Guided, Proactive Mentors on mentorAI

Most AI “chatbots” wait to be asked a good question. In real courses, students often don’t know what to ask, when to ask it, or how to sequence their study. That’s where guided, proactive mentors come in: course-aware assistants that understand the structure of a class—units, outcomes, readings, and checkpoints—and nudge learners forward with timely, context-specific help. At ibl.ai, we build mentors that do more than answer. They guide: suggesting next steps, surfacing the right slide or reading at the right moment, prompting reflection after practice, and linking every explanation back to instructor-approved sources so students study from the materials that actually matter.

---

What “Guided And Proactive” Means In Practice

• Course-aware by design. Each mentor is configured with a simple map of the course—units/modules, learning goals, and the key artifacts (slides, readings, rubrics).

• Nudges that respect the flow. As learners progress, the mentor suggests what to do next (“Before Problem Set 2, review Unit 1.3 on limit laws”) and offers quick refreshers or mini-checks aligned to the current unit.

• Evidence over opinion. Answers are cited—the assistant links directly to the slides, readings, or notes it drew from—so students can verify, re-read, and go deeper.

• Reflection built-in. After AI help, the mentor prompts short reflections (“Explain why your approach works” / “What would fail if the assumption changed?”) to counter the classic illusion of competence.

• On-scope by default. A custom safety/moderation layer keeps the assistant within course boundaries (e.g., it politely declines off-topic requests and routes students back to relevant materials).

Guidance Patterns That Work (And Why)

• Unit previews (orienting the learner)

Short, structured briefings at the start of each module highlight essential concepts and likely stumbling blocks. These reduce early thrashing and help students budget attention before they dive into practice.

• Just-in-time refreshers (maintaining momentum)

When the mentor detects a concept dependency, it offers a concise refresher with a citation to the exact slide/reading. Students move forward without leaving the course context or guessing which resource to open.

• Misconception-targeted hints (scaffolding, not solving)

Instead of dumping solutions, the mentor delivers a graded hint sequence aligned to the unit’s goals. Hints nudge students toward the next productive step and point back to the instructor’s explanation.

• Post-help reflections (making learning visible)

After assistance, the mentor prompts a brief explanation, counter-example, or “why it works” statement. This combats the illusion of competence and gives instructors a quick read on understanding.

• Progress checkpoints (lightweight formative signal)

Periodic, low-stakes checks tied to unit outcomes help surface confusion clusters early. Instructors can then add a mini-primer or clarify in class—targeted fixes without reworking the whole unit.

• Scope-aware redirects (staying within the course)

When students drift off-topic, the mentor responds with a friendly boundary and a relevant pointer back into the unit (“That topic is outside our course; try Section 2.4 for the method we do use”).

Why Faculty Like This Model

• Keeps learning on rails. The mentor knows the path and reduces thrashing, especially early in a unit.

• Teaches from your course. Because explanations point to your slides/readings, students build trust in the course’s canon (and you can quickly spot gaps).

• Balances help with agency. Guided hints + reflection prompts support progress without handing over the work.

• Simple to start, deep when needed. Out-of-the-box defaults work immediately; instructors can later fine-tune pedagogy, tone, and guardrails.

How Instructors Set It Up (Without Extra Overhead)

• Attach materials and map units. Add the slides/readings for each unit, plus any outcomes or key terms.

• Choose guidance defaults. Pick a nudge pattern (preview → practice → reflect) and set a light cadence for suggestions.

• Set scope and boundaries. Define what the assistant will and won’t cover; off-scope questions get a friendly redirect.

• Review early signals. After the first week, glance at confusion clusters and citation opens; adjust nudges or add a one-page primer where needed.

Student Experience

• “What’s next?” is always clear.

• Help arrives at the unit level they’re working in.

• Every answer links to the exact course source.

• Reflection prompts make understanding visible—to them and to you.

---

Getting Started

If you want your AI to do more than answer—to guide—we can help you stand up a course-aware mentor quickly, then iterate with your faculty on tone, nudges, and scope. Reach out at ibl.ai/contact to see a guided mentor in action.