Programme Parameters
Graduate / Intermediate
3 Months (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This certification empowers teachers to introduce foundational AI and robotics concepts to students in grades 9-10, focusing on engaging, hands-on learning experiences that inspire interest in STEM. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This certification empowers teachers to introduce foundational AI and robotics concepts to students in grades 9-10, focusing on engaging, hands-on learning experiences that inspire interest in STEM.
📋 Course Objectives
1. Develop hands-on skill in hands-on learning experiences.
2. Translate AI in Industry & Manufacturing theory into practical, reproducible analysis.
3. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI in Industry & Manufacturing
• R&D engineers and working professionals applying AI in Industry & Manufacturing in industry
• Academics and educators building research or teaching capacity in AI in Industry & Manufacturing
• Data and computational scientists moving into hands-on learning experiences
🚀 Key Learning Outcomes
• Confidence to reason about hands-on learning experiences in real projects.
• A demonstrable AI in Industry & Manufacturing project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Teaching Computing to Early Secondary Learners
• Age-appropriate concept sequencing and common misconceptions at this stage
• Concrete-to-abstract progression using physical robotics before code
• Managing mixed prior experience within one classroom
Core AI Ideas Without Heavy Mathematics
• Pattern recognition, training data and prediction explained through activities
• Unplugged activities that teach algorithmic thinking without a computer
• Bias and fairness introduced through examples students recognise
Hands-On Build and Program
• Microcontroller and sensor basics on affordable, repairable kits
• Block-based programming and structured transition toward text
• Debugging as a taught skill rather than an obstacle
Classroom Project Design
• Scaffolded project briefs with achievable scope in school timetables
• Group work structures that prevent one student doing everything
• Low-cost and offline-capable project options for constrained schools
Evaluation and Curriculum Fit
• Rubrics for process and reasoning, not just a working artefact
• Mapping to national curriculum outcomes at this stage
• Safeguarding and acceptable-use guidance for student AI tool use
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | ROS |
| Covered Tool / Platform | MATLAB |
| Covered Tool / Platform | Simulink |
| Covered Tool / Platform | Arduino |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Gazebo |
| Covered Tool / Platform | SolidWorks |
Frequently Asked Questions
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