"I am thrilled with mentorAI by ibl.ai — it’s a state-of-the-art, LLM-agnostic platform that gives our college full control over our AI teaching agents. We can customize every aspect of these mentors to align with our curriculum and pedagogical goals, empowering our faculty to innovate in ways we never thought possible. The platform’s technical sophistication is matched only by the warmth and responsiveness of the ibl.ai team — they have been true partners, working closely with us to fine-tune the system and support our instructors at every step. It’s a solution born from genuine collaboration and a keen understanding of educational needs. It aligns perfectly with our institution’s mission to elevate teaching and learning through cutting-edge yet human-centered technology."
- Dr. Juana Mendenhall, Morehouse College
Who is ibl.ai?
ibl.ai is the family-owned engineering firm behind mentorAI, an AI-native teaching and learning platform that helps universities improve student outcomes and teaching effectiveness—without giving up control. mentorAI provides 24/7 adaptive tutoring, faculty productivity tools, and real-time learning analytics, all while the institution owns its code, data, and deployment model.
It’s LLM-agnostic, integrates with major LMSs via LTI, and is used across hundreds of institutions. In short: we center faculty and learners, and we align AI to your academic mission—not the other way around.
What we do (and why universities choose us)
- Improve learning and retention. mentorAI delivers round-the-clock, course-aware support that helps students stay engaged and on track—especially in high-enrollment or gateway courses.
- Boost faculty capacity. Instructors get help with high-friction tasks—drafting materials, building assessments, and giving formative feedback—so they can focus on pedagogy and higher-touch mentoring.
- Make decisions with data. Program-level dashboards surface performance trends, at-risk learners, and evidence for accreditation and funding.
- Keep institutional control. Your university retains ownership of code and data and chooses where to host (your cloud, on-prem, or our managed cloud). You also choose the model—OpenAI, Gemini, Claude, Llama, or your own—per course or use case.
- Integrate cleanly. We connect via LTI to Canvas, Blackboard, Brightspace, and Moodle; support SSO/SIS; and expose a full REST API/SDK for your engineers.
- Lower total cost of ownership. University-wide licensing avoids per-seat bloat, and LLM choice lets you right-size costs by pairing premium models where needed with efficient models for everyday Q&A.
Why work with ibl.ai?
Because we’re purpose-built for education. Our platform emphasizes faculty support, student success, and governance—with full ownership, flexible hosting, open integrations, and measurable ROI. We’re not repackaging a generic chatbot; we’re helping academic leaders run better courses and programs.
Our collaboration with Morehouse College: MORAL AI
Together with Morehouse College and Dr. Juana Mendenhall, we’re advancing a practical approach to MORAL AI—giving instructors the ability to configure AI mentors that reflect their values, course goals, and institutional mission. The aim isn’t abstract ethics; it’s day-to-day teaching support that is morally aligned, transparent, and faculty-led.
What “MORAL AI” means in practice
- Plays off the broader conversation about responsible AI use and ties to Morehouse’s moral, ethical, and justice-centered values.
- Will support the HUB for Teaching and Learning to train other HBCUs, PBIs, and small liberal arts colleges on the ethical and responsible use of AI in the classroom for faculty/staff and students.
- Instructor-defined guardrails. Faculty can shape tone, boundaries, and acceptable sources so mentors teach in ways consistent with their course norms and community standards.
- Grounded, cite-back answers. Mentors privilege instructor-approved materials over the open web, reducing hallucinations and keeping guidance anchored to the curriculum.
- Human in the loop. Instructors review analytics to see where students struggle, fine-tune prompts and policies, and intervene when the task warrants human judgment.
Equity, privacy, and local-first options
In our recent planning conversations, we explored pairing mentorAI with local LLMs on devices so mentors can run offline when needed. The goals are straightforward:
- Privacy by default. Notes and student interactions can remain on the device when that’s the requirement, minimizing data exposure.
- Equitable access. A standard local setup gives every learner the same core capability, not just those with premium accounts or perfect connectivity.
- Cost control. Local or on-prem deployment helps cap model costs and prevents vendor-driven price shocks—important for sustainable rollouts at HBCUs and resource-constrained campuses.
A practical path for HBCUs
Dr. Mendenhall’s vision extends beyond one campus: a hub-and-spoke model that lowers the barrier to entry for other HBCUs. Concretely, we’re aligning on:
- Fast onboarding, minimal friction. Standing up tenant instances quickly (including custom domains) so faculty can pilot with their own materials in days, not months.
- LMS-native experiences. LTI integration with Canvas ensures students meet the mentor where they already learn—no extra tabs, no new passwords.
- Faculty community and webinars. Short, hands-on sessions for course teams to share practices, tune moral guardrails, and iterate on prompts together.
- Partnership leverage. When appropriate, engaging cloud and hardware partners to support pilots (e.g., credits, devices) so faculty can test local/offline and cloud-backed modes without budget strain.
- Consortium-ready architecture. Our multi-tenant backend lets multiple colleges operate on shared infrastructure with strong isolation, simplifying governance while keeping costs predictable.
What success looks like
- Students get consistent, values-aligned help that reflects their instructors’ intent.
- Faculty keep authorship over how AI teaches and can dial capabilities up or down.
- Institutions scale ethically—with clear policies, transparent analytics, and flexible deployment (cloud, on-prem, or hybrid).
Where we’re headed
Our work with Morehouse is about agency: putting instructors and institutions in control of what AI says, how it behaves, and where it runs. That aligns with ibl.ai’s core commitments—educational focus, institutional ownership, LLM flexibility, and measurable impact—and it charts an accessible path for peer HBCUs who want to move quickly without compromising mission or means.
If you’re a university leader or faculty member exploring AI teaching agents—and you want them to reflect your curriculum and your values—send us a DM or contact us at ibl.ai/contact to learn more!