Agentic AI Training for Universities
Plan agentic AI training for university teaching, research, or administration.
We reply within 24 working hours. No commitment.
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Agentic AI, university: lecturer builds a research agent and a syllabus clause
// problems we solve
Problems we often find.
Students use AI for course work. Lecturers do not share 1 common rule across courses.
We write a syllabus clause for each course. The clause states what AI use is allowed, what is not, and the acknowledgement format for prompts used.
A literature review built with AI sometimes cites a source that does not exist.
A sourced literature-review agent only summarises documents a lecturer provides. The agent lists the source page, and the lecturer checks each claim one by one.
Student data and research respondent data have entered public AI tools before.
We write a data rule: student and respondent data do not enter public AI tools. Class projects use sample data or data that is already anonymised.
A campus administrative unit wants an agent for academic services, with no clear owner.
Every agent gets 1 owner, an action limit, and a human approval point. We write this plan before a unit uses the agent.
// how it works
The steps we run.
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Preparation
The university sends the number of lecturers and students, 2 sample courses, and any existing GenAI policy.
Output: A written agenda, 2 course cases, and a draft syllabus clause.Research and course agent
Each lecturer builds 1 agent: a sourced literature-review agent or a task assistant with an acknowledgement format. The lecturer writes its limits and test method.
Output: 1 agent per lecturer, with test notes and a task rubric.Course policy and data
Lecturers write the syllabus clause, the AI-use acknowledgement format, and the data rule for students and respondents.
Output: A syllabus clause per course and a data rule, ready for university approval.Test on 1 course
Lecturers use the agent and the clause on 1 course. We answer written questions within working hours.
Output: An evaluation note for 1 course and a clause revision.// what you get
A clear scope. Useful handover.
Plan agenticAgenticA way of working where an AI agent performs the routine steps and a person checks and approves the result.Open the glossary AI training for university teaching, research, or administration.
- Level and need mapping
- Project plan
- AgentAI agentAn AI program that performs work steps by itself, for example reading a message, drafting a reply, and recording the result.Open the glossary evaluation material
- Data and review rules
The format: lecturer material, 1 agent per lecturer, a syllabus clause template, and an AI-use acknowledgement format. It also includes a task rubric, and a student and respondent data rule.
// price and inclusions
What the price covers.
| Included | Not included | Billed apart |
|---|---|---|
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Ground rules
- Revision: 1 revision round for the agenda and course cases before day 1.
- Reschedule: free of charge with 5 working days of notice before day 1.
- Data and access: student and respondent data do not enter public AI tools. The agent, clause, and rubric become the property of the university.
- Warranty limit: we guarantee training and material match the agenda. Accreditation and student learning outcomes are not part of this guarantee.
What we guarantee
- We answer your message within 24 hours on a working day.
- Accounts, code, and data stay in your name from day 1.
- Every handover includes a test report or written notes.
- No renewal fee to Rama Digital. You pay domain, hosting, and third-party subscriptions to the vendor directly.
- A fixed price on this page is the price we invoice. A "From" price becomes fixed in the written quotation you approve.
// is this for you
Good fit for
- You represent a university, an institute, or a polytechnic.
- Your lecturers want AI assistants for course work.
- You want clear written rules for student data.
Not a fit for
- You represent another level. Open Agentic AI Training for Schools and Campuses.
What you prepare
- The number of participating lecturers and students.
- The date and the length of the programme.
- Laptops and internet access in the training room.
What the price excludes
- Participant devices and internet access.
- AI tool subscriptions.
How it differs: The senior-high programme uses school tasks, while this programme uses campus tasks.
// how this differs
Compare with other services.
| University | Senior High and Vocational | Company Team | |
|---|---|---|---|
| Duration | 1 day-1 semester | 1 day-1 semester | 2-5 hours |
| Price | From IDR 3.9M | From IDR 4.5M | From IDR 9.8M per batch |
| Result | A research and course agent, syllabus clauses, and student and respondent data rules | A class-group agent project with a scoring rubric | Practice sessions per department, with data rules and human review |
| When to pick | Participants are lecturers, and the task uses a university course | Participants are senior-high or vocational teachers, and the task uses a secondary-school class | Participants are campus staff outside teaching, such as administrative staff |
// evidence and sources
Evidence we checked.
The 2024 higher-education guide asks each university to set its own GenAI policy as part of academic policy. Lecturers write in the syllabus what is allowed, and students add an acknowledgement and the prompts they used in the citation style.
Directorate General of Higher Education, Ministry of Education, "Guide on Generative AI Use in Higher-Education Teaching" (2024)Read 12 September 2026The same guide names 4 main ethics topics for GenAI on campus: academic integrity, data security and protection, equity and accountability, and environmental impact.
Directorate General of Higher Education, Ministry of Education, "Guide on Generative AI Use in Higher-Education Teaching" (2024)Read 12 September 2026A trustworthy agent works in an agentic but deterministic way: its steps and limits are written, so its results can be repeated and checked.
Rama Digital, "Agentic, But Deterministic: How to Design a Trustworthy AI Agent"Read 12 September 2026
- Security, Privacy, and the Indonesian Data Law for Corporate AI Agents (ID)
- Agentic, But Deterministic: How to Design a Trustworthy AI Agent (ID)
- Human Approval Matrix and Governance for a Codex Pilot (ID)
Owner: Rama Digital team · Updated 12 September 2026
// definitions
Terms we use.
- AI-use acknowledgement
- An AI-use acknowledgement is a note on a task that names the part AI helped with and the prompts used, in the course citation style.
- Sourced literature-review agent
- A sourced literature-review agent is an AI agent that only summarises documents a lecturer provides, and lists the source page for each claim.
- Agentic
- Agentic describes work where a routine task runs through an AI agent, and a lecturer then checks and approves the result. See in the glossary
- MCP
- MCP is a standard rule set that lets an AI program use campus tools and data under clear permission. See in the glossary
FAQ
What is Agentic AI Training for Universities?
Agentic AI Training for Universities helps lecturers build research and course agents with clear rules. Lecturers get a sourced literature-review agent, syllabus clauses, and an AI-use acknowledgement format. The price starts at IDR 3.9M, and the duration follows the scope, from 1 day to 1 semester.
Can students use AI for course work after this program?
Yes, within the syllabus clause each lecturer writes during this program. The clause states which tasks allow AI, the limits, and the AI-use acknowledgement format. Each lecturer sets the rule for their own course, because policy can differ across departments and courses.
How do students write an AI-use acknowledgement on a task?
Students write which part of the task used AI help and which prompts they used, in the course citation style. This format is the AI-use acknowledgement. Lecturers receive a template during the program, then adjust it to their course citation style and task type.
Can this program serve research and campus administration?
This program trains agents for research and course work, including a sourced literature-review agent. An agent for administrative services, such as enrolment or a helpdesk, uses the AI Agent Ops Install service instead. That service covers installation and access rules that differ from this training.
Does the material follow the 2024 higher-education GenAI guide?
Yes. The material follows the 2024 guide on Generative AI use in higher-education teaching, from the Directorate General of Higher Education. The guide asks each university to write its own GenAI policy, and lecturers write the usage rule inside each course syllabus.
How are student data and respondent data protected?
Student data and research respondent data do not enter public AI tools. Class projects use sample data or data that is already anonymised. This rule is written into the program plan, and every agent built during the program follows the same data limit throughout.
How many lecturers and students take part, and how long does the program run?
Lecturers who teach the course take part, and students join during the in-class test stage. The number of lecturers and students follows the data the university sends before the program. The base program runs 2 full days for lecturers. The scope can run longer, up to 1 semester, as stated in the agenda.
What is the price for the University program, and what does it include?
The price starts at IDR 3.9M. The final price is written in the offer after we review the number of lecturers, courses, and program length. Scope includes lecturer training, a sourced literature-review agent, a syllabus clause template, an acknowledgement format, and student data rules.
What is the difference between this program and the senior-high program?
The senior-high program uses a class-group agent project with a scoring rubric, for secondary-school students. The University program uses campus tasks: a research agent, a sourced literature-review agent, and syllabus clauses for lecturers. The University price starts at IDR 3.9M, and senior-high starts at IDR 4.5M.
How does a university start this program?
Send the number of lecturers, 2 sample courses, and your target date through the request form. We reply with an agenda and a written offer within 24 working hours. The university gets 1 revision round for the agenda, and a free reschedule with 5 working days of notice before day 1.
// next step
Start with 2 courses and 1 syllabus clause.
Send the number of lecturers, 2 sample courses, and your target date. We reply with an agenda and a written offer within 24 working hours.
