Universities are moving from AI curiosity to AI commitment. Action plans are being drafted with clear goals: protect student data, raise learning outcomes, reduce costs, and build internal capacity—not just procure another “chatbot.” At the same time, public policy momentum and industry investments are accelerating the need to operationalize AI responsibly across teaching, student success, research, and operations. Recent reporting highlights increased federal attention on AI education policy and significant industry funding for higher-ed AI training and tools—signals that the time to execute is now.
ibl.ai is designed as the campus AI layer that makes these plans real—safely, measurably, and on your terms.
The Problem With “Just Add ChatGPT”
Most AI Action Plans fail in execution because the tools sit outside the institution:
- No safe context. External SaaS assistants rarely get approved to ingest registrar/LMS data. Without context, they can’t personalize or measure learning meaningfully.
- No telemetry that matters. If AI runs off-campus, you don’t get course-aligned analytics (sessions, topics, misconceptions, costs) tied to cohorts or outcomes.
- Governance friction. FERPA, data residency, and security reviews become blockers rather than enablers.
The ibl.ai Fit: On-Prem/Your Cloud, Course-Aware, Outcome-Driven
ibl.ai is an
AI operating system for educators: agentic mentors, unified APIs, and SDKs that run on-prem or in your cloud so the
code and data remain yours. Mentors live where students learn (Canvas, Blackboard, Brightspace) via LTI and can responsibly use
approved student and course context to tutor, advise, and nudge.
What That Unlocks:
- Teaching & Learning: Course-aware mentors cite your materials, respect your pedagogy, and scaffold mastery with proactive guidance.
- Student Success & Advising: Memory of goals, risk signals, and progression helps mentors personalize support and trigger early alerts.
- Faculty Empowerment: Full control over prompts, scope, tools, and safety. 1:1 enablement and office hours drive adoption.
- Research & Innovation: Python/web SDKs and a unified API let campus teams build on a base—shipping secure agentic apps faster.
- Measurement: Built-in analytics connect engagement × concept understanding × cost, enabling cost-per-outcome reporting.
- Governance: Tenant isolation, additive safety (pre- and post-moderation), audit trails, and domain scoping keep mentors in-bounds.
Map mentorAI to Your AI Action Plan Pillars
1) Governance & Risk
- Deploy on-prem or in your cloud (GCP/AWS/Azure).
- Additive safety: request moderation before model calls and response checks after—with institution-defined policies.
- Fine-grained role-based access and tenant isolation.
2) Teaching & Learning
- LTI-embedded mentors in your LMS; answers are cited to approved sources.
- Faculty set pedagogy (Socratic, direct instruction, etc.), proactive prompts, and scope (course-bounded).
- Memory layer holds structured, approved context (major, enrolled courses, progression cues, accommodations).
3) Student Success & Equity
- Topic analytics + sentiment + usage heatmaps identify at-risk learners and underserved groups.
- Mentors nudge students to the “next best action” (readings, practice sets, support services).
4) Infrastructure & Interoperability
- Unified API + Python/web SDKs standardize model access, RAG, tools, and analytics.
- Model-agnostic routing: use OpenAI, Gemini, Claude, or others interchangeably as costs/features evolve.
- Connect SIS/LMS/document stores for approved, auditable context.
5) Workforce & Faculty Enablement
- Structured onboarding, workshops, and 1:1 faculty sessions reduce the adoption curve.
- Templates and sample mentors for common courses jump-start design.
6) Budget & Procurement
- Replace $/user SaaS with developer-rate model usage under a campus license.
- Track cost per session, per model, and per course; roll up to cost per outcome.
7) Evidence & Accountability
- Dashboards: Overview (pulse), Users (equity), Topics (coverage & confusion), Transcripts (quality), Financial (cost-to-learning).
- Exportable metrics for academic assessment and board reporting.
Architecture (At a Glance)
Your systems (SIS, LMS, docs) → mentorAI Data Adapters. Secure connectors bring in
approved course materials and student context.
ibl.ai Core (Platform Layer)
- Memory & Policy: structured student/program context + safety rules
- Agent Runtime: tools, retrieval, function calling, multi-model routing
- Analytics: engagement, topics, sentiment, transcripts, costs (with audit logs)
- Admin UX: faculty controls for prompts, scope, safety, datasets, and LTI
- Channels: LMS (via LTI + sidebar), web, and mobile—so mentors show up where learners already are
- Models & Tools (Swappable): Use the best LLM or capability per task today; change it tomorrow without rewriting your apps.
Result: you keep control of context and telemetry at the center while remaining agile on the model edge.
Start Pragmatically: From Pilot to Platform
Phase 1 — Prove utility (weeks, not semesters)
Pick 2–3 high-impact use cases: a course mentor, a prospective-student guide, and an advising helper. Embed in LMS, seed Memory, and light up analytics.
Phase 2 — Expand with evidence
Tune prompts, add datasets, and publish dashboards to academic leadership. Introduce a second department; compare outcomes/DFW trends alongside usage.
Phase 3 — Build on the base
Use the SDKs to ship domain-specific agentic apps (labs coaching, writing studio, career readiness, financial services). Reuse the same platform, safety, and telemetry.
Why “Build On A Base” Beats Build Or Buy
- Faster than build: Your teams focus on workflows and UX, not multi-tenant security, model routing, and analytics plumbing.
- Safer than buy: Data stays on-prem/in your cloud; mentors are course-bounded with institution-defined safety.
- Cheaper to scale: Swap models as prices/features change; track real cost per outcome to guide reinvestment.
Conclusion
Recent coverage underscores both policy momentum around AI education and large-scale industry funding for university AI capacity, reinforcing the urgency—and opportunity—for institutions to operationalize responsible, measurable AI. AI Action Plans that stick are grounded in
context, control, and evidence. ibl.ai was built for that reality: on-prem/your-cloud deployment, course-aware mentoring, faculty-controlled safety, and analytics that tie usage to understanding and cost. It’s the pragmatic way to deliver agentic AI across teaching, student success, and operations—without surrendering your data or your future flexibility.
If you’d like to
explore how ibl.ai can turn your AI Action Plan into measurable learning gains, visit
https://ibl.ai/contact to learn more.