From the Student Success edition of September 22, 2026
Ivy Tech design case: HITL GenAI advising on public data with FERPA-safe separation
Ivy Tech Community College of Indiana · Higher education / academic research · United States (community college pathway advising; transferable with limits)
- Publisher
- Research Square
- Original publication
- 2026-07-17
- Source retrieved
- 2026-09-23
What happened
Design-based case derives eight principles for community-college GenAI pathway advising: requirement groups not flat checklists; GenAI as explanation/option-framing not system of record; separate public program data from protected student records; human advisor review for consequential recommendations; plus agency, non-deficit language, visible uncertainty, and non-reductive workforce framing. Prototype can run FERPA-safe on public data alone. Why now: absent from archive and newly salient after ACC Digital Twin early-alert coverage and Pass A evidence that GenAI design/governance dominate raw access.
Why it matters
Gives U.S. community colleges a privacy-first blueprint for GenAI advising as campuses scale early-alert and pathway bots without waiting for outcome RCTs.
Evidence and measured results
Artifact/workflow design-based research on public curricular data; no learner-use or retention outcomes reported.
Limitations and uncertainty
Single-case design study; no measured student outcomes; principles not yet trial-validated.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-23; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Community colleges want GenAI advising at scale but face FERPA risk and fear of chatbot degree planners.
- Stakeholders
- advising leadership
- student success
- IT security/registrar
- academic affairs
- Discovery questions
- Can pathway Q&A run on public catalog data only?
- Where must humans approve recommendations?
- How are flexible requirement groups modeled today?
- Value hypothesis
- Bounded value: deploy a public-data HITL advising explainer before any student-record GenAI joins.
- Potential engagement
- 4–6 week design sprint: requirement-group model + privacy architecture review—not an outcome guarantee.
- Unsupported claims
- Retention or completion gains from this design alone
- Autonomous GenAI degree planning
- Transfer without local catalog modeling
Pre-sales engineering
Role takeaway
- Fit
- Fits as public-data advising explanation service with optional later SIS-gated what-if module.
- Architecture and integration
- Catalog/requirement-group service → RAG GenAI → advisor console; no default student PII in model context.
- Prerequisites
- Public catalog extract
- Requirement-group schema
- Advisor review UI
- Constraints
- Design-only evidence
- Single institution case
- No learner outcome data
- Security
- Enforce public vs protected data planes; audit prompts for accidental PII.
- Proof of value
- Privacy review pass
- Advisor task-time on review queue
- Student comprehension of pathway options in usability tests
Delivery
Role takeaway
- Work
- Model requirements, launch public-data GenAI explainer, train advisors, document escalation.
- Dependencies
- Registrar data
- Advising staff time
- Security review
- Ownership
- Advising leadership with CISO/registrar co-sign.
- Skills and adoption
- Train advisors to treat GenAI as option-framing, not authority.
- Governance checkpoints
- FERPA assessment
- Consequential-recommendation gate
- Ambiguity display QA
- Acceptance criteria
- No student PII in GenAI context for v1
- Human sign-off on consequential recommendations
- Risks
- Flattening flexible requirements
- Premature SIS integration
- Students over-trusting GenAI plans
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Split public catalog/requirement-group services from student SIS; GenAI explains options; advisors approve consequential paths.
Governance
Who approves, reviews and stays accountable for outcomes?
Codify human-in-the-loop for high-stakes recommendations; make curricular ambiguity visible rather than deterministic.
Security and privacy
What data, permissions and controls need testing?
Default to public-data advising mode; require additional permissions/governance before student-specific what-if tools.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Non-deficit language and visible uncertainty support equitable pathway agency; workforce alignment must not collapse advising to labor demand alone.
Procurement
What should contracts, pricing and exit terms secure?
Specify public-data-first advising GenAI with human-review requirements in RFPs; defer student-record features.
Operating model
Which teams own the service once it runs?
Operate GenAI as advising explanation capacity while advisors retain decision authority on consequential paths.
Publication history
- 2026-09-22Student Success · Issue 174 resources
Stable resource ID: peck-hitl-genai-cc-advising-privacy-20260717