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AI for Education & EdTech

AI for adaptive learning, automated assessment, intelligent tutoring, and student-success analytics — built for schools, universities, and edtech companies that have to teach more learners with the same faculty.

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34%
Improvement in learning outcomes
70%
Reduction in grading time
45%
Student engagement increase
50K+
Personalized learning paths created

Trusted by teams at MatchWise, ServiceCore, QuantFi, Desson Abogados, Mexico Por el Clima, and others across the US and LATAM.

What we build

Anatomy of an AI workflow for Education & EdTech

Each ships in 8–12 weeks. Pick a workflow to see what goes in and what comes out.

Adaptive tutoring & student copilot

Curriculum-grounded tutors that diagnose each student's current understanding, sequence content to fill specific gaps, and provide Socratic-style coaching. RAG over your textbook chapters, lecture notes, and worked examples — the tutor teaches what the course actually teaches, not the open web.

1:1 tutoring out of reach for most students24/7 curriculum-grounded tutor per learner

Inputs we read

  • Course textbooks, lecture notes, and worked examples
  • Standards alignment (Common Core, NGSS, AP, IB, SEP, MEC)
  • Per-student mastery trace from prior work
  • LMS assignments and gradebook (Canvas, Moodle, Brightspace)
  • Faculty-approved Socratic prompting policies

Outputs delivered

  • Adaptive item selection at the productive-struggle zone
  • Scaffolded hints that resist giving away final answers
  • Knowledge-tracing estimates per skill, per student
  • Multilingual (EN/ES/PT) tutor sessions
  • Citations to source for every factual claim

Decide your path

Build, buy, or partner?

Three real options, each with different trade-offs on cost, control, and customization.

Khanmigo · Riiid · Squirrel AI

Vendor SaaS

Best for: K–12 districts and consumer learners who need an off-the-shelf tutor for a major subject

Data control
Vendor-controlled; data may flow to third-party LLMs
Customization
Low — the vendor's pedagogy, not yours
Time to value
Days to weeks
Cost (3 yr)
Per-seat license fees that scale with enrollment
Recommended

Clearframe partner build

Best for: Universities, large districts, and edtech companies with distinctive curriculum, multilingual cohorts, or accreditation requirements

Data control
Your environment; no vendor training
Customization
High — grounded in your curriculum, standards, and rubrics
Time to value
8–16 weeks per workflow
Cost (3 yr)
Predictable; pays back within 1–2 academic terms
DIY

In-house build

Best for: Universities and large edtech companies with a 10+ engineer team

Data control
Full control
Customization
Full
Time to value
12–18 months to first production system
Cost (3 yr)
Highest upfront, lowest recurring

What is AI for education?

AI for education is the application of large language models, machine learning, and natural language processing to the work that drives learning outcomes — personalized instruction, formative assessment, content creation, student support, and early intervention. It does not replace teachers; it removes the mechanical layer of grading, content authoring, and individual practice support so educators can spend more time on the pedagogical work that actually moves outcomes.

Schools, universities, and edtech companies face the same equation: more learners, the same number of faculty, and rising expectations for personalized experience. We build AI that scales the one-on-one tutoring relationship — the single most evidence-backed intervention in education research (Bloom's two-sigma problem) — to every student in the cohort, without sacrificing the rigor and integrity institutions are accountable for.

Glossary

Key terms on this page

LMS (Learning Management System)

The institutional platform — Canvas, Blackboard, Moodle, D2L Brightspace, Google Classroom — that holds courses, gradebooks, and student work.

SIS (Student Information System)

The system of record for student demographics, enrollment, transcripts, and financial aid — Banner, Workday Student, PeopleSoft, PowerSchool.

RAG (Retrieval-Augmented Generation)

A pattern where an LLM answers using your curriculum and reference materials, with citations to source, instead of generating from open-web training data.

Formative assessment

Low-stakes practice that informs teaching, distinct from summative (high-stakes graded) assessment. AI is much safer in formative contexts, where errors don't carry transcript consequences.

Learning analytics

The measurement, collection, analysis, and reporting of data about learners and their contexts — used here for early-warning, mastery tracing, and program-level outcome reporting.

How we work

What the engagement looks like

A typical first engagement runs 8 to 16 weeks and ships one production-grade workflow — most often a curriculum-grounded tutor for a high-enrollment course, an essay-feedback system for a writing program, or an early-warning dashboard for the success-coaching team.

1–2 weeks

Step 1

Paid scoping sprint

Map the curriculum, LMS integration, faculty stakeholders, and success metrics. Agree on calibration sets and bias-audit scope with program leadership.

Curriculum and LMS integration mapFaculty-graded calibration setSuccess criteria and bias-audit scope
5–11 weeks

Step 2

Build

Same senior engineers from kickoff to deploy. Weekly demos against faculty-graded benchmarks. RAG over your approved curriculum — never open-web training.

Working model with weekly demosCalibration report (kappa, accuracy, fairness)LTI 1.3 integration with the LMS
Week 8–16

Step 3

Opt-in pilot

Roll out as an opt-in pilot in one course, one program, or one cohort before scaling. Learning-analytics dashboard wired into the LMS for program directors, deans, and institutional research.

Opt-in faculty and student pilotBias-audit and learning-gains reportProgram-level analytics dashboard

We don't ship demos. Every deployment is measured against learning gains on validated assessments, course pass rates, term-to-term retention, faculty time saved, and student- and faculty-reported usefulness.

How we handle your data

Education AI lives on student records. Our deployments keep student data inside your environment or under FERPA-compliant data processing agreements, never train models on student PII, and produce full audit logs of every model decision touching a student record.

What we do

Your student data stays in your environment
No third-party model training on student PII
Bias audits before high-stakes use
Per-query audit logs
Faculty-in-the-loop on every grading and tutoring decision

Architectures designed to meet

FERPA
COPPA (K–12 under-13 learners)
SOC 2 controls
GDPR and LATAM equivalents (LFPDPPP, LGPD)
State student-data privacy laws (CA SOPIPA, NY Ed Law 2-d)

We don't carry these certifications ourselves — your firm's compliance posture stays yours to claim.

Frequently asked questions about AI for education & edtech

Will AI tutors replace teachers?
No. AI tutors handle the high-volume, low-judgment layer — practice problems, formative feedback, knowledge checks, scaffolded hints — that currently consumes teacher hours without exercising their pedagogy. Teachers spend more time on small-group instruction, conceptual coaching, and the relational work that drives outcomes.
How accurate is AI essay grading and is it fair?
Modern LLM-based scoring, when calibrated against rubrics and human-rater samples, typically achieves quadratic weighted kappa above 0.80 — equivalent to or better than the agreement between two trained human raters. We always pair AI scoring with random human review (typically 5–15% of essays) and bias audits across demographic segments before any high-stakes use.
Will student data train someone else's model?
In our deployments, no. We use models in inference-only modes, route through endpoints that contractually exclude training (Azure OpenAI, AWS Bedrock, Anthropic on commercial terms), and deploy in your environment when sensitivity requires it. We sign FERPA-compliant data processing agreements and document data flows for institutional counsel.
How do you prevent AI tutors from giving students incorrect information?
We ground tutors in retrieval-augmented generation (RAG) over the institution's approved curriculum and reference materials, so the model can only respond using verified content with citations to source. We add Socratic prompting that resists giving away final answers and a refusal layer for anything outside the retrieved corpus.
Can AI predict which students are at risk of dropping out?
Yes — early warning models combining LMS engagement signals, assignment patterns, attendance, and gradebook data routinely identify at-risk students 4–8 weeks before traditional indicators trigger. The hard part is the intervention, not the prediction. We design these systems with the success-coaching team, not just the data team.
How do you handle academic integrity in an AI-everywhere world?
We don't sell AI-detection products — they have unacceptable false-positive rates against multilingual and neurodivergent writers. We help institutions redesign assessment toward in-class, oral, draft-trail, and process-graded work, and we deploy AI tutors that show their reasoning so faculty can have informed conversations with students about appropriate use.
Can this work for Spanish-language and bilingual programs?
Yes. We deploy multilingual stacks for English, Spanish, and Portuguese — including LATAM-specific curricular alignment (SEP standards in Mexico, MEC in Brazil) — and the architecture extends to indigenous languages where training data and community partnership allow.

Related services

ai machine learninggenerative airetrieval augmented generationchatbots ai agentsnatural language processingconsumer apps

Most education & edtech teams we work with ship to production in 90 days.

Worth 30 minutes to see what that would look like for your firm? Book a call with one of our senior engineers — no sales handoff, no deck.

Book a 30-minute call