Per-Course and Per-Student Mentors on mentorAI

At ibl.ai we’ve learned a simple truth from working with universities: precision beats “one big chatbot.” Course contexts differ, student needs diverge, and faculty pedagogy is not one-size-fits-all. That’s why mentorAI lets you scope assistants per course—and, when useful, per student within a course—so the AI behaves and answers with the right granularity. Faculty don’t want a Pre-Calc student getting Calc III answers, and they want control over how the assistant explains concepts. In mentorAI, that’s the default posture: one mentor per course, with the option to instantiate per-student mentors when you want personalized behavior.

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Why Granularity Matters

• Rigor without spillover. Each mentor can be trained only on that course’s materials, so answers don’t “leak” across levels or departments.

• Faculty voice preserved. Instructors set the mentor’s prompt and pedagogy—tone, level, examples—so explanations match how they teach.

• Transparent answers. With Document Retrieval, replies cite the exact lecture/slide/page and display a ranked Source Panel; one click opens the original file for verification and deeper study.

• Safety on top of safety. A custom moderation layer lets you scope what’s “in bounds” for each mentor (e.g., refuse questions outside a course domain), layered over the base model’s alignment.

How Per-Course Mentors Work

• Create the mentor. Give it a name and description; pick a language model (OpenAI, Gemini, Anthropic, etc.—your choice per mentor).

• Add the corpus. Instructors drag-and-drop approved files (slides, PDFs, readings) into the mentor’s dataset. Retrieval is then limited to these sources.

• Set visibility. For each file, use the Visible toggle to decide whether it appears in the Source Panel; hidden files can still inform answers without being shown, and you can flip visibility instantly—no retraining.

• Teach the teacher. Adjust the prompt/pedagogy settings to guide explanations, examples, or steps appropriate for your learners.

Result: When a student asks a question, the answer is grounded in that course’s materials and cites them, with sources ranked for inspection.

When To Go Per-Student (Within a Course)

Sometimes you want finer control—an assistant that adapts to an individual’s progress or needs. In those cases, you can spin up a mentor per student per course (when you have the right context and approvals).

• Personalized scaffolding. Calibrate the prompt to a learner’s background or goals while keeping answers sourced to the same course files.

• Optional insight for instructors. If enabled, you can track learner history to spot common stumbling blocks and refine materials.

Privacy and governance remain institution-controlled; mentors use only the data and scope you approve.

Faculty Experience: Simple First, Control When Needed

Faculty have told us they want “factory defaults” that work out of the box—and settings they can adjust when they have time. mentorAI starts simple (create → add files → go), but lets instructors dial in:

• Prompt & pedagogy controls (explainers, steps, tone).

• Model choice per mentor (use the LLM that fits cost/performance needs at any time).

• Source visibility (show or hide specific files without re-indexing).

• Domain-specific safety (constrain answers to the course topic).

What Students See

• Clear, concise answers grounded in the course’s own materials.

• Inline citations and a Source Panel ranked by relevance; one click opens the original document to read more.

• Consistent explanations that match how their instructor teaches (because the mentor’s behavior is set by the course team).

Where This Pays Off

• Academic integrity. Students can verify every claim against the course’s own readings.

• Pedagogical alignment. The assistant sounds like your course—not a generic chatbot.

• Operational agility. Faculty can hide/reveal sources on demand and adjust prompts without re-training.

• Right-sized personalization. Use per-student mentors where they add value; otherwise keep it simple with the per-course default.

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In Conclusion

If you want AI that mirrors your syllabus, cites your slides, and adapts at the course and student level—without sacrificing safety or faculty control—let’s talk. Visit ibl.ai/contact to see per-course and per-student mentors in action.