# "Jarvis for Educators" at Alabama State University > Source: https://ibl.ai/case-study/alabama-state-university ## Case Study How Alabama State University is building a campus-wide agentic AI system that connects learning, advising, and administration into a single, governed intelligence layer — turning siloed data into actionable insight for student success. Under the leadership of Dr. Damian Clarke, Chief Information Officer, ASU is deploying an agentic AI system that draws insight from the university's key systems — ERP, LMS, CRM, and SIS — to provide real-time, contextual support for every student journey. The goal: unify the student story across data systems so faculty and staff can act sooner, personalize outreach, and help more students thrive. ### Quick Stats - **4 Systems** SIS, LMS, CRM, ERP unified - **Any LLM** Model-agnostic - **FERPA** Compliant by design - **RBAC** Role-based governance --- ## The Challenge ### From siloed systems to a unified "campus brain" **Fragmented data** — SIS, LMS, CRM, and advising tools each captured part of the learner experience but rarely spoke the same language — students fell through the cracks between systems. **Integration fatigue** — Faculty and advisors spent more time toggling between dashboards and chasing data than teaching, mentoring, or acting on the insights that matter. **Delayed interventions** — Without a unified view of each student, at-risk patterns were spotted too late — missing assignments, low engagement, and financial holds went unconnected. --- ## The Vision ### An intelligent co-pilot for every educator Dr. Clarke's vision of "Jarvis for educators" gives every faculty member and administrator an AI co-pilot that synthesizes data from across campus systems in real time — with faculty always in control and data always in-house. **Summarize student progress** — Synthesize LMS grades, SIS records, and advising notes in real time — no more toggling between five dashboards before a meeting. **Flag early-risk patterns** — Proactively surface missing assignments, low engagement, and financial holds — and suggest actionable next steps before it's too late. **Prepare for every interaction** — Before an advising meeting, the AI surfaces the student's academic plan, communication history, and current support needs — ready to go. **Automate follow-ups** — Send consistent, FERPA-compliant communications via approved institutional channels — no student outreach falls through the cracks. --- ## Data Architecture ### Four data streams, one governed memory layer ibl.ai's platform provides standards-based connectors (LTI 1.3, APIs) to pull structured signals from ASU's core systems. Each data stream becomes a governed memory layer that agents can safely reference — not a data dump, but contextual intelligence built on consent and transparency. - **SIS** — Enrollment, academic standing, financial aid status - **LMS** — Engagement, performance, assignment trends - **CRM** — Prospect intent, application milestones, communications - **Advising** — Appointments, risk alerts, outreach history --- ## Impact ### How agentic AI amplifies ASU's mission Alabama State's initiative positions the university as a model for equitable AI enablement in HBCUs — ensuring that advanced technology strengthens the human connection at the heart of higher education. **For faculty** — More time for teaching and mentoring — less time chasing data across dashboards. AI handles the synthesis so educators can focus on the student. **For advisors** — Timely alerts and unified context — students no longer fall through the cracks between systems. Every interaction is informed by the full picture. **For administrators** — Analytics that tell the whole story: which interventions drive retention, where bottlenecks form, and how resources can be deployed equitably. --- ## Technical Foundations ### Transparent, traceable, and adaptable ibl.ai's Agentic Operating System ensures every AI interaction is governed, auditable, and built for longevity — not a black box, but a platform ASU owns, configures, and evolves. #### Typical SaaS AI - Black-box model — no visibility into decisions - Vendor controls the data pipeline - Locked into one LLM provider - Generic, disconnected from campus systems - No role-based governance for sensitive data - Vendor-dependent roadmap #### ibl.ai at ASU - Every AI interaction is transparent and traceable - Data stays within university policy boundaries - Model-agnostic — swap LLMs without rewriting workflows - LTI 1.3 integration directly inside Canvas - RBAC governs access to PeopleSoft, Slate, and EAB Navigate - Deploy in ASU's preferred cloud environment --- ## For University Leadership ### A blueprint for student success infrastructure ASU's approach isn't a single pilot — it's a model for what a 21st-century institutional memory system can look like: grounded in ethics, powered by AI, and governed by educators. **Data works for people, not the other way around** — Faculty and advisors make data-informed decisions without toggling between systems or waiting for reports. The AI synthesizes — humans decide. **Equitable AI enablement** — ASU is pioneering a model for HBCUs that ensures advanced technology strengthens — not replaces — the human connection at the heart of higher education. **Scalable care and communication** — Data fluency plus agentic AI makes personalized outreach possible at institutional scale. Every student gets timely, informed support — not just the ones who ask for it. **Built for longevity** — Standards-based plumbing (LTI 1.3, APIs, RBAC) and model-agnostic orchestration mean ASU's investment compounds over time as AI capabilities evolve. --- ## Get Started Connect your SIS, LMS, CRM, and advising tools into a single governed intelligence layer — with a team that works alongside yours. [Try ibl.ai](/contact) | [See all case studies](/case-studies)