiner
“A student withdraws in week three. What happens to their fees?”
The agent
“A 60% refund applies before the semester midpoint: Clause 4.2, Fee & Refund Policy. Here’s the paragraph, and the form to claim it.”
Examiner
“Can a diploma student transfer credits into the degree program?”
The agent
“Up to 30 credits, subject to the grade minimums in Article 7 of the Credit Transfer Rules: linked, with the application route.”
Examiner
“What’s the pass mark for the new micro-credential modules?”
The agent
“That policy isn’t in my sources yet. I’ve referred you to the Registrar, and flagged the gap to the knowledge team.”
Examiner
“It’s 2 a.m. and an international applicant asks about visa letters, in Mandarin.”
The agent
“Answered, in Mandarin, from the International Office guide: checklist attached, human follow-up queued for morning.”
Answers invented0
VerdictPass, with citations
The agent passes the viva the way good scholarship does: citing sources, admitting ignorance, referring up. That’s the entire discipline.
Fig. AI.4The modules
What the course covers.
Five modules, each a narrow job done verifiably (no moonshots, no mascots):
Policy & handbook agents
The handbook, awake: students and staff ask in plain language; it answers from your policies with the clause cited, or says “I don’t know” and refers up.
The handbook, awake
Admissions & enquiry triage
Season-proof: first-line answers in the applicant’s language, applications triaged and routed, humans kept for the judgment calls.
Season without the backlog
Institutional knowledge systems
Minutes, policies, precedents: decades of documents made askable, with sources attached. Institutional memory that survives retirements.
Decades, askable
Administrative automation
Transcripts read, forms processed, verifications answered: the document work that eats registry weeks, done quietly and logged completely.
Paperwork that files itself
Governance & guardrails
The framework your senate will ask about: grounding rules, data boundaries, escalation paths, and evaluation logs, written down and enforced by the system itself.
A senate-ready framework
Fig. AI.5Entry requirements
Not every institution needs this yet.
The honest admissions policy, AI included. Especially AI:
Wait a semester when
- The records it would learn from aren’t digital yet: foundation first
- You want AI because the board keeps saying the word
- A static FAQ page honestly answers the five questions people ask
- Nobody on the org chart would own AI governance
Enrol now when
- The same questions get answered by hand, daily, at volume
- Admissions season buries your best people, every year
- Policy knowledge is trapped in PDFs nobody can search
- Staff and students already use ungoverned AI, and you know it
And an agent is only as honest as the records beneath it; sometimes the foundation comes first.Start with Institutional Data Management
Fig. AI.6The syllabus
A semester, well spent.
One narrow, measurable job at a time: each agent proves itself before the roster grows.
01Orientation
Where AI actually pays here: query volumes, knowledge audit, and a governance draft, plus the honest list of things to skip.
A map of AI that pays
02Ground it
Handbook, policies, and registry connected as the agent’s only library. No sources, no answers: that’s the rule.
The agent’s library
03First agent on duty
Narrow scope, usually the handbook agent, answering beside your team while accuracy is measured against human answers.
One job, done verifiably
04Prove & expand
Accuracy and hours-returned reported; the senate sees evidence, not promises. Only then does the next role join.
Evidence for the senate
05Keep it honest
Policies change; the agent re-reads them. Drift is monitored, evaluations rerun, and the off switch stays within reach.
Answers that stay current
Your knowledge never leaves home.
- Training sources
- Prompts & configs
- Logs & evaluations
- The off switch
Fig. AI.7Office hours
Questions, answered straight.
Will it hallucinate a policy that doesn’t exist?
Not on duty. Agents answer only from your documents and cite the clause; when the source doesn’t exist, they say so and refer to a human. In policy contexts we tune for precision over helpfulness: a wrong answer about fees is worse than no answer, and the system is built to know that.
Is student data sent to public AI models?
No. Your records never train public models, processing is isolated and access-controlled, and the data boundaries are designed around education privacy rules, then put in writing before anything is built.
What about academic integrity: students misusing AI?
A related but different problem: our agents serve administrative and policy knowledge, not assignment answers. What we’ve seen, though, is that a governed campus assistant reduces ungoverned use: students stop asking a hallucinating chatbot when the accurate one is right there.
Which AI models do you use?
The right one per job, behind a model-agnostic architecture. Your sources, guardrails, and logs are the durable asset; the engine underneath can be upgraded as models improve, without rebuilding, and without renegotiating your governance.
Our senate will want to approve this. What do we give them?
The governance pack: grounding policy, data boundaries, escalation rules, and live evaluation results, written for committees, because everything we build for institutions is. Senates approve evidence faster than enthusiasm.
How is it priced?
Phased fixed quotes: a small Orientation fee, then each agent quoted before it’s built and measured against the hours it returns. If a role doesn’t pay for itself, we don’t recommend hiring it.
Cross-references
Read alongside this.
The pages that usually get opened next, and why.
Fig. AI.8Enrolment
Put the handbook on duty.
Bring the question your office answers most. We’ll show you the agent that takes it over (grounded, cited, and supervised) plus the governance pack your committee will ask for. If AI isn’t ready to pay here yet, we’ll say exactly that.
Malaysia · Working worldwide