Case Study
Outcome-Aligned Program Design at Fort Hays State
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.
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
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.
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.
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.
Three different problems, one system
Program alignment, teaching load, and institutional governance are usually handled by three separate tools. Here they share a source of truth.
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.
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
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.