We're at SUNY CIT 2026 in Stony Brook
Today ibl.ai is at SUNY CIT 2026 — the SUNY Conference on Instruction & Technology — at Stony Brook University.
We're especially proud that Deepa Deshpande and Audeliz Matías are presenting research-based findings on empowering students and faculty with AI their institution actually owns.
Their work draws on a multi-campus SUNY Innovative Instruction Technology Grant (IITG) initiative that adopted ibl.ai to manage AI across campuses.
The presenters
Deepa Deshpande, Ph.D. is Associate Director of the Center for Innovation and Teaching Excellence at SUNY College of Technology, Alfred State.
Audeliz (Audi) Matías, Ph.D. is a Professor in the Department of Natural Sciences, College of Arts and Sciences, SUNY.
Both led a faculty-driven rollout across SUNY campuses — coordinating faculty, administrators, and IT teams — and have lived the questions every institution faces when it scales AI responsibly.
What the findings point to
The throughline of their work is that adopting AI across an institution is not a software problem — it's a governance, ownership, and partnership problem. A few takeaways from the SUNY case study:
Institutions should own their AI data
The platform keeps institutional information secure and appropriately governed, with control set at both the university and the course level.
Student and faculty work stays under the institution's control — not on a vendor's servers under the vendor's defaults.
One model-agnostic platform beats four subscriptions
ibl.ai governs access to ChatGPT, Claude, Gemini, Perplexity, and other models from a single platform — so faculty can compare models side by side without being locked to one vendor.
Stacking those as separate per-seat licenses runs about $80 per user per month. A usage-based, model-agnostic platform comes in over 90% lower at scale, because cost tracks actual token usage rather than multiplying with every seat and every model.
Instructors get real control
"ibl.ai gives instructors far more control than ChatGPT… I can decide what it won't answer, define the personality, and point students to our own campus resources."
— Ken Fujiuchi, SUNY
That control — defining behavior, limits, and which campus resources an agent draws on — is what turns a generic chatbot into a tool faculty trust in the classroom.
The partner matters as much as the platform
When AI tools converge on similar features, responsiveness and follow-through decide whether faculty actually adopt them. Both presenters pointed to the same differentiator.
"ibl.ai has been an outstanding partner on our multi-campus SUNY IITG project… What has impressed me most is the quality of their support."
— Deepa Deshpande, Ph.D., Alfred State
"The platform offers something that is difficult to find with other AI vendors: institutional ownership and control of AI infrastructure and data."
— Audeliz Matías, Ph.D., SUNY
Why it matters for higher education
AI in higher ed lives or dies on trust: trust that student and faculty work stays protected, that governance fits the institution, and that the vendor stays engaged after the demo.
The SUNY findings make the case that ownership plus partnership — not feature checklists — is what lets an institution expand AI use with confidence instead of restricting it.
If you're evaluating AI for your campus, that's the bar: a model-agnostic platform you own and govern, backed by a team that follows through.
Explore the full SUNY case study, see how ibl.ai works for higher education, or talk to our team.