We’re excited to share a new multi-campus partnership led by Empire State University (SUNY Empire), in collaboration with five additional SUNY campuses, to explore how AI mentors and LLM tools can support teaching and learning across all six campuses. The participating campuses include: Alfred State College, Stony Brook University, SUNY Fredonia, SUNY Upstate Medical University, and SUNY Schenectady, alongside SUNY Empire as the lead institution. This work is sponsored by the SUNY Innovative Instruction Technology Grants (IITG) program.
At the heart of this collaboration, sponsored by the SUNY Innovative Instruction Technology Grants (IITG) Program, is a simple idea: faculty, not vendors, should control how AI shows up in their classrooms. SUNY Empire is coordinating a cohort of faculty members across the collaborating SUNY campuses who will design outcome-aligned experiences, AI tutors and mentors, capstone projects, and sequences of scaffolded AI-infused assignments, using multiple large language models.
These designs will then reach students across the campuses, creating a rich, comparative picture of what actually works for learners in different disciplines. In parallel with the design work, the collaborating research team is conducting a formal study to examine the usability and efficacy of generative AI tools for teaching across diverse instructional contexts.
Behind the scenes, ibl.ai is providing a single, LLM-agnostic platform where the research team can centrally govern access to models like ChatGPT, Claude, Gemini, Perplexity, and others, while retaining full control of API keys, seat assignments, and permissions.
Institutions are not locked into any one provider LLM: they can mix and match models, compare them, and evolve their strategy over time, without sacrificing ownership of their tools, data, or pedagogy.
To support this work, we’re extending a shared collaboration environment across the collaborating campuses where faculty, instructional designers, mentors, and students can build together, mirroring the professional-development infrastructure we’ve already established with SUNY.
This is not just a technology deployment; it’s a cross-campus learning community focused on evidence, usability, and real outcomes across multiple LLMs, and a replicable framework that other campuses can use to support faculty innovation and build sustainable, cross-institutional learning communities.
What this means for faculty
For faculty participating in the project, the value is pedagogical control with real technical depth behind it:
- Customized AI mentors for each course, aligned with program outcomes and disciplinary norms, rather than generic chatbots.
- Ownership of mentors and visibility into student interactions, datasets, and outcomes, so instructors can see how AI is impacting learning, not just guess.
- A dedicated support team and training model that treat instructors as co-designers, not end-users, so they can safely experiment, iterate, and share what works across campuses.
In short: faculty keep control over the pedagogy while ibl.ai handles the complexity of AI infrastructure.
What this means for students
For students, the collaboration is about personalized, always-available support that stays aligned with their courses:
- Proactive AI mentors that guide students toward specific learning outcomes instead of just answering one-off questions.
- 24/7 tutoring tuned to each course’s materials, with real-time feedback on assignments to highlight misconceptions and next steps.
- A platform that remembers their history, tracking progress, engagement, and patterns over time, so instructors and advisors can better support them when it matters most.
The goal is to surround students with an intelligent layer of just-in-time help, grounded in the courses they’re actually taking.
What this means for academic and IT leadership
For the research team, faculty leads, and IT/security partners, this partnership is designed to make working with AI at scale safer and more manageable:
- Permission management and role-based access that distinguish students, instructors, and administrators across multiple campuses and schools.
- Centralized API key, seat, and usage management across multiple LLMs, so model access and technical controls live in one place instead of being scattered across individual subscriptions.
- AI and data governance within the platform: accessibility, privacy, and security controls across all provisioned LLMs under a single umbrella, plus analytics aligned with SUNY’s research methodology to measure actual impacts on teaching and learning.
- Rich analytics and dashboards that surface learner performance and institutional trends so academic teams can make data-informed decisions about where AI is helping and where more support is needed.
This is the kind of infrastructure that lets institutions move fast with AI while maintaining clarity around responsibilities, privacy, and long-term sustainability.
Why we’re excited about this partnership
What makes this work special isn’t just the technology; it’s the people. Empire State University is bringing together researchers, faculty, and instructional designers who are deeply committed to human-centered, outcome-aligned teaching that meaningfully supports student learning and success. Our role at ibl.ai is to match that commitment with an AI platform that is flexible, LLM-agnostic, and built around institutional control of data and code, plus a support team that shows up as collaborators, not just vendors.
We’re looking forward to learning alongside the full team of collaborating campuses and sharing what we discover about multi-LLM teaching strategies, effective AI mentors, and new models of faculty-led innovation in higher education.
To learn more, visit https://ibl.ai/product/mentorai