ATLAS
Your courses, restructured by AI, validated by experts
ATLAS is an AI-powered course regeneration platform that takes existing educational content — recordings, slides, scripts, surgical videos — and transforms it into structured, adaptive learning experiences. The system extracts concepts and competencies, generates learning objectives mapped to Bloom's taxonomy, builds prerequisite relationships, and restructures everything using andragogy and instructional design principles. Expert validation gates ensure clinical accuracy. Learner profiling adapts content depth to each user's existing competency level.
The problem we solve
Medical and dental education companies sit on years of recorded courses, conference lectures, KOL presentations, and surgical demonstrations. This content represents enormous clinical value — but it's locked in formats that don't adapt to learners, can't verify comprehension, and don't scale across skill levels. A recorded theory day treats a 20-year veteran and a first-year resident identically. Meanwhile, high-value hands-on training — cadaver labs, wet labs, clinical workshops — is expensive and time-limited. Organizations can't afford to waste lab time on participants who arrive unprepared, yet they have no reliable mechanism to verify readiness. Rebuilding courses manually with instructional designers is slow, expensive, and starts from scratch every time.
Content Is the Curriculum
Most educational content already contains the building blocks of a well-designed course — learning objectives, prerequisite relationships, competency markers, assessment opportunities. They're just implicit, unstructured, and buried in hours of recordings. ATLAS makes the implicit explicit. The AI extraction pipeline identifies what's already there, structures it using established pedagogical frameworks, and surfaces it for expert validation. The system doesn't invent educational content — it reorganizes existing clinical knowledge into adaptive learning architectures that respect how adults actually learn. The expert validation gate ensures that no AI-generated objective, competency mapping, or assessment reaches learners without clinical sign-off.
Core Features
Content Ingestion & Analysis
ATLAS accepts recorded lectures, presentation slides, surgical videos, PDFs, and raw scripts. The system transcribes, segments, and analyzes source materials to identify discrete teachable concepts, clinical knowledge domains, procedural steps, and assessment-relevant content. Nothing is discarded — every source becomes part of the knowledge base.
Concept & Competency Extraction
The Paideia extraction engine identifies hierarchical concepts (domain, topic, concept), clinical competencies with performance indicators, and prerequisite relationships between knowledge elements. Each concept is classified by difficulty level, estimated learning time, and type — foundational, intermediate, advanced, or applied.
Learning Objective Generation
Extracted concepts are transformed into structured learning objectives using Bloom's taxonomy — from knowledge-level recall through comprehension, application, analysis, evaluation, and synthesis. Each objective is typed as knowledge, skill, or clinical judgment, with prerequisite dependencies and common errors flagged for assessment design.
Andragogy Gap Detection
The system identifies latent andragogical potential in content that wasn't originally designed with adult learning principles. Real-world examples, case-based reasoning, and experiential elements are flagged as opportunities for instructional redesign — even when the original author didn't explicitly structure them that way.
Expert Validation Gate
AI-generated learning objectives and competency mappings enter a validation queue where subject-matter experts — KOLs, course directors, faculty boards — review, approve, modify, or reject each element. This is the non-negotiable quality control layer. No content reaches learners without clinical sign-off.
Knowledge Base Integration
Before generating new course structures, ATLAS performs a two-stage search across existing validated content. First pass matches on learning objectives to find materials with aligned educational goals. Second pass runs semantic search within filtered results to identify the most contextually relevant content.
Adaptive Course Restructuring
Validated objectives and matched content are restructured into personalized learning pathways using instructional design principles and andragogy frameworks. The system generates course programs with logical progression, prerequisite-aware sequencing, and assessment checkpoints.
Learner Profiling & Adaptation
Learners enter the system through a competency profiling process that establishes their existing knowledge level across relevant domains. The course adapts in real time: foundational users receive full instructional pathways; advanced users skip mastered concepts and focus on gaps.
Competency Tracking & Readiness Verification
The system continuously tracks learner progress against validated competency frameworks. For pre-qualification scenarios — such as verifying readiness before a cadaver lab — ATLAS provides objective competency scores across multiple dimensions with configurable readiness thresholds.
Assessment Generation
Assessments are generated directly from validated learning objectives, ensuring alignment between what's taught and what's measured. The system produces formative assessments during learning, summative evaluations at milestones, and competency verification tests mapped to specific clinical skills.
Key Benefits
Unlock Existing Content Value
Years of recorded courses, conference lectures, and KOL presentations become structured, adaptive learning programs without starting from scratch.
Maximize Expensive Training Time
Cadaver labs, wet labs, and clinical workshops cost thousands per participant-day. ATLAS ensures participants arrive verified-ready, eliminating wasted lab time on underprepared attendees.
Scale Across Skill Levels
One content base serves multiple learner profiles. A first-year resident and a 15-year practitioner receive appropriately calibrated learning experiences from the same source material.
Maintain Clinical Authority
The expert validation gate means KOLs and faculty remain the quality authority. AI handles extraction and structuring; clinicians make the educational decisions.
Reduce Course Development Cycles
Traditional instructional design takes months. ATLAS produces validated course structures in days — with knowledge base search ensuring reuse of proven content across programs.
Closed-Loop Educational Quality
Learning objectives drive content, content drives assessment, assessment validates competency, competency data informs future course design. Every element is traceable back to validated objectives.
How it Works
Ingest
Client provides existing course materials — recordings, slides, surgical videos, scripts, PDFs. ATLAS transcribes and segments all content into analyzable units.
Extract
The Paideia engine identifies concepts, competencies, learning objectives, prerequisite relationships, and assessment opportunities. Output is a structured knowledge map of everything the source material contains.
Validate
Extracted objectives enter the expert validation interface. Subject-matter experts review each element with full source context. Approved objectives become the authoritative foundation for course generation.
Search & Match
ATLAS searches the existing knowledge base using two-stage retrieval — objective-level matching followed by semantic search. Validated content from previous courses is identified for reuse.
Restructure
Validated objectives, matched content, and prerequisite maps are assembled into adaptive course architectures using instructional design frameworks with logical progression and assessment checkpoints.
Profile & Adapt
Learners complete a competency profile. The system calibrates content depth, pacing, and assessment difficulty to their existing knowledge level. Adaptation continues throughout the learning experience.
Verify & Report
Competency tracking runs continuously. For pre-qualification scenarios, the system reports readiness status against configurable thresholds. All assessment data traces back to validated learning objectives.
Technical Specifications
Extraction Engine
Paideia — a multi-stage AI pipeline that identifies hierarchical concepts, competencies with performance indicators, Bloom's-taxonomy-aligned learning objectives, prerequisite relationships, terminology glossaries, and assessment-relevant content from raw educational materials.
Knowledge Architecture
Directed acyclic graph of concepts with prerequisite edges, competency nodes with performance indicators, and learning objective nodes mapped to source content chunks. Supports propedeuticity — enforcing prerequisite mastery before dependent content.
Retrieval System
Two-stage hybrid search: first pass matches on learning objective alignment, second pass performs semantic vector search within filtered results. Ensures content reuse across courses and consistency in shared concepts.
Validation Interface
Focused review environment for subject-matter experts. One objective at a time, source material context panel, approve/reject/edit actions, annotation tools, and progress tracking. Optimized for clinical professionals with limited review time.
Adaptive Engine
Learner competency profiles drive real-time content selection. The system evaluates existing knowledge against the course's concept graph and generates personalized pathways that skip mastered nodes and adjust assessment difficulty.
Assessment Framework
Assessments generated from validated learning objectives with direct traceability. Supports formative (in-course), summative (milestone), and competency verification (pre-qualification) modes. Common errors from source content inform question design.
Integration
Connects with ARIA for multilingual content processing — source materials in any supported language are transcribed and translated before extraction. NEMO integration enables AI avatar-based delivery of regenerated course content.
Dental Implant Cadaver Lab Pre-Qualification
A dental implant company runs quarterly cadaver lab workshops for advanced implantology training. Each lab costs €15,000 in facility fees, cadaver procurement, and instructor time — for 12 participants over two days. Historically, the first day is consumed by theory lectures that repeat content most participants have already encountered. Some participants arrive without adequate foundational knowledge, slowing the entire group during hands-on sessions.
The company provides ATLAS with their existing materials: 8 hours of recorded theory lectures, 200+ presentation slides, a surgical protocol PDF, and video recordings of 6 demonstration procedures. ATLAS transcribes and segments everything, then extracts 47 discrete concepts, 12 competencies, and 89 learning objectives across zygomatic implant planning, nerve proximity assessment, flap management, and immediate loading protocols.
The course director and two senior faculty members review the extracted learning objectives through the validation interface over three days, approving 81, modifying 6, and rejecting 2 that the AI had over-scoped. The approved objectives become the course foundation.
Two weeks before each cadaver lab, registered participants receive access to their personalized ATLAS course. An experienced practitioner with 200+ implant cases skips foundational anatomy modules and focuses on zygomatic-specific planning and complication management. A younger surgeon with 30 cases receives the full scaffolded pathway including prerequisite anatomy and basic surgical planning.
Competency tracking monitors progress across five dimensions: anatomical knowledge, surgical planning, technique recognition, complication awareness, and risk assessment. The readiness threshold requires 80% across all dimensions. Participants who don't meet the threshold receive targeted recommendations for the specific concepts they need to review.
Result: the theory day is eliminated. Lab time increases by 40%. Every participant meets minimum competency before touching a cadaver. Post-lab evaluations show 35% improvement in participant confidence scores and measurable reduction in basic procedural errors during supervised practice. The course materials — validated once — are reused across four quarterly labs with minor updates.