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AI FOR EDUCATION & EDTECH

Custom AI/ML solutions for education & edtech industry

Custom AI and ML for K-12 districts, higher-ed institutions, and edtech platforms. Personalized learning, enrollment intelligence, content moderation, and outcomes analytics — built for FERPA and accreditation.
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84 AI laws

Enacted across 27 states so far in 2026, including Idaho SB 1227, which makes district AI policies and AI data-privacy rules mandatory.

RAG-required

Student PII must stay out of foundation-model training data — architecture matters more than vendor brand.

WCAG 2.1 AA

Binding on public schools and universities under ADA Title II by April 26, 2027, alongside FERPA, COPPA, and SOC 2 Type II.

Achieve immediate, organization-wide results

Six measurable outcomes across learning, enrollment, and student success — deployed in months, not years.

Personalized Learning Paths

Adaptive content sequencing per student. Flags struggling learners and recommends interventions before they fall behind.

Enrollment & Yield Modeling

Per-applicant likelihood-to-enroll, financial-aid optimization, and pipeline forecasting for higher-ed admissions.

Early-Warning & Retention

Student-success models flag attendance, grade, and engagement drops 4–8 weeks before formal early-alert windows.

AI Content Moderation (Safe-for-Education)

Multi-modal classifiers tuned for K-12 and higher-ed policy with explicit FERPA / COPPA boundaries.

Administrative LLMs

Email triage, FOIA / records response, grant writing, and curriculum-alignment automation.

Research AI & Literature Synthesis

RAG-grounded research assistants over your private corpus + public literature (PubMed, arXiv, Semantic Scholar). Custom alternative to Elicit/Consensus/Scite for institutions that can't ship research data to third-party SaaS.

Capabilities across the education & edtech value chain

Personalized Learning & Outcomes

  • Adaptive content sequencing per student
  • Mastery and prerequisite-gap detection
  • Tutor LLMs with curriculum-aligned guardrails
  • Formative-assessment auto-generation

Enrollment, Retention & Student Success

  • Yield modeling and financial-aid optimization
  • Early-warning models for attendance and grade drops
  • Student-engagement and LMS-activity analytics
  • Intervention-recommendation engines for advisors

Administrative Automation & Content

  • Email triage and family-communication LLMs
  • FOIA / public-records response automation
  • Grant writing and curriculum-alignment assistance
  • Research-AI literature synthesis over private corpora + public papers

Trust, Safety & Compliance

  • AI-content moderation tuned for K-12 / higher-ed policy
  • Plagiarism and AI-detection with explainable scoring
  • FERPA / COPPA / state-law audit trails
  • Accessibility (WCAG 2.1 AA) compliance checking
From the playbook

How a 24-campus university system lifted first-year retention 7 points and added $18M in tuition retained

A 24-campus university system was facing 5-point first-year retention declines as the demographic cliff intensified. We built an early-warning student-success model fusing LMS engagement, attendance, grade trajectory, and financial-aid status — surfacing high-risk students 6–10 weeks before mid-semester academic alerts. Advisors received prioritized outreach lists with the specific risk driver and intervention recommendation per student (academic vs financial vs social). First-year retention lifted +7 percentage points system-wide, equivalent to $18M in tuition retained annually. The same platform now feeds yield modeling for incoming classes — improving net-tuition-revenue forecasting accuracy by 12%.

See more case studies →

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Speak with an education & edtech AI expert

A 45-minute scoping call. We’ll come prepared with your enrollment and retention numbers, your student-data constraints, and a directional read on which models move the needle at your institution.

Ask us about

  • Adaptive learning paths and tutor LLMs with curriculum guardrails
  • Enrollment yield and financial-aid optimization
  • Early-warning retention models with intervention recommendations
  • FERPA / COPPA-compliant ML architecture (RAG-first)
  • Custom research-AI with RAG over your corpus + public literature (Elicit/Consensus alternative)
  • Email and family-communication LLMs for K-12 staff

    Contact Us
    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    Frequently asked questions

    FERPA and COPPA compliance are non-negotiable. All training and inference happens in your environment or single-tenant cloud under a school-official designation. We use RAG architectures that prevent student PII from entering foundation-model training data. We comply with state-level student-privacy laws (CA SB 1177, NY Education Law 2-d, Idaho SB 1227, plus 30+ state-specific frameworks) and with the FTC's amended COPPA Rule, which took full effect on April 22, 2026 and requires a written data-retention policy and separate parental consent for third-party disclosure.
    Yes, by default. AI-generated content (tutoring responses, summarized materials, generated assessments) ships with semantic HTML, ARIA labels, alt text, and keyboard-navigable controls. We test against axe-core 4.12 and ANDI automated scanners plus manual screen-reader review. DOJ extended the ADA Title II compliance dates by one year in April 2026: public entities serving 50,000 or more people must meet WCAG 2.1 Level AA by April 26, 2027, smaller entities and special districts by April 26, 2028. There is no exception for course content, so anything we generate into an LMS (Canvas, Brightspace, Schoology) has to meet the standard too.
    Every model that touches student-level decisions (grades, discipline flags, performance predictions, intervention recommendations) ships with model cards, decision-explainability artifacts, and an opt-out mechanism per student/parent. Documentation supports the disclosure requirements of 2026 state laws such as Idaho SB 1227, which mandates a statewide K-12 AI framework, local district policies, AI data-privacy rules, and a bar on AI replacing human teachers. MultiState tracked 134 AI-in-education bills across 31 states in the 2026 session, so we build the audit trail to a common denominator rather than to one state.
    Yes. The underlying ML platform (data ingest, feature store, model registry, monitoring, FERPA audit logging) is reusable across customer types. Domain models differ — K-12 focuses on adaptive learning and safety, higher-ed on retention and yield, edtech vendors on personalization and content moderation — but most multi-segment players run one shared stack with multiple model families.

    Explore AI/ML solutions for education & edtech

    Ready to talk education & edtech AI?

    Start with a 45-minute strategy session. We come prepared with a directional read on your operation and a scoped proposal.

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