Deploy sandboxed AI coding environments, NEP-mapped curriculum, and CBSE/state-board certified teacher training — fully aligned with the National Education Policy 2020 mandate for computational thinking from Grade 6 onward.
No credit card required · Data hosted in India
75+
Active Sandboxes
5 Lac+
Scripts Executed
12k+
Certified Teachers
A unified infrastructure layer connecting sandboxes, curriculum, and certification into a single compliance-ready deployment.
Isolated, low-bandwidth AI/robotics environments deployable in rural and urban classrooms alike.
Grade-wise modules mapped directly to NCF-SE 2023 computational thinking outcomes.
Standardized training tracks aligned to NCTE and state SCERT frameworks.
Auto-generated audit trails for trust boards and state education departments.
One coherent learner pathway. Six connected domains. Skills that deepen at every stage.
Foundation
Where the journey begins — explore patterns and cause-effect through hands-on discovery.
K–Grade 2Preparatory
Build algorithms & prototypes
Grades 3–5Middle
Model, code & automate
Grades 6–8Secondary
Optimise, deploy & innovate — where learners graduate into intelligent systems builders.
Every AI2N-certified facilitator completes a structured pathway covering pedagogy, sandbox operations, and student safety — verified against NCTE competency benchmarks.
Certification Levels
To scale RobotSpace.ai Robotics Academy (R2A) into a premium institutional product, the RobotSpace.ai Academic Architecture Review Board (RAARB) enforces enterprise-grade standardisation across every academic module published to the AI2N.ai ecosystem. No module is released for institutional deployment until all seven core assets pass automated schema validation, editorial review and platform-readiness checks for AI2N.ai and autobotlabs.ai integration.
How the seven core assets group into four delivery engines that carry a learner from theory to a certified capstone.
Engine 01
Delivers conceptual foundations, vocabulary and structured learner practice.
Engine 02
Powers automated and manual validation, quiz delivery and performance analytics.
Engine 03
Enables faculty delivery, classroom choreography and troubleshooting.
Engine 04
Defines summative mission parameters, code-review criteria and credential triggers.
The required platform specification every certified asset must meet before release.
Asset 1
Foundational computing concepts, architectural diagrams and core reading material.
Map chapters to CBSE CTAI milestones and NEP 2020 outcomes; include vector robot schematics, physics definitions and computational vocabulary lists.
Asset 2
Structured interactive learning pages for learner execution.
Include logic tracing grids, pseudo-code blocks, coordinate charts, sensor-threshold calculations and comprehension builders; optimised for AI2N.ai digital input and offline print use.
Asset 3
System-integrated automated and manual validation keys.
Provide completed workbook pages with answer overlays and optimal visual-block and Python layouts, linked to the AI2N.ai Faculty Console for rapid troubleshooting.
Asset 4
Turnkey classroom directions, pacing and lesson choreography.
Include minute-by-minute pacing charts, conceptual analogies, vocabulary prompts and a classroom troubleshooting matrix for virtual robot behaviour and corrective code actions.
Asset 5
Practical application rules, virtual bench configurations, infrastructure safety and operational boundaries.
Bridge learner code with autobotlabs.ai digital-twin assets and define safe execution limits for classroom computing setups.
Asset 6
Data-driven milestone check-gates at the close of each instructional segment.
Embed five-question quizzes, syntax validation challenges and logic gates in the LMS and feed the analytics engine to identify learners below target class performance.
Asset 7
Summation project parameters and formal qualification metrics.
Include mission sheet, presentation defence structure, code-review criteria and automated evaluation protocol; successful compilation unlocks a verifiable RobotSpace.ai learner credential through the central ledger.
Because the RobotSpace.ai/R2A model prioritises engineering realism and structural execution, every Asset 5 document includes an immutable component manifest with four mandatory technical sections.
Define the exact autobotlabs.ai map environment, robot profile manifest and sensor map allocation for the project.
Provide Smart-TV or projector broadcasting instructions and bandwidth-saver settings for entry-level school devices.
Require every continuous loop to include a minimum wait(0.05) statement and define project-failure conditions such as sustained wall scraping or leaving the active canvas mesh.
Include a fixed AI2N.ai 100-mark evaluation matrix for functional performance, code structure, sensor logic and processing efficiency.
The fixed AI2N.ai 100-mark capstone grading matrix referenced in Asset 5 and Asset 7.
Full mission-task validation with no collisions.
Minimal line count, appropriate loop deployment and readable logic.
Closed-loop reactive handling instead of timer-only steps.
Optimised path compilation and fast processing.
Onboard your institution to a fully NEP 2020 compliant AI & robotics network. Our team responds within 2 business days.