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AI-native education & EdTech infrastructure

Move beyond static video courses. Autonomous AI learning systems built for measurable mastery.

I build custom-engineered learning architectures on multimodal audio models, deterministic rubric engines and adaptive diagnostic loops — voice evaluation, rubric-exact writing feedback and study plans that rewrite themselves, on infrastructure you control.

Book a 20-min audit Explore learning blueprints
  • Feedback while the learner is still practising
  • Deterministic CEFR / exam rubric alignment
  • No per-seat LMS tax

Engineered for high-throughput pedagogical platforms

  • Gemini Multimodal Audio
  • Claude
  • Whisper
  • n8n
  • PostgreSQL / pgvector
  • Next.js
  • Canvas / Moodle LTI
The retention cliff

Slow feedback, shallow chatbots, and rigid syllabi drive students away.

Students abandon digital courses when feedback takes days and practice feels disconnected from exam reality.

The human grading bottleneck

Human coaches reviewing spoken drills and essays create grading queues. Feedback latency destroys learning momentum and forces academies to artificially cap cohort sizes.

The fix

Multimodal voice and essay evaluation pipelines that deliver phoneme-level, rubric-calibrated scoring while the learner is still practising, not days later.

The “fuzzy praise” chatbot trap

Generic ChatGPT wrappers offer superficial encouragement and basic grammar fixes without evaluating against structured band criteria, task achievement, or hesitation latency.

The fix

Deterministic rubric engines built to score against CEFR, TOEFL, IELTS, and professional accreditation standards — the same criteria a human examiner uses.

The static syllabus trap

Linear course curriculums ignore individual competency gaps. Advanced students get bored, struggling students hit a wall, and both groups lose interest before completing the program.

The fix

Dynamic diagnostic engines that continuously calibrate tomorrow's practice drills around yesterday's recorded weaknesses.

Modular production engines

EdTech systems engineered for measurable learning outcomes.

Every system runs on infrastructure you control, keeping learner data on your own stack.

Voice & oral evaluation

Multimodal evaluation engine

For Language schools, flight/medical training institutes, and test-prep academies (TOEFL, IELTS, Cambridge, Duolingo).

What's included (4)
  • Direct acoustic waveform processing to assess phoneme-level pronunciation, cadence, pause duration, and conversational fluency.
  • Real-time hesitation measurement and grammar validation under timed exam constraints.
  • Actionable phonetic correction models and synthesized native-speaker comparison audio.
  • Low-latency evaluation on dedicated GPU/multimodal inference endpoints, measured on your own data before launch.
  • Gemini Multimodal
  • Whisper / Qwen
  • WebRTC Audio Stream
  • PostgreSQL

Rubric & writing assessment

Deterministic assessment engine

For High-stakes exam academies, university admissions platforms, and professional certification programs.

What's included (4)
  • Grades open-ended essays and text against official criteria (e.g., Task Achievement, Cohesion & Coherence, Lexical Resource).
  • Pinpoints logic flaws, native-language interference (“false friends”), and structural syntax errors.
  • Generates pedagogical inline margin notes detailing exact rubric deductions.
  • Calibrated against official band criteria, with score bands validated during onboarding, not assumed.
  • Claude
  • JSON Schema Validator
  • n8n Webhook Router
  • Vector Embeddings

Adaptive diagnostic & syllabus

Syllabus generator

For Cohort communities, corporate upskilling platforms, and asynchronous course providers.

What's included (3)
  • 15-minute dynamic diagnostic assessment mapping learner baseline across fine-grained sub-competencies.
  • Ingests target band score, available weekly study hours, and exam date to generate an adaptive dated schedule.
  • Continuous syllabus rewriting: weights upcoming drills toward weakest sub-skills based on daily performance data.
  • Adaptive Engine Logic
  • PostgreSQL (pgvector)
  • Next.js App Router
  • REST API

Course authoring & micro-lessons

Autonomous authoring pipeline

For Educational publishers, corporate L&D teams, and multi-instructor academies.

What's included (3)
  • Ingests raw PDFs, lecture videos, and curriculum docs to automatically generate structured micro-lessons.
  • Produces retrieval-based spaced repetition quizzes, flashcards, and conceptual mind-map summaries.
  • Native trilingual localization (e.g., EN / ES / RU) preserving pedagogical nuance rather than crude literal translation.
  • Document Chunking Engine
  • Claude
  • n8n Content Pipeline
  • Markdown / SCORM Export
Outcome engineering

Built for the complete learner lifecycle, from first diagnostic to workplace fluency.

  1. Diagnose

    Adaptive baseline

    15-min dynamic testing maps exact sub-skill point leaks.

  2. Assess

    Rubric simulation

    Full mock tests scored against official rubrics under real exam constraints.

  3. Apply

    Document refinement

    AI personal statement & resume analysis catching false-friend translations and structural flaws.

  4. Rehearse

    Voice simulation

    Real-time voice simulation for admissions, visa, and corporate interviews.

  5. Placement

    Professional fluency

    Workplace-ready communication engines for meetings, correspondence, and technical reporting.

Sample report

The feedback a learner actually gets.

A rubric report from the writing assessment system. Every score points back to the band descriptor it came from, and every weakness turns into a drill.

Scores cite the rubric
Each criterion is scored against the official band descriptors, so a teacher can check any mark in seconds.
Weaknesses become next week's work
The lowest criterion decides which drills the learner sees next. The syllabus rewrites itself.
Teachers stay in the loop
Scores that sit on a band boundary are flagged for human review instead of being published silently.
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report / writing-task-2 / sample learner [SAMPLE DATA]

Writing Task 2: “Should cities ban private cars?”

6.5 Overall band

  • Task response 7.0

    Clear position held throughout. The second argument needs a concrete example.

  • Coherence & cohesion 6.5

    Paragraphing is logical, but the same linking word opens four sentences.

  • Lexical resource 7.0

    Good range on transport vocabulary, two collocation errors.

  • Grammatical range & accuracy 6.0

    Weakest criterion. Complex sentences attempted, but article errors recur in five of eleven sentences.

Next drills

  • Articles with countable and uncountable nouns, twelve targeted items
  • Rewrite paragraph three using two subordinate clauses
  • Sent to a teacher for review, because the band sits on the 6.0 / 6.5 boundary
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Engagement timeline

From rubric calibration to live student deployment in 3 weeks.

  1. Step 1
    Days 1–3

    Pedagogical rubric & architecture audit

    • Audit curriculum rubrics, student drop-off analytics, and target exam benchmarks; design evaluation schemas.
  2. Step 2
    Weeks 1–2

    Multimodal model orchestration & API mapping

    • Build n8n pipelines, connect multimodal voice/audio models, integrate vector stores, and connect DB endpoints.
  3. Step 3
    Weeks 2–3

    Sandbox calibration & grader benchmarking

    • Benchmark evaluation engines against your historical human-graded tests to verify calibration and parity.
  4. Step 4
    Week 3+

    Live LMS integration & learner telemetry

    • Production launch with real-time grading telemetry, exception routing, and cohort completion analytics.
FAQ

Frequently asked questions.

Anything not covered here gets answered on the call.

Book a 20-min audit
Can AI evaluation match the accuracy of experienced human examiners?

When bound to deterministic rubrics, it gets close. I calibrate the system with explicit scoring matrices and few-shot examples from your own graded work, and we validate parity against your historical grades before launch — I won't promise a number until it's measured on your data.

How does the system handle heavy accents and microphone background noise?

I use multimodal audio models that process the acoustic waveform directly, rather than relying solely on imperfect speech-to-text transcripts. This separates ambient noise from true phoneme pronunciation, cadence, and fluency.

Can this connect with our existing LMS (Canvas, Moodle, custom web app)?

In most cases, yes. I build on standard REST APIs, webhooks, and LTI standards. The evaluation engine can sit headless in the background, receiving submissions from your platform and pushing grades back to your database.

How do you prevent students from using prompt-injection attacks to get high scores?

Student inputs are isolated inside structured evaluation pipelines. They never interact directly with the model's system instructions. Output schemas are validated against JSON Schema / Zod before being accepted.

Free 20-minute audit

Ready to turn your educational platform into an AI-native learning engine?

I'll look at your current evaluation bottlenecks and your grading rubric, and outline the architecture to get you to instant, calibrated feedback.

Alex Shilov

The 20-minute audit is free, with no obligation, and it's with me, not a sales team. I read every request myself and reply within one business day.

Alex Shilov · AI Systems Builder and Auditor

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