AI MVP Development for EdTech Platforms

Building an EdTech MVP is less about a slick course player and more about surviving the interoperability and privacy gauntlet that every district, university, and corporate L&D buyer runs you through before signing. We build products that speak the standards schools actually procure against: LTI 1.3 and LTI Advantage (Deep Linking, Names and Role Provisioning Services, and Assignment and Grade Services) so your tool launches cleanly inside Canvas, Moodle, Blackboard, and Schoology; OneRoster for gradebook and roster sync; QTI for portable assessment items; and xAPI or cmi5 statements flowing to an LRS so learning activity is captured beyond a single SCORM package. Getting these right in the MVP is what turns a pilot into a paid contract.

Student data privacy is the make-or-break layer, and it is not one regulation but a stack. FERPA governs education records for any product touching K-12 or higher-ed institutions; COPPA (and GDPR-K in the EU) constrains anything used by under-13 learners and forces verifiable parental consent flows; and state laws like California's SOPIPA and the Student Data Privacy Consortium's national DPA template dictate what you can retain, mine, or train models on. We architect data minimization, per-district data residency, deletion-on-request, and consent capture into the schema from day one — because retrofitting FERPA compliance after a pilot is how EdTech MVPs die.

The AI that genuinely moves learning outcomes is adaptive, not just generative. We implement knowledge-tracing engines — Bayesian Knowledge Tracing for interpretable mastery estimates or Deep Knowledge Tracing (LSTM-based) when you have enough interaction data — combined with Item Response Theory to calibrate question difficulty and drive computer-adaptive testing. Layer spaced-repetition scheduling on top and the platform stops showing everyone the same linear path and starts targeting each learner's specific gaps, the way our TypeMaster adaptive typing build identified weak finger pairs from keystroke telemetry and generated exercises against them.

AI tutors are the highest-demand feature we get asked for, and also the easiest to build irresponsibly. A tutor that hallucinates a wrong derivation or fabricates a citation is worse than no tutor in an education setting. We ground every tutor in your own curriculum using RAG over the course corpus, enforce retrieval-citation so answers point back to the source lesson, add refusal and 'I'm not sure' guardrails, and keep a Socratic-prompting layer so the model scaffolds toward the answer instead of just handing homework solutions to students. We also wire in AI-generated-content detection awareness so your academic-integrity posture is defensible.

Assessment automation is where AI pays for itself operationally. We build short-answer and essay scoring with rubric-aligned LLM grading plus a human-in-the-loop review queue for disputed scores, automatic distractor and item generation from source material (validated against QTI), and early-warning analytics that flag at-risk or likely-to-drop learners from LMS engagement, assignment-submission, and formative-assessment signals — mapped against competency frameworks so an advisor sees which specific standards a student is missing rather than a single opaque risk number. For language and skills products we add ASR-based pronunciation and fluency scoring aligned to CEFR proficiency levels.

Distribution and access in EdTech run on rails most consumer apps ignore. We integrate rostering and single sign-on through Clever and ClassLink, Google Classroom and Google Workspace for Education, and SIS systems like PowerSchool and Infinite Campus so teachers never manually create accounts. Accessibility is a procurement gate, not a nice-to-have: we build to WCAG 2.2 AA and Section 508, produce a VPAT, and test with screen readers, because a district legal review will reject an inaccessible product outright regardless of how good the AI is.

We ship the EdTech MVP that gets you through a pilot in 2-3 weeks with a team of 15+ engineers, and you keep 100% of the code — no per-seat platform lock-in, no proprietary runtime you can never leave. That means real LTI certification-ready launches, a compliant data model, an adaptive engine you can tune, and instructor and admin dashboards teachers will actually adopt, rather than a demo that collapses the moment a real district IT team asks for a data processing agreement.