Hyper-Personalized
AI Learning — Proviz
An end-to-end intelligent education platform: dynamic course generation, AI video lectures, adaptive learning paths, a personal AI tutor, job-aligned skill mapping, and a living knowledge base — all tailored to each learner in real time.
01 · Background
Why education needed to be reinvented
Most online learning platforms offer the same course to every learner, regardless of what they already know, how they like to learn, what job they are targeting, or how much time they have. The result is dropout rates above 90% on MOOCs and millions of learners stuck in content that is too slow, too fast, or simply irrelevant to their goals.
Provizraq Labs was founded on April 3, 2025, with one mission: make education truly personal, accessible, and AI-powered for every learner on earth. Proviz is the flagship product of that mission — a platform where no two learning experiences are ever the same.
"I already know Python. I don't need the 10-hour beginner course. I need the specific 40 minutes that gets me from where I am to where I want to be." — Early user interview
The breakthrough insight was that the content itself should be generated, not retrieved. Instead of searching a catalogue, learners describe their goal — and Proviz builds the exact course, lecture series, notes, and quiz set they need, on demand.
02 · Core Capabilities
Seven pillars of the Proviz learning stack
Every capability in Proviz was designed to work together as a closed loop: content is generated, delivered, assessed, adapted, and continuously improved based on how each learner actually engages with it.
03 · Dynamic Course Generator
A full course from a single sentence
The course generator is the most-used capability in Proviz. A learner types what they want to learn and what they already know — and within 90 seconds, they have a fully structured, paced, and personalized curriculum.
Under the hood, a multi-step LLM pipeline extracts the learning goal, queries the knowledge graph for prerequisite concepts, runs a skill-gap analysis against the learner profile, designs module structures, and generates lesson-level content with assessments — all in a single coherent workflow.
Generator pipeline stages
- Goal extraction and intent parsing
- Prerequisite graph traversal from knowledge base
- Skill gap diff against learner profile
- Module structure design and sequencing
- Lesson content + assessment generation
- Difficulty calibration and pacing
What makes it personal
- Skips concepts the learner already knows
- Adjusts depth based on prior test scores
- Selects examples from learner's domain
- Adapts reading level and explanation style
- Reorders prerequisites intelligently
- Sets time estimates to learner's pace history
04 · AI Video Lectures
Narrated, illustrated, and level-matched lectures
Proviz generates video lectures for every lesson — not just text. A multimodal pipeline writes a narration script, creates synchronized visual diagrams and slides, and produces an audio-visual lecture with chapter markers, transcript, and concept highlights.
Critically, the same concept is explained differently for a beginner vs. an intermediate learner. The system detects the learner's level from their profile and prior assessment performance and adjusts the vocabulary, analogies, and depth of explanation accordingly.
05 · Personal AI Tutor
A tutor that knows your entire learning history
The AI tutor is a persistent conversational agent scoped to each learner. Unlike a generic chatbot, it has full memory of every lesson completed, every quiz score, every concept the learner struggled with, and every question they have asked before.
The tutor uses a Socratic method by default — asking guiding questions rather than giving direct answers — to build genuine understanding. It can switch to direct explanation mode when a learner is stuck, provide worked examples on request, and generate new practice problems calibrated to the learner's current mastery level.
Tutor capabilities
- Persistent memory across all sessions
- Socratic questioning by default
- Struggle detection from quiz patterns
- On-demand worked examples and practice
- Concept linking to prior lessons
- Tone adaptation: encouraging, direct, concise
Under the hood
- Long-term memory via vector + structured store
- Course context injected at each session start
- Mastery state read from assessment events
- RAG retrieval for factual grounding
- Confidence scoring on generated explanations
- Feedback loop: learner ratings improve tutor
06 · Adaptive Learning Paths
Every learner takes a different route
Static courses give everyone the same sequence. Proviz continuously re-sequences content based on real signals: quiz scores, time spent, replay rate on video segments, tutor conversation patterns, and explicit self-assessment from the learner.
If a learner aces a concept, the system compresses or skips it and accelerates to harder material. If they struggle, it adds scaffolding, inserts bridging lessons, and slows the pace. The path updates after every interaction — not just at the end of a module.
The adaptation engine is powered by a Bayesian knowledge tracing model per concept, updated continuously with new evidence. It tracks not just "did they pass" but how confident and how durable their understanding is, and schedules spaced repetition for concepts at risk of being forgotten.
07 · Job-Aligned Learning
Every lesson tied to a career outcome
Proviz goes beyond skill trees. Each learner sets a target role or career direction, and the platform maps every lesson, module, and assessment back to real job-market requirements scraped and indexed from live job postings, company engineering blogs, and industry skill frameworks.
A skill-gap analysis runs at enrolment and re-runs as the learner progresses. It surfaces exactly which skills are blocking a promotion or a career switch, and auto-adjusts the course curriculum to close those gaps first.
Green pills = skills covered in current path. Red pills = gaps Proviz auto-schedules next.
08 · Platform Architecture
How all the pieces fit together
Proviz is built as a modular AI stack where every capability is a first-class service with its own evals, latency budget, and fallback path. The learner profile is the central data primitive — every service reads from and writes back to it.
09 · Outcomes & Evals
Measuring what actually moves learners forward
Every AI capability in Proviz is evaluated continuously against learner outcomes — not just model benchmarks. The eval suite tracks content quality, tutor faithfulness, path efficiency, and real-world learning retention.
10 · My Role — Founder & AI Engineer
What I designed, built, and owned
- 01 Founded Provizraq Labs Pvt. Ltd. on April 3, 2025 and defined the product vision: hyper-personalized, AI-generated education accessible to every learner globally.
- 02 Designed and built the dynamic course generator pipeline — multi-step LLM workflow for goal extraction, prerequisite mapping, skill-gap analysis, module design, and content generation with sub-90s latency.
- 03 Architected the AI video lecture engine: narration script generation, visual diagram synthesis, slide sync pipeline, and adaptive explanation calibration by learner level.
- 04 Built the personal AI tutor agent with persistent long-term memory, Socratic question-style prompting, struggle detection from assessment events, and RAG-grounded factual responses.
- 05 Designed the adaptive learning path engine using Bayesian Knowledge Tracing per concept, real-time re-sequencing, spaced repetition scheduling, and difficulty calibration from engagement signals.
- 06 Built the job alignment system: live job-posting indexer, skill gap analyzer, career ladder mapper, and curriculum auto-adjustment to prioritize blocking skills.
- 07 Implemented the hybrid RAG pipeline (BM25 + dense vectors + cross-encoder rerank) for the knowledge base, powering the tutor and course generator with verified, cited source grounding.
- 08 Designed the AI notes and slides generator: structured note formatting, visual slide deck production, and exportable cheatsheet generation for every lesson.
- 09 Conducted learner and stakeholder research, shipped eval harnesses for content quality and tutor faithfulness, and instrumented learning analytics dashboards for continuous improvement.