Student support triage, content drafting, and enrollment personalization for edtech platforms, schools, and corporate L&D teams. $3,999/mo retainer.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
Edtech founder, online-school operator, or corporate L&D lead whose student support queue is growing faster than the team, whose instructors spend hours on admin instead of teaching, and whose enrollment funnel treats a career-changer and a recent grad the same way.
The pain today
- Student support tickets pile up while response times stretch to days
- Instructors burn hours on quizzes, practice problems, and reading lists instead of teaching
- Enrollment funnels send identical emails to prospects with completely different goals
- Routine admin — scheduling, tech issues, how-to questions — reaches instructors who shouldn't be handling it
- Past AI experiments raised real concerns about accuracy and learning outcomes
The outcome you get
- Student support triage with AI-drafted responses held for human review before sending
- Content drafting that gives instructors a reviewed first draft, not a blank page
- Enrollment sequences personalized by program interest and career stage
- Admin and tech tickets resolved without reaching instructors
- Clear guardrails so AI never touches academic judgment or sensitive student matters
Where AI actually helps in education without harming learning outcomes
AI automation for education works in three distinct zones. Student support triage handles the volume of admin and tech questions — assignment submission, portal access, schedule lookups — so instructors stop fielding requests that a well-built workflow could answer in seconds. Content drafting gives instructors a structured first draft of supplementary material: quizzes, practice problems, reading lists, cohort feedback templates. The instructor reviews and approves before any student sees it. Enrollment personalization serves prospects tailored content based on program interest and career stage, reviewed by marketing before it goes out.
None of these replace teachers. Teachers spend roughly 40% of their time on administrative work that does not require their expertise. Getting that time back is the actual value.
Student support triage with AI-drafted responses held for human review before sending
Student support triage that protects the instructor-student relationship
Incoming support tickets categorize on arrival: admin, tech, academic, or personal. Admin and tech tickets get AI-drafted responses queued for a support agent to review and send. Academic questions route directly to the relevant instructor with no AI draft — the instructor needs to engage directly, and an AI-generated academic response is both an accuracy risk and a pedagogical mistake. Personal or sensitive matters go to human staff immediately, with no AI summary, no AI draft, no AI involvement.
For edtech platforms running high ticket volumes, this pattern cuts median response time substantially while keeping the instructor-student relationship intact for the conversations that matter. I design the routing rules in the first two weeks, then refine them based on real ticket data from your system.
30+: Active users.
Content drafting: instructor reviews everything before students see it
I build structured prompt workflows that generate supplementary content from a lesson plan or learning objective. Quiz questions from a topic outline. Practice problems from worked examples. Reading lists from a keyword and level. Feedback template drafts from a rubric.
Every output goes into a review queue. The instructor edits, approves, or rejects. Nothing reaches students without that sign-off. For instructors shipping multiple cohorts per year, this saves hours per cohort launch without removing the instructor's judgment from the process. Custom prompts per instructor or program capture teaching style and vocabulary over time so the drafts get closer to what each instructor would write.
LMS and enrollment system integration
Student support automation only works if the AI can read the context it needs. I integrate with the LMS — Canvas, Moodle, custom platforms — and where applicable the Student Information System, so the support workflow can pull the right course, section, and enrollment data when drafting a response.
For enrollment personalization, the integration point is usually a CRM or marketing platform. Prospect data (program interest, prior education, career stage) feeds the personalization logic. The output is a set of variant emails or landing page content that the marketing team reviews before scheduling. For edtech platforms with many programs, this lifts enrollment conversion on the programs that are undersold. For single-program schools, simpler conditional logic handles most of the job.
Corporate L&D: the same patterns, different content
Corporate L&D teams have the same structural problem schools do: content creation is slow, support for learners is reactive, and personalization is near zero. The AI automation patterns transfer directly. Support triage handles learner questions about modules, deadlines, and technical access. Content drafting generates practice scenarios, assessment questions, and manager discussion guides from a course outline. Enrollment personalization maps to onboarding path assignment based on role and department.
The main difference is compliance. L&D content for regulated industries — financial services, healthcare, law — requires additional review layers. I scope those in the first month and set the review workflows accordingly.
Instill: a structured-prompt library for repeatable teaching tasks
I built Instill as a self-initiated AI skills platform. It currently has 30+ active users, 1,000+ skills saved, and powers 45+ projects. The stack is Next.js, React, TypeScript, PostgreSQL, and the MCP Protocol.
The structured-prompt library pattern that runs Instill maps directly to education. A library of reviewed prompts for quiz generation, lesson plan drafts, feedback language, and personalized comms gives instructors and curriculum designers a consistent, maintainable tool rather than a blank ChatGPT window. The library improves as instructors iterate on prompts and flag outputs that missed the mark.
When AI should not be involved
Mental health disclosures, personal safety situations, and any matter involving a minor in distress route to trained human staff with no AI contact. Crisis escalation flows must be human-only.
For K-12 operators where FERPA and COPPA apply, I scope AI features tightly to teacher and admin workflows. Student-facing AI in K-12 requires careful data minimization and, for under-13 learners, parent consent and COPPA-compliant data handling. The first month of the engagement maps which workflows are AI-appropriate and which are human-only. Most operators have more AI-appropriate work than they expect and a clear set of hard limits that are easy to honor once they are defined.
Pricing is $3,999/mo. Covers integration, prompt engineering, LMS or SIS connection, monitoring, and ongoing iteration. 14-day money-back guarantee. Cancel anytime. 100% code ownership under Work Made for Hire.
Recent proof
A comparable engagement, delivered and documented.
An AI knowledge base your whole team uses via MCP
A personal library for Skills, Agents, and Rules, built once, used across Claude, Cursor, and any MCP-compatible AI tool. Save your best workflows once. Run them anywhere.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
Assignment submission questions, portal access resets, schedule lookups, tech troubleshooting, and enrollment status inquiries are the highest-volume, lowest-complexity tasks in most student support queues. These are strong AI candidates because the answers are factual, repeatable, and can be verified against system data. Academic advising, grade disputes, and anything requiring professional judgment stay with humans. The split is usually 60-70% routine admin, 30-40% human-required — that ratio varies by institution type.
AI-drafted supplementary content — quizzes, practice problems, reading lists — is generally accepted when an instructor reviews and approves it before students see it. AI-authored course content published without instructor review is a different matter: accuracy risk, academic integrity risk, and an ethics question most institutions are not ready to answer. The line I use: AI drafts, instructor approves, students see approved content. Transparency about AI's role in content creation is part of maintaining student trust.
FERPA-covered student data goes only to LLM providers with documented FERPA-compliant terms — specific enterprise tiers or self-hosted models. Student records are not sent to consumer AI APIs. For K-12 where COPPA applies to under-13 learners, AI features target teachers and admin staff rather than students directly, and any student-facing use requires data minimization and parent consent. I help map the data flow in the first engagement month to match your institution's regulatory posture.
Objective questions — multiple choice, short answer with a clear correct answer — can be scored automatically with high reliability. Essay grading is a different matter. AI can draft feedback for instructor review, but assigning a final grade should stay with the instructor. For high-stakes assessments like certification exams or course finals, I recommend AI does not touch grading at all. The practical approach: AI pre-scores and drafts feedback; the instructor finalizes. That keeps the academic relationship intact and the grades defensible.
Prospect signals — program interest, career stage, prior education, how they found you — feed conditional logic that selects or generates tailored email sequences and landing page content. The marketing team reviews generated variants before any campaign runs. For edtech platforms with many programs, this improves enrollment on programs that tend to get generic messaging. The setup takes two to four weeks: integration with your CRM or marketing platform, signal mapping, variant creation, and review workflow. Scope depends on how many programs and how much existing prospect data you have.
The underlying patterns are the same: support triage, content drafting, and personalization. The differences are compliance and content type. Corporate L&D in regulated industries — healthcare, financial services, legal — needs additional content review layers before anything goes to learners. Corporate onboarding automation also maps to role and department rather than academic program. The tech setup is similar; the review workflows and compliance scoping take longer in regulated corporate contexts.
The first month covers scoping, integration setup, and routing rule design. By the end of month one, student support triage is typically live in a monitored state — human agents review every AI-drafted response before it sends. Content drafting workflows go live in month two once the prompt library has been calibrated against real instructor feedback. Full enrollment personalization, if it involves a CRM integration, runs in month two or three depending on the complexity of your existing data and platform.