What Are AI Agents in Higher Education?

A definition that is useful in a course#

The word "agent" is used loosely. A practical definition has three parts. An agent uses an AI model to decide what to do next. It has tools, such as the ability to search documents, read a gradebook or fill in a form. And it works toward a goal over more than one step.

Anthropic's engineering guide to building agents draws a distinction that is helpful here. Workflows, it says, orchestrate models and tools through predefined code paths. Agents are systems in which the model dynamically directs its own process and tool use. In practice, many products described as agents sit somewhere between the two. For a faculty member the more useful questions are what the system can read, what it can change and who has to approve.

Chatbot or agent?#

What agents do in a course#

Agents in higher education tend to fall into five jobs.

1. Authoring. Drafting assignments, rubrics, question sets, scenarios or course outlines from a brief and source documents. A person edits and approves. See how AI can help faculty build assessments.
2. Tutoring and answering questions. Answering student questions from course materials, with citations. See AI course assistants explained.
3. Assessment support. Asking questions about a student's submission, drafting rubric scores, or summarizing a transcript for a grader.
4. Simulation. Playing a character in a scenario. See AI role-play in education.
5. Analysis. Answering questions about how a course or assignment went, such as which criteria students missed.

Some jobs involve little risk because the agent only drafts. Others touch records or students and need tighter control.

What makes an agent safe to use in a course#

Four properties separate a trustworthy educational agent from a risky one.

• Scoped permissions. The agent should see only what the signed-in user is allowed to see. An instructor's assistant should not read another instructor's course, and a student's assistant should not see other students' work or grades.
• Human approval for consequential actions. Publishing, releasing grades, sending announcements and deleting data should require a person to confirm. Drafting is low risk. Changing records is not.
• Grounding. Answers should come from identified course material, with citations, and the agent should say when it does not know.
• Isolation of untrusted input. Student submissions and uploaded documents can contain instructions aimed at the model. The system should treat that content as data, not as commands.

Also ask about auditability: can you see what the agent did and when?

Privacy and FERPA#

An agent that reads student records is handling education records. In the United States the Family Educational Rights and Privacy Act governs who may access them and under what conditions. Ask where data is processed, whether a vendor uses it to train models, how long it is retained and what agreements govern the vendor as a school official. The Department of Education's student privacy site explains FERPA, and your institution's privacy office can advise on a specific tool. See also Rocketproof.ai's security and FERPA page.

What agents cannot do#

• They do not understand your learning outcomes unless you tell them.
• They can produce confident wrong answers.
• They should not decide grades. Kasneci and colleagues review the opportunities and challenges of large language models in education, and a recurring theme is that teachers and learners need to evaluate model output critically instead of accepting it.
• They do not replace professional judgment about students.

UNESCO's guidance on generative AI in education calls for a human-centred approach, which in practice means a person stays responsible for decisions that affect students.

Questions to ask a vendor#

1. What can the agent read, and how does that follow the user's role?
2. What can it change, and what needs a person's approval?
3. Where do its answers come from, and does it cite them?
4. How does it treat text from students and uploaded files?
5. Is there a log of what it did?
6. Where is data stored, and is it used to train models?
7. Can I turn individual agents off?

Agents built into a platform and agents connected from outside#

An agent can live inside a product, with access to that product's data under that product's permissions. Or an outside assistant can connect to a product through a protocol such as the Model Context Protocol. Each approach has strengths and risks, and we compare them in AI agents inside a platform versus external MCP integrations.

Where Rocketproof.ai fits#

Rocketproof.ai includes AI agents for instructors that draft assignments, simulations, rubrics, question sets and course structures, and that answer questions about course and submission results. Students have a per-course Course Guide and a Study Coach. Each agent runs inside the signed-in user's role and course access. The assistants draft and advise, and a person saves, publishes and grades. The built-in AI agents page lists each one, and the agentic AI in education page explains the design principles.