How ibl.ai Scales Faculty & User Support
mentorAI scales effortlessly across entire campuses by using LTI 1.3 Advantage to deliver one-click SSO, carry role information, and sync rosters and grades through the Names & Roles (NRPS) and Assignment & Grade Services (AGS) extensions—so thousands of students drop straight into their AI tutor without new accounts while every data flow remains FERPA-aligned. An API-driven ingestion pipeline then chunks faculty materials into vector embeddings and serves them via Retrieval-Augmented Generation (RAG), while multi-tenant RBAC consoles and usage dashboards give IT teams fine-grained policy toggles, cost controls, and real-time insight—all built on open-source frameworks that keep the platform model-agnostic and future-proof.
Rolling out an AI mentor to thousands of classes—and keeping both faculty and students happy—requires more than clever algorithms. It demands automation, content-aware intelligence, and centralized controls so IT teams aren’t buried in tickets. mentorAI delivers all three through: 1. One-click user provisioning via LTI that drops the AI straight into any LMS 2. Pre-built, course-aware mentors that train themselves on faculty materials through an API-driven pipeline 3. Enterprise-grade admin tools that keep identity, policy, and analytics under one roof Below is a dive into each pillar, focused on higher education but equally relevant to workforce and enterprise skilling programs.
One-Click Access with LTI
Learning Tools Interoperability (LTI 1.3) turns mentorAI into a native tab inside Canvas, Blackboard, Moodle, Brightspace, or D2L:- Single sign-on—no new passwords. A signed LTI launch piggybacks on the LMS’s SSO, instantly authenticating the user.
- Automatic role mapping. The launch payload carries whether the user is a *student*, *instructor*, or *admin*; mentorAI grants matching permissions on the fly.
- Course context included. Course IDs and section info flow through the launch, letting mentorAI store analytics and content per course without manual tagging.
Why it matters
- Zero-friction adoption. Students and faculty click once and are in; no account-creation screens, no CSV uploads for IT.
- Built-in compliance. Because authentication rides on institutional SSO, existing security and FERPA policies continue to apply.
- Instant scalability. Whether it’s 50 users in a pilot or 50,000 at semester start, every login is self-service.
Faculty-Centric Mentors Trained by API
A mentor is only as useful as the knowledge it can reference. mentorAI’s content-ingestion and Retrieval-Augmented Generation (RAG) pipeline turns a pile of course documents into a 24/7 teaching assistant:- API-driven creation. When a course shell appears in the LMS, a webhook (or scheduled script) calls ibl.ai’s API to spin up a fresh mentor tied to that course.
- Document ingestion. PDFs, slide decks, lecture transcripts, and supplementary files are uploaded—or fetched automatically from the LMS file store.
- Chunk, embed, index. Content is sliced into passages, converted to vector embeddings, and stored in a high-performance vector database.
- Retrieval-augmented answers. When a student asks a question, the mentor pulls the most relevant passages, cites them, and weaves them into its language-model response—dramatically cutting hallucinations.
What faculty gain
- Day-one usefulness. Students can ask about the syllabus, readings, or lecture 1 from the very first class session.
- No AI scripting required. Instructors simply drop the files they already produce; the platform does the heavy lifting.
- Editable guardrails. Faculty can add or exclude sources, adjust the mentor’s persona, or freeze knowledge at any point—keeping control squarely in academic hands.
Centralized Admin & Analytics
mentorAI’s multi-tenant backend lets a small IT team oversee institution-wide deployments without losing sleep.- Unified user directory. Every account is created, updated, or de-provisioned automatically through LTI events or API calls.
- Role-based access control (RBAC). Fine-grained policies dictate who can tweak mentor prompts, view class-level analytics, or export transcripts.
- Usage dashboards. Heat-maps show peak activity times, top question categories, and cost trends per department—actionable insight for both academic leadership and finance.
- Policy toggles. Need to purge chat logs after 30 days, route prompts to a private LLM cluster, or disable file uploads for a sensitive course? Admins flip a config switch—no code changes required.
IT outcomes
- Fewer tickets. Self-service login and automated mentor setup eliminate common help-desk requests.
- Predictable governance. Central knobs for data retention, privacy, and SSO keep the platform audit-ready.
- Elastic overhead. Whether serving one college or an entire university system, the same admin console scales without extra headcount.
The Bottom Line
mentorAI isn’t just smart—it’s deployable. LTI launches handle access at any scale, API-driven ingestion turns faculty materials into knowledgeable mentors overnight, and enterprise admin tools keep everything secure and accountable. The result: students receive instant, course-specific support, instructors gain an always-on teaching ally, and IT leaders roll out campus-wide AI without expanding their workload. That’s support at scale—the ibl.ai way. Learn more at [https://ibl.ai](https://ibl.ai)Related Articles
Owning Your AI Application Layer in Higher Ed With ibl.ai
A practical case for why universities should run their own, LLM-agnostic AI application layer—accessible via web, LMS, and mobile—rather than paying per-seat for closed chatbots, with emphasis on cost control, governance, pedagogy, and extensibility.
How ibl.ai Scales Feature Implementation
mentorAI’s rapid release cadence comes from standing on battle-tested open-source stacks: Open edX’s XBlock plug-in framework lets ibl.ai layer AI features atop a mature LMS instead of rewriting core courseware, LangChain’s retrieval-augmented generation and agent libraries provide drop-in building blocks for new tutoring workflows, and Kubernetes plus Terraform offer vendor-neutral orchestration that scales the same containers across any cloud or on-prem cluster. Together these OSS pillars let ibl.ai ship campus-specific customizations in weeks, hot-swap OpenAI, Gemini, or Llama via a single config, and support millions of learners without vendor lock-in.
How ibl.ai Scales Software Infrastructure
mentorAI’s cloud-agnostic backbone packages every microservice as a Kubernetes-managed container, scaling horizontally with the platform’s Horizontal Pod Autoscaler and Terraform-driven multicloud clusters that run unchanged across AWS, Azure, on-prem, and other environments. Kafka-based event streams, SOC 2-aligned encryption, schema-isolated multitenancy, LTI 1.3 single-sign-on via campus SAML/OAuth 2.0 IdPs, and active-active multi-region failover with GPU autoscaling together let ibl.ai serve millions of concurrent learners without slowdowns or vendor lock-in.
How mentorAI Integrates with Blackboard
mentorAI integrates with Blackboard Learn using LTI 1.3 Advantage, so every click on a mentorAI link triggers an OIDC launch that passes a signed JWT containing the user’s ID, role, and course context—providing seamless single-sign-on with no extra passwords or roster uploads. Leveraging the Names & Roles Provisioning Service, Deep Linking, and the Assignment & Grade Services, the tool auto-syncs class lists, lets instructors drop AI activities straight into modules, and pushes rubric-aligned scores back to Grade Center in real time.