AI-native LMS development — pgvector RAG, video tracking, evaluation automation.

Technical problems we have actually solved in this category

• 01

  AI chatbot — pgvector RAG + 10 Function-Calling tools

  Gemini 3 Flash + pgvector (3072-dim) RAG ingests PDFs, DOCX, and VTT subtitles. A 10-tool Function-Calling agent reaches into course progress, assignments, and grades.

  lms-ct (B2B corporate-training LMS)

• 02

  The trap of video-watching tracking

  Naive currentTime tracking is bypassed by the seek bar. We use a 3-layer pattern: seek-bar restriction + refresh recovery + accumulated actually-watched intervals.

  lms-ct · gangwon-gender-edu

• 03

  NABCD 31-item evaluation automation

  We auto-evaluate 31 NABCD subitems for entrepreneurship capstones and generate a 'Path to Green' step-by-step revision guide. The professor reviews the AI score and confirms the verdict.

  smit-practicum-platform

• 04

  Vimeo integration + dynamic survey engine

  Responsive carve-outs for Vimeo Player overlays, fixed-layout 5-point Likert scales, conditional display, and early-termination logic — the UX details specific to education domains.

  Gangwon Province gender-equity education platform

• 05

  Vercel sin1 serverless with a 1-connection pool

  Worked around Vercel serverless's max=1 connection pool with unstable_cache (60s). Reduced 6 sequential queries to 5 parallel queries, taking sidebar response time from 600ms to 80ms.

  lms-ct

• 06

  Multilingual LMS — lecture content + i18n in parallel

  next-intl + country flags / ISO codes. Same lecture authored in ko / en at the same time, with branching when language priority differs by lecture.

  smit-practicum-platform

Frequently asked questions

What does a RAG chatbot cost to operate?

Gemini 3 Flash + pgvector + Vercel runs at $50–$150/month for 100 monthly users and 5,000 chatbot calls. As traffic grows, model mix (Flash for short answers, Sonnet for precision reasoning) plus response caching keeps the curve flat.

How accurate is video-watching tracking?

A 4-layer pattern — currentTime + seek-bar restriction + refresh recovery + accumulated actually-watched intervals — reaches 95%+ accuracy. Naive currentTime tracking alone falls below 50% because the seek bar bypasses it. lms-ct runs this pattern in production.

Is it better to extend an existing LMS (Moodle, Canvas) or build new?

If AI features + Korean-first UX + industry-specific evaluation matter, building new is faster overall. Bolting RAG onto Moodle costs more time in LTI compatibility, plugin issues, and DB-schema workarounds. For pure course operation, Moodle / Canvas are fine.

How do you ensure AI evaluation quality?

AI scores first; the professor or instructor reviews interactively. smit-practicum's NABCD evaluation shows AI score + 'Path to Green' guide, and the professor confirms the verdict. We do not recommend AI-only scoring.

What should we consider for multilingual LMS?

Lecture content (MDX / HTML) and UI (JSON i18n) belong in different workflows. Instructors author content in ko / en pairs; UI i18n is extracted at build time. Allow asymmetry: some lectures live in ko only, some in ko + en, some in ko + en + fr — otherwise operations stall.

How is learner data protected?

Standard Personal Information Protection Act applies to learner identity, progress, and grades. Minor learners require parental consent on top. Clinical / medical education adds the Medical Service Act. Consent workflows are covered in detail on the clinical SaaS page.