# Outcome-Aligned Program Design at Fort Hays State > Source: https://ibl.ai/case-study/fort-hays-state-university ## Case Study How FHSU's Department of Social Work uses ibl.ai to connect program outcomes, course design, and field experience — with faculty keeping control of the pedagogy and the institution keeping its data. Most campus AI deployments answer student questions. This one starts a step earlier: does the curriculum actually line up with the outcomes it claims? FHSU and ibl.ai co-developed a program-building agent and course agents tuned to Social Work's competencies and accreditation requirements. ### Quick Stats - **CSWE-aligned** Accreditation standards built in - **OSCQR** Course-quality rubric applied - **Program-wide** Outcome mapping across the curriculum - **Institution-controlled** LLM-agnostic, data stays put --- ## The Starting Point ### Curriculum alignment is the hard part Accredited programs carry an ongoing burden: proving that every course objective maps to a program outcome, that gaps and redundancies are known, and that the evidence is ready when the accreditor asks. That work is normally manual, and it ages the moment a syllabus changes. The collaboration begins in the Department of Social Work, where faculty and students use ibl.ai to strengthen the connections between learning outcomes, course design, and field-based experience. The shared goal: reduce friction for faculty, increase coherence for students, and deliver authoritative answers tied to program outcomes. --- ## What Was Built ### A live guide, not a static document Curriculum maps and alignment matrices are usually spreadsheets that go stale. Here they are conversational and current — small, purpose-built agents that read the actual course materials. **Program learning outcomes analysis** — Purpose-built agents map course objectives to program outcomes and CSWE standards, highlight gaps and redundancies, and produce an accreditation-ready narrative with a curriculum map and OSCQR-based syllabus annotations. **Comprehensive course redesign** — Faculty get AI-assisted recommendations on assessments, pedagogy, and open educational resources — improving measurability and alignment without giving up instructor agency. **Agentic AI for experiential learning** — A course-integrated agent supports reflection, ethical reasoning, and fieldwork connections, with a prompt library, anonymized transcripts for review, and summary reports that surface improvement opportunities. **Faculty development and support** — Hands-on workshops, weekly check-ins, and no-code configuration inside the platform accelerate iteration and adoption — the agent is something faculty change themselves. --- ## Connected Curricula ### Fitting an existing framework, not replacing it FHSU already had a curriculum-design framework — "Connected Curricula" — built on gap analysis, backward and experiential design, AI-enhanced delivery, and continuous improvement. The platform slots into that loop rather than asking the university to adopt a vendor's methodology. **Gap analysis** — Agents surface where a program's stated outcomes are under-covered or duplicated across courses. **Design and delivery** — Backward and experiential design decisions are made by faculty, with the agent supplying evidence and options. **Continuous improvement** — Program data and outcome analytics feed the next revision, so alignment is a loop instead of a one-time audit. --- ## Who It Serves ### Three different problems, one system **For students** — Faster answers, clearer expectations, and a visible line between what they are asked to do in a course and what the profession will require of them. **For faculty** — Less administrative burden around course alignment, while keeping full control over design and pedagogy. The agent proposes; the instructor decides. **For the institution** — A sustainable, faculty-led model for generative AI that preserves data ownership and reinforces academic standards rather than routing around them. The through-line: AI that strengthens human teaching instead of substituting for it — outcome mapping, program design, and student support under one transparent system the university controls. See the higher-education view: https://ibl.ai/solutions/higher-education --- ## Why It Holds Up ### Accreditation-grade, not demo-grade An agent that summarizes a syllabus is a demo. An agent whose output a program can put in front of an accreditor has to be grounded in the real materials, traceable to a standard, and reviewable by a human before it counts. - Mapped against CSWE competencies, not a generic rubric - Course quality assessed with OSCQR, the standard the institution already uses - Anonymized transcripts available for faculty review - Model-agnostic — the institution picks the LLM and can change it - Data ownership stays with FHSU --- ## Get Started ### Build your own connected-curriculum initiative Map outcomes to standards, redesign courses with evidence, and support students with agents your faculty configure — on infrastructure you own. - 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