AI Consulting for Education

FERPA scope is part of design, not an afterthought. Before any code is written, we map which categories of student data the tool will touch (directory info, education records, disciplinary records) and which it won't. We pick AI vendors with business-tier terms that contractually exclude your data from training and define retention windows. We document the data flow end-to-end, where it goes, where it lives, and for how long. If the use case requires personally identifiable student data, we get a Data Processing Addendum signed before kickoff. If the use case can be solved without student PII, we design it that way on purpose, because every system that doesn't see student data is one less FERPA risk surface.

It will if we design for that review from the start. I work with your IT-security team during scope lock so the review isn't a surprise. The deliverables they typically want: a system architecture diagram, a data flow diagram, vendor security questionnaires (SOC 2, ISO, whatever your framework is), the AI vendor's data processing terms, and an incident response plan. I bring those documents in the format your team reads. Where projects fail is when a consultant treats IT as a checkbox at the end. By then, fundamental design choices (where data lives, which API gets called) are baked in. We invert that. IT reviews the design, not the finished product.

Yes, and I'll help you build the board packet. For projects above your approval threshold, we structure the engagement in two phases: a small pre-board phase ($10K-$20K) that produces the FERPA memo, IT review brief, vendor due diligence, and pilot design, and a larger post-board phase that's the actual build. The board packet I help prepare includes a one-page executive summary, the FERPA scope, the IT-security signoff letter, and a 6-month rollout timeline tied to the academic calendar. The phasing also gives the board confidence: they're not approving a black box, they're approving a build that's already been pressure-tested by IT and faculty.

The realistic windows are: summer (May-August) for a fall rollout, winter intersession for a spring rollout, or the first 3 weeks of a term if the tool is meant to be used immediately. Mid-semester rollouts almost always fail because faculty and staff are at peak load and have no bandwidth for change. The best K-12 timing is March-June: scope through spring, build through summer, train in early August, ship at back-to-school. For higher ed, late spring kickoff for a fall pilot is the cleanest path. If you're starting in October and want a January launch, it's possible but tight, and we'll cut scope to make it work.

By taking them seriously. Faculty skepticism is usually about three things: academic integrity (will this be misused by students or undermine grading), workload (is this another platform I have to learn), and quality (does this actually do what's promised). I address each in order. For integrity, we scope tools that augment faculty judgment, not replace it (the grade is always set by a human). For workload, we slot tools into existing platforms (LMS plugins, email, Google Docs) instead of new logins. For quality, we run a small pilot before broad rollout and let skeptical faculty break it. If a tool can't survive a serious instructor's stress test, it shouldn't ship.

Usually, with caveats. Major SIS platforms (Banner, PeopleSoft Campus Solutions, PowerSchool, Infinite Campus) have export options or limited APIs. LMS platforms (Canvas, Blackboard, Brightspace, Schoology, Google Classroom) have more open API surfaces. The realistic approach: read-only integration first, write-back integration second once trust is established. I'll never propose a deep two-way integration in a starter project. The integration choice is also a vendor-evaluation question. If your SIS contract doesn't allow third-party data access, we work around it (often via scheduled exports rather than live API calls). I'll flag that risk during scope lock, not at build.

Three questions: does the vendor's product actually solve your specific problem (most EdTech AI is generic), what's the data ownership clause, and is the price defensible vs. a 6-week custom build. EdTech vendors have an advantage on already-procured platforms (your team trusts them, integration is done) and on use cases too narrow to justify a custom build. They lose on use cases where you need workflow flexibility, data control, or a tool that lives outside their platform. I'll tell you when to buy and when to build. If a vendor product is 80% of what you need at 30% of the cost of custom, buy it. If it's 50% of what you need and locks your data, build.

Slow down on student-facing AI. The risk surface is bigger (FERPA, accessibility, hallucinations affecting a vulnerable population, parental concerns, board scrutiny). I'd rather start with internal staff-facing or faculty-facing tools, prove the model works in your environment, then expand outward to students once your team has muscle memory. When student-facing tools are the right call (admissions FAQs, library research help, narrow tutoring use cases), the design needs explicit guardrails: no personal advice, no medical or mental health topics, clear handoff to humans, transparent disclosure that the user is talking to AI. Get this wrong and you're on the front page of the local paper.

Training in education is different from training in a corporate environment. Faculty don't show up for mandatory all-hands training, and staff are spread across buildings, schedules, and unionized job categories. What works: short asynchronous video walkthroughs (5 to 8 minutes each), a one-page quick-reference per role, and three live Q&A sessions in the first month, scheduled for the times your people actually have free (not 2pm Tuesday when everyone's teaching). For K-12, building-level champions matter more than district-level training. Pick one teacher per building who'll evangelize the tool to colleagues. That's the only training program I've seen actually move adoption numbers in education.

Both, but the engagements are different. With institutions, the work is build-and-handoff: design a tool, ship it, transfer to your team. With EdTech founders, the work is closer to fractional-CTO or technical co-founder: helping you scope your AI product, evaluate models, design the data architecture, and avoid the FERPA and procurement pitfalls that kill EdTech startups when they try to sell into districts. If you're a founder, the engagement is usually monthly retainer plus targeted build sprints. If you're an institution, it's project-priced. Both audiences benefit from the same insight: the technical work is half the battle, the institutional fit is the other half.