# Faculty-Led AI at George Washington University > Source: https://ibl.ai/case-study/george-washington-university ## Case Study How the School of Engineering & Applied Science put course-grounded AI agents in the hands of instructors — student-centered by design, faculty-led in practice, and priced by usage instead of per student. Professor Lorena A. Barba introduced a faculty-configured AI agent at GWU's School of Engineering & Applied Science — one where the instructor sets the persona, supplies the course materials, and moderates how students interact with it. The point was never AI access. Students already had that. The point was AI grounded in the actual course. ### Quick Stats - **Faculty-led** Instructors configure the agent - **Course-grounded** Answers cite course materials - **Any LLM** OpenAI, Gemini, Llama, Anthropic - **Usage-based** No per-student license --- ## The Approach ### Student-centered, faculty-led Most campus AI programs start by buying seats and hoping for adoption. GWU started from the course: what should the AI know, how should it behave, and who decides? The answer to the last question is the instructor. > "What sets our approach apart is its focus on pedagogical design and cost-effectiveness. Unlike enterprise-wide solutions, the pay-as-you-go model could save institutions significant resources while offering greater customization." > — Professor Lorena A. Barba, George Washington University > "Most are using ChatGPT, and not very well. Our AI agent addresses this reality by providing a customizable, course-specific tool that grounds AI responses in course materials." > — Professor Lorena A. Barba, George Washington University --- ## What Faculty Control ### The instructor is the administrator In the faculty demo, Professor Barba added course resources, set the agent's behavior through prompts, and moderated student interactions — then showed the agent answering student questions with course-specific material and pushing back toward critical thinking rather than handing over answers. **Instructor-controlled persona** — The faculty member defines how the agent behaves, what it will and won't do, and the tone it takes with students. **Course resources as the source** — Readings, notes, and problem sets are loaded in, so the agent answers from the course rather than from the open internet. **Context-aware responses** — Retrieval-augmented generation keeps answers tied to the supplied materials, reducing invented or off-syllabus responses. **Model selection, not lock-in** — OpenAI, Google Gemini, Llama, Anthropic — the model is a setting, chosen per course and swappable as the field moves. **Visible usage and cost** — Consumption is tracked through the AI vendor dashboard, so a department can see exactly what a course actually costs to run. **LTI embedding in the LMS** — The agent drops into the existing course shell over LTI — students meet it where they already work, with no separate login. --- ## The Cost Shape ### Per-student licensing is the wrong shape for a campus A per-seat license bills for every enrolled student whether or not they open the tool. Usage-based pricing bills for what a course actually consumes. At a university-sized population the difference is not a discount — it is a different cost curve. | Model | How it bills | List price | |---|---|---| | ChatGPT | Per student, per month | $20 / student / mo | | Microsoft Copilot | Per student, per year | $80 / student / yr | | ibl.ai at GWU | Pay-as-you-go, plus bulk rates | Metered usage | Published list prices at the time of the GWU program. On those figures, ibl.ai's usage-based model came in roughly 85% below ChatGPT and 75% below Microsoft Copilot for the same population. **Enterprise-wide seat license:** billed for every enrolled student, used or not; one vendor's model for every course; generic assistant, no course grounding; cost rises with enrollment, not with value. **ibl.ai at GWU:** pay for the tokens a course actually consumes; model chosen per course, swappable anytime; answers grounded in that course's materials; department-level visibility into real cost. Model the same math for your institution: https://ibl.ai/llm-price-calculator --- ## Measured, Not Assumed ### A program with a research design attached At GWU's TRAILS conference in February 2025, GWU and ibl.ai presented "Student-Centered Approach to AI in the Classroom" — a faculty-tailored program studied the way an academic program should be, with instruments and published findings rather than vendor anecdotes. **AI as tutor, coach, and teammate** — Students used the agent for understanding material, for creative work, and as support inside coursework — three distinct modes, not one chatbot use case. **Real-world data collection** — Surveys, focus groups, and faculty interviews tracked what the agents actually changed — for students and for the instructors configuring them. **Motivation and engagement as the outcome** — The research looked past usage counts to what matters academically: whether the agents affected student drive and performance. **Best practices published for faculty** — Guides and findings were written up so other instructors could adopt the approach — and shared openly to build AI literacy beyond one campus. Conference poster on figshare: https://figshare.com/articles/poster/Student-Centered_Approach_to_AI_in_the_Classroom_Faculty_Tailored_AI_Mentor_Pilot_Program_at_GW_TrailsCon_Conference_/28307570/1?file=52019624 --- ## In Their Words ### Professor Lorena A. Barba, School of Engineering & Applied Science, George Washington University "ibl.ai's generative AI in education is set apart by their unparalleled expertise and their commitment to empowering educators by putting full control in their hands. Their platform enables instructors to customize AI agents, ensuring that the AI responses are always grounded in course materials—minimizing the risk of inaccuracies or hallucinations. For any educational institution looking to lead the future of AI-driven learning, ibl.ai is the gold standard." Read what other institutions say: https://ibl.ai/case-studies --- ## Why It Generalizes ### One course, then a department, then a campus The GWU program is deliberately reproducible. Nothing in it depends on a campus-wide procurement or a single approved model — an instructor can stand up a course-grounded agent, measure it, and hand the recipe to the next department. **Starts at the course** — A department can begin on its own terms, without waiting on an institution-wide procurement cycle. **Configured, not coded** — Faculty add materials and write prompts. Nothing about the setup requires an engineer on standby. **Scales on usage** — Adding a department adds its consumption, not a new per-student line item across the whole population. **The platform underneath:** GWU runs on Agentic OS (https://ibl.ai/product/agentic-os) — model-agnostic, deployable in your own cloud, VPC, on-premise, or air-gapped, with the institution owning the code and the data. See the higher-education view: https://ibl.ai/solutions/higher-education --- ## Get Started ### Run the same program at your institution Course-grounded agents your faculty configure, on any model, priced by what you actually use — and deployed wherever your data has to live. - Book a Demo: https://cal.com/iblai/30min - Get Started Free: https://ibl.ai/join - Download & Own Your AI: https://ibl.ai/download - Calculate your savings: https://ibl.ai/llm-price-calculator