Student Agent

I need to ask a professor for a meeting

ILURA helps make a professor meeting request specific: topic, reason, preparation and possible times.

Free to start · No account · Data Not Collected

When ILURA helps

Best for office hours, research questions, feedback and academic planning.

When ILURA is not the right tool

Not for asking the professor to do the work for you.

Reusable agent rule

Topic, reason, preparation, two time options.

First proof

The professor should know why the meeting is worth scheduling.

Write it now

Write it on your iPhone right now.

Open ILURA, bring in your notes or draft, and get it written in your voice — on device, free, no account. It saves the rule "Topic, reason, preparation, two time options.", so the next one is faster.

Free to start · No account · Data Not Collected
What the problem is

ask professor for meeting

This is the moment when a user does not need a blank AI chat. They need a role-aware answer for a specific situation: clarification questions, professor messages, study notes and academic communication that should be clear, not generic.

What ILURA does

Turns the answer into a private rule.

ILURA helps the user draft the immediate response, then turns the useful behavior into a saved rule for the Student Agent. The next time the same pattern appears, the user is not starting from zero.

How to use it
  1. Open the Student Agent or invoke it from a supported iOS surface.
  2. Paste or select only the text needed for this situation.
  3. Apply the rule: Topic, reason, preparation, two time options.
  4. Review the result before sending, saving, or reusing it.
What good looks like

The professor should know why the meeting is worth scheduling.

If the output only sounds polished but does not preserve the decision, boundary or next step, it is not trained enough yet.

Agent path

Turn the moment into trained behavior.

This pain point should not remain a one-off prompt. In ILURA, the useful part becomes a private role rule for the Student Agent. The user still chooses the text and reviews the output, but the correction does not reset the next time the same pattern appears.

Related reading

Read the guide behind this agent behavior.

Next pain points

Keep moving through the same user problem graph.