1. ✅ STEM-Aligned Learning – Curriculum designed on global STEM.org guidelines.
2. ✅ Hands-On Projects – Build chatbots, AI-powered robots, and real-world applications.
3. ✅ Future-Ready Skills – Coding, robotics, critical thinking, and problem-solving.
4. ✅ Expert Mentors – Learn from industry professionals with real AI experience.
5. ✅ Step-by-Step Growth – Beginner to advanced pathways for all learners.
6. ✅ Ethical AI Practices – Learn to use AI responsibly and for positive impact.
7. ✅ Globally Recognized Standards – Designed to support STEM.org Accreditation readiness.

Our AI course isn’t just about technology—it’s about preparing you to innovate, lead, and thrive in tomorrow’s world.

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What is STEM Education?

STEM stands for Science, Technology, Engineering, and Mathematics. STEM education emphasizes inquiry-based, hands-on learning that encourages innovation, critical thinking, and creativity. TrainologyX integrates STEM principles with Artificial Intelligence and Robotics to prepare learners for real-world challenges.

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Course Objectives

• Develop problem-solving and computational thinking.
• Enhance creativity through real-world AI & Robotics projects.
• Provide future career readiness in technology fields.
• Promote innovation, collaboration, and teamwork.

AI & Robotics Training Program

Our AI & Robotics Training is designed to make cutting-edge technology accessible to all learners, regardless of age, background, or prior experience. Whether you’re a school student, college learner, or working professional, we’ve tailored the curriculum to match your level.

School Students (Grades 6–12):

Objective: Spark curiosity and build a strong foundation in AI & Robotics.

1. Introduction to Robotics concepts (sensors, motors, simple automation)
2. AI basics explained in a fun & easy way
3. Hands-on projects: building small robots & AI-powered games
4. Focus on problem-solving, creativity, and teamwork
5. Ideal for STEM competitions & future readiness

College Students

Objective: Bridge academic knowledge with industry-relevant AI & Robotics skills.

1. Python programming for AI & Robotics
2. Machine Learning basics & applications
3. Robotics integration – hardware + AI models
4. Practical workshops: object detection, voice assistants, autonomous bots
5. Internship & project opportunities to boost career prospects

Professionals & Enthusiasts

Objective: Master advanced AI & Robotics for career growth or innovation.

1. Deep Learning & Neural Networks
2. Computer Vision, NLP (Natural Language Processing)
3. Autonomous Robotics – navigation, path planning, AI-driven automation
4. Industry use-cases: healthcare, manufacturing, smart systems
5. Guidance on research, product development & entrepreneurship

School Students (Grades 6–12)

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Weekly Roadmap – Beginner Track

Weeks 1–2: Intro to Robotics Hardware & Basic Coding

Topics

1. What is robotics? (History, applications, and scope)
2. Introduction to microcontrollers (Arduino / Raspberry Pi / ESP32)
3. Basic electronics (resistors, LEDs, motors, power supply)
4. Programming fundamentals (Python or Arduino C)
5. Controlling LEDs, motors, buzzers with simple code

Activities / Mini-Projects

1. Blink an LED (hello world of hardware)
2. Control a motor using a button
3. Build a traffic light system with LEDs

Learning Outcome

1. Comfort with microcontroller setup & IDEs
2. Ability to write, upload, and debug basic hardware code

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Weeks 3–4: Sensors, Actuators, and Simple Projects

Topics

1. Types of sensors (IR, ultrasonic, temperature, light, motion)
2. Actuators (servo motors, DC motors, stepper motors)
3. Working with sensor input and actuator output
4. Basics of robotics control systems

Activities / Mini-Projects

1. Distance measurement with ultrasonic sensor
2. Line-following robot (using IR sensors)
3. Obstacle-avoiding robot

Learning Outcome

1. Ability to interface multiple sensors and actuators
2. Build simple autonomous robots

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Weeks 5–6: AI Fundamentals & Simple ML Models

Topics

1. Introduction to AI vs ML vs Deep Learning
2. Supervised vs unsupervised learning
3. Datasets and preprocessing basics
4. Simple ML models (linear regression, classification)
5. Intro to computer vision (OpenCV basics)

Activities / Mini-Projects

1. Train a model to recognize shapes/colors
2. Use camera + OpenCV to detect objects or faces
3. Voice command control using simple ML models (speech recognition APIs)

Learning Outcome

1. Understand ML workflow (data → model → prediction)
2. Apply AI techniques for perception tasks

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Weeks 7–8: Integration of AI with Robotics & Project Showcase

Topics

1. Combining robotics hardware with AI software
2. Real-time computer vision with a robot (camera integration)
3. Speech-to-text + robot response
4. Basics of reinforcement learning in robotics

Activities / Mini-Projects

1. AI-powered obstacle-avoiding robot (vision-based)
2. Voice-controlled robotic arm
3. Smart home robot (detects human presence, responds to commands)
4. AI line-follower that adapts speed based on path complexity

Learning Outcome

1. Build a working AI-enabled robotic system
2. Present a functional project that combines hardware + AI

1. Line-Following Robot using Sensors

1. Hardware: Arduino/Raspberry Pi, IR sensors, motor driver, DC motors, chassis, battery.
2. Concepts: Sensor calibration, PID control for smooth movement.
3. Skills: Embedded C / Python programming, electronics basics.

2. AI-Powered Waste Sorting Bot

1. Hardware: Conveyor belt mechanism, Raspberry Pi/Jetson Nano, camera module, servo motors.
2. Software: TensorFlow/PyTorch for object detection (plastic, paper, metal, etc.).
3. Concepts: Computer vision, CNNs, real-time classification.
4. Skills: Python, OpenCV, ML model training & deployment.

3. Voice-Controlled Robotic Arm

1. Hardware: Robotic arm kit (servo-based), Raspberry Pi/Arduino, microphone.
2. Software: Speech recognition (Google Speech-to-Text, Vosk, or CMU Sphinx).
3. Concepts: Natural language commands → mapped to motor positions.
4. Skills: Python/Arduino programming, robotics kinematics, speech processing.

4. Chatbot with Natural Language Processing

1. Tools: Python, NLTK/Spacy, Hugging Face Transformers, Rasa/Dialogflow.
2. Concepts: Intent detection, entity recognition, context handling.
3. Skills: NLP, conversational AI design, API integration.

5. Smart AI Assistant with Speech Recognition

1. Hardware (optional): Raspberry Pi with mic & speaker (for standalone use).
2. Software: Speech-to-Text (Whisper, Vosk, Google API), Text-to-Speech (gTTS, pyttsx3).
3. Concepts: Multimodal AI (voice input + text output), task automation (calendar, reminders, IoT control).
4. Skills: Python, API integration, event-driven programming.

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Tools & Platforms

• Robotics Kits: LEGO, Arduino, Raspberry Pi.
• Programming: Scratch, Python, ROS.
• AI Platforms: TensorFlow, Teachable Machine, OpenCV.
• Cloud AI: Google Colab, Azure Cognitive Services

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Certification & Accreditation

Participants will be awarded a Certificate of Completion from TrainologyX.

The program is designed in alignment with STEM.org Accreditation standards, ensuring that learners gain skills validated against globally recognized benchmarks.

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Career Pathways After Course

• Robotics Engineer – Designs and develops intelligent machines that automate tasks and interact with the physical world.
• AI Specialist – Builds and deploys smart algorithms to enable systems to learn, adapt, and make decisions.
• Data Scientist – Extracts insights from data using advanced analytics, machine learning, and statistical modeling.
• IoT Developer – Creates connected solutions that integrate devices, sensors, and cloud platforms for smarter systems.
• Research & Innovation Roles – Drives breakthroughs by exploring emerging technologies and developing future-ready solutions.