Curriculum

The RAOP curriculum follows a simulation-to-hardware learning progression grounded in Guided Inquiry-Based Learning (GIBL). Educators begin with guided virtual labs supported by starter MATLAB/Simulink models, guided questions, and short checklists that help participants run the activity, make small changes, and interpret what they observe. The on-site phase reinforces key concepts through hardware-aligned validation in the AVRC Laboratory. Across all platforms, the curriculum is designed to produce classroom-ready outputs: lesson materials, student deliverables, and aligned assessments that educators can implement and adapt in K–12 settings.

Recommended starting point

Review key ideas in Concept Review.
Use the Resource Navigator to locate manuals, quick-start guides, and supporting materials.
Follow the Activities pathways selected for the cohort.

Structure

Two-week virtual phase (virtual labs + guided workflow)
One-week on-site phase (hardware-aligned validation in AVRC Lab)
Four groups per cohort, each focused on a platform strand

Instructional Model

Guided Inquiry-Based Learning (GIBL): educators investigate a guiding question using structured prompts and checkpoints.
Supported learning progression: clear steps, examples, and interpretation checks designed for educators who may be new to robotics or controls.
Evidence-centered work: record outputs, explain what the evidence shows, and connect results to classroom-ready activities.

Educator Outputs

Lesson plan aligned to a selected RAOP activity
Student-facing materials (prompts, tasks, deliverables)
Assessment plan (formative checks + summative rubric)
Implementation plan (resources, pacing, adaptations)

How GIBL Works in RAOP

Guided Inquiry-Based Learning (GIBL) means participants are not expected to “already know” robotics or control systems. Each activity begins with a clear goal and a guiding question. Educators use starter models, structured prompts, and short checklists to explore system behavior, test simple changes, and interpret results. The focus is on reasoning from evidence and translating the experience into a classroom-ready activity.

Start with a guiding question

Begin with a clear question and a short context review.

Use a supported workflow

Use starter files, checkpoints, and interpretation prompts to reduce guesswork.

Build classroom-ready outputs

Convert results into student prompts and an assessment aligned to learning goals.

Three-Week Learning Progression

The plan below summarizes the intended progression across virtual and on-site phases.

Week	Focus	Modality	Expected Deliverables
Week 1	Onboarding + Core Concepts + First Guided Labs	Virtual (Digital Twin)	Complete setup checks and access verification for required tools and materials Finish guided introductory modules selected by the RAOP team Begin a draft classroom translation plan (topic, grade band, constraints, learning goals)
Week 2	Deeper Labs + Interpretation + Classroom Translation	Virtual (Digital Twin)	Complete additional selected modules (modeling/control/robotics workflows as applicable) Document results and interpretations using provided lab procedures and prompts Draft lesson plan components (objectives, student tasks, materials, pacing) Draft assessment approach (formative checks + a summative rubric outline)
Week 3	Hardware Validation + Implementation Readiness	On-site (AVRC Lab Hardware)	Validate selected workflows on physical hardware with step-by-step support Refine lesson plan and assessment materials based on validation outcomes Finalize an implementation package (lesson plan + student materials + rubric + logistics)

Consistency across platforms

Each activity uses a common GIBL workflow and evidence collection prompts.
Starter MATLAB/Simulink models support simulation, identification, and tuning where applicable.
On-site sessions emphasize validation, interpretation, and practical constraints.

Curriculum Strands

Controls Foundations (Qube-Servo 3)

Core control concepts grounded in measurement, modeling, stability, and feedback design. Educators build intuition in virtual labs and then validate key results during the on-site week.

Representative learning targets

Instrumentation and data collection (hardware interfacing, filtering)
System modeling (step response, parameter estimation, block-diagram modeling, and/or frequency response)
Stability analysis (experimental and analytical)
Controller design and tuning (P/PD concepts, performance criteria, qualitative tuning, and basic control design)

View related activities

Dynamics and Attitude Control (Aero 2)

Rotational dynamics, parameter estimation, filtering, and control design delivered via a simulation-to-hardware workflow using guided MATLAB/Simulink models and structured interpretation prompts.

Representative learning targets

Instrumentation and data collection (hardware interfacing)
Measurement and filtering
Block-diagram modeling, parameter estimation, and model validation
Controller design and tuning (PID concepts, performance criteria, qualitative tuning, and design)

View related activities

Manipulation and Sensing (QArm)

Manipulation fundamentals and sensing concepts through structured tasks that translate naturally into classroom demonstrations and student activities. Work begins virtually and is reinforced with on-site validation.

Representative learning targets

Sensing and feedback concepts for manipulation tasks
Kinematics concepts and motion planning intuition (workspace identification, lead through, teach pendant)
Pick-and-place workflows and task decomposition (trajectory generation)
Visual manipulation activities (image acquisition, object detection, introduction to visual servoing)

View related activities

Ground Autonomy and Navigation (QBot Platform)

Mobile robotics foundations using guided activities that emphasize sensing, observation, and navigation-oriented applications. Virtual work supports classroom transfer and on-site validation.

Representative learning targets