How ibl.ai Makes AI Simple and Gives University Faculty Full Control

“Factory defaults” that work out of the box—with deep prompt & pedagogy settings when instructors want them If AI is going to help in real classrooms, it has to pass one test first: don’t give faculty more to do. In our work with campuses, we’ve designed mentorAI so instructors can get value on day one—and still have fine-grained control over tone, prompts, sources, and safety when they’re ready. Below is the model we’ve seen work repeatedly: sane defaults that respect time, paired with knobs and dials that reward curiosity.

---

Start simple: out-of-the-box mentors that respect faculty time

• One click to useful. Instructors spin up a course mentor, drag-and-drop syllabus/lectures/readings (or point to approved URLs), and the assistant is ready. No prompt engineering required.

• Cited, course-grounded answers. mentorAI uses retrieval-augmented generation (RAG) to cite the professor’s materials—not the open web—so students see exactly where a concept comes from.

• Live where teaching happens. Through LTI, the mentor appears as a sidebar natively in any LMS, which means students get help in context and faculty don’t have to change workflows.

• No-drama governance. Run as SaaS or in your own cloud; bring your own model keys if you like. You keep ownership of your materials and decide what the assistant can access.

Result: faculty get immediate lift—fewer repetitive emails, faster clarifications for students, and no extra hoops.

Dial it in: deep prompt & pedagogy controls when you’re ready

When instructors want more, the depth is there—without forcing it up front.

• System prompt & tone. Adjust how the mentor speaks (Socratic? Step-by-step? Clinical?) and set discipline-specific expectations (e.g., show working, cite page numbers).

• Scope of knowledge. Constrain the assistant to your corpus only, allow approved web domains, or add datasets (e.g., policy docs, lab manuals).

• Assessment helpers. Generate draft questions, rubrics, and outlines from your own content—then review and edit before publishing.

• Per-course, per-student granularity. Most deployments use one mentor per course (and can even tailor to each student), preventing spillover from higher-level content that would confuse novices.

• Analytics you can act on. See common questions, flagged misconceptions, and where students struggle—fuel for targeted announcements and mini-lessons.

These controls are optional. Like a smartphone’s settings menu, everything works from “factory defaults,” but power users can shape the experience to their pedagogy.

Safety and alignment you can actually see

Campus leaders need more than “trust us.” mentorAI layers institutional guardrails above the base model’s alignment:

• Topic boundaries. Define what the mentor will (and will not) answer—e.g., limit a prospective-student assistant to admissions content only.

• Language and policy filters. Enforce department or institution standards consistently across courses.

• Transparent logs. Administrators can review interactions (with privacy controls) to audit answers, refine prompts, and demonstrate compliance.

Because these rules are visible and editable, faculty remain the final arbiters of what “good” looks like in their class.

Integrate the way your IT team prefers

Cautious institutions want optionality. mentorAI is built to avoid lock-in:

• Model-agnostic by design. Use OpenAI, Gemini, Claude, Llama, or campus-hosted models—per course or use case. Right-size cost and capability without re-platforming.

• Flexible hosting. Deploy in your environment or ours; multi-tenant architecture lets central IT serve many departments with clean data isolation.

• APIs & SDKs. Your developers can build custom apps against the same backbone our web apps use, so pilots can grow into campus-wide systems without starting over.

• LMS ingestion options. Most faculty simply upload materials; if you prefer, IT can enable API-based ingestion from the LMS after approvals.

The theme is simple: start small, keep control, scale on your terms.

What the first week usually looks like

• Provision a single course and upload the initial corpus (slides, readings, assignment prompts).

• Enable the LMS sidebar so students see the assistant where they study.

• Run with defaults for a few days; watch FAQs disappear and misconceptions surface.

• Tune prompts and guardrails based on early signals (e.g., require citations, restrict to your corpus, adjust tone).

• Review analytics and decide whether to add assessments or expand to the next course.

The cadence is deliberate: value first, customization second. That’s how we keep adoption high without overwhelming busy teams.

---

Why simplicity + control wins faculty trust

• Time back to teach. Defaults reduce setup and cut repetitive Q&A.

• Academic integrity. Cited answers grounded in instructor materials minimize hallucinations and reward close reading.

• True ownership. Instructors and institutions decide the models, the hosting, the scope, and the rules.

• Room to grow. When curiosity kicks in, the system supports deeper pedagogy—without forcing it on day one.

If you’re weighing how to introduce AI into real courses, start with a tool that works immediately and hands over the keys when you’re ready. Contact at us at ibl.ai/contact to learn more.