AI That Moves the Needle on Learning Outcomes — and Proves It

How on-prem mentorAI with built-in analytics gives faculty and administrators a clear, privacy-first view of what students are learning.

---

Universities don’t need another chat widget. They need evidence: Which students are engaging? What topics are sticking (or not)? Where are the gaps, and did the AI help close them? That level of visibility only happens when AI mentors live inside the institution’s own environment and have responsible access to the right context. That’s exactly why mentorAI by ibl.ai is deployed on-prem or in your cloud, with campus-owned code and data. When the platform is integrated with your systems, mentors can operate with meaningful context—major, program, enrolled courses, and performance signals—and your teams can measure learning outcomes with first-party analytics instead of guesswork.

---

Why Outcomes Are Hard To Measure With External SaaS AI

• No secure, reliable context. It’s rarely feasible (or approvable) to sync registrar/LMS data into an external SaaS chatbot. Without context, assistants can’t personalize or track learning meaningfully.

• No shared telemetry. If the AI sits outside your stack, you don’t get session-level analytics aligned to your curriculum or cohorts.

• Governance gaps. Data residency, FERPA, and security reviews become blockers—not enablers.

What Changes With mentorAI On-Prem (Or In Your Cloud)

• Context that matters. Mentors can use campus-approved data—such as a student’s major, course roster, and progression indicators—to tailor help and keep guidance in-bounds.

• Privacy by design. Code and data run in your environment, with tenant isolation and fine-grained controls.

• Evidence at your fingertips. Every mentor includes embedded analytics so faculty and administrators can see engagement, topics, quality signals, and costs in one place.

Memory: The Backbone Of Context-Aware Mentoring

mentorAI includes a Memory layer that lets institutions maintain persistent, structured student context the mentor can responsibly reference in conversations. Typical fields include:

• Academic profile: program/major, enrolled courses, cohort

• Progress cues: unit completion, prior attempts, instructor-approved notes

• Preferences & supports: pacing notes or accommodations shared by the student or instructor

Because Memory lives with your deployment, it can be pre-seeded from campus systems and updated as learning unfolds—giving each interaction continuity while respecting institutional policy.

• Result: Mentors aren’t “generic helpers.” They’re course-aware and student-aware, which is essential for impact on learning outcomes.

From Conversation To Evidence: Built-In Analytics That Matter

Every mentor ships with a full analytics suite so you can measure what students do—and what they get out of it.

• 1) Overview (program-level pulse): Track sessions, active users, and topic trends over time to see how engagement aligns to course calendars and assessments.

• 2) Users (equity & reach): See engagement heatmaps and cohort activity to spot who’s using the mentor (and when)—useful for identifying underserved groups and optimizing outreach or office hours.

• 3) Topics (coverage & confusion): Monitor which concepts are most discussed and whether interest or difficulty spikes around certain units. This is a leading indicator for curriculum adjustments and targeted reviews.

• 4) Transcripts (quality & learning signals): Explore representative conversations with tagging, suggested topics, and sentiment to understand misconceptions and the mentor’s explanations. Faculty can pull excerpts into class, create follow-up activities, and refine prompts/resources.

• 5) Financial (cost-to-learning): Track cost by provider and by model, plus cost per session. That makes it easy to report a cost-per-outcome story (e.g., cost per passed unit, reduced DFW rates alongside usage).

Together, these views close the loop: outcomes = engagement (who/when) × content understanding (what/how) × cost (efficiency).

Turning Analytics Into Action

• Early alerts. Topic spikes + negative sentiment + reduced activity can flag at-risk learners for quick outreach.

• Instructional refinement. High-traffic transcripts surface recurring misconceptions, which instructors can address in class or through nudges.

• Personalized nudging. With Memory and topic analytics, mentors can proactively point students to the next unit, a relevant reading, or a short practice set—right when they need it.

• Assessment alignment. Faculty can map topics and mentor prompts to course outcomes, then watch how engagement and mastery shift as materials are updated.

Governance First: Your Environment, Your Rules

Because mentorAI runs on-prem or in your cloud, institutions retain full control over data flows, retention, and auditability. Additive safety and domain scoping ensure mentors stay in-scope for academic policies and course content.

What This Means For Your Campus

• Better outcomes, measured. Rich, first-party telemetry shows whether AI support correlates with completion, persistence, and mastery.

• Less friction for IT and faculty. No risky data exports to third-party SaaS; no black-box dashboards.

• A durable analytics asset. As more courses adopt mentors, your outcome insights compound—across programs, terms, and cohorts.

---

See It In Action

If you’d like a walkthrough of the Memory experience, chat history review, and mentorAI’s comprehensive analytics dashboard, we’re happy to spin up a pilot in your environment and align the analytics to your program outcomes. Visit https://ibl.ai/contact to learn more.