Centre for Intelligent Robotics (CIR) · IIIT Allahabad × STEMLearn.AI
AI-TRAC 2026
AI Trainer Certification & Internship Program
for Robotics and Machine Intelligence Mentors
“Training Future AI & Robotics Mentors for India’s Schools”
1. Program Overview — AI-TRAC 2026
▼The Centre for Intelligent Robotics (CIR), IIIT-A, in strategic collaboration with STEMLearn.AI (Teevra EduTech Pvt. Ltd.), proposes AI-TRAC 2026 — a four-week residential Capacity Building and Mentorship Program in AI, Robotics, Machine Learning and Smart Systems.
AI-TRAC 2026 is designed to train the next generation of AI and Robotics mentors who can deliver structured, outcome-oriented AI and robotics education to students in Grades 9–12 across Indian schools.
The program addresses the gap between subject-matter expertise and effective teaching practice by combining rigorous technical training in AI and robotics with structured pedagogy sessions, guided micro-teaching exercises, and capstone project development.
2. Rationale & Strategic Context
▼India’s school education system faces a critical shortage of trained AI and robotics educators. While national policy frameworks, including NEP 2020 and NSQF, emphasize AI literacy for secondary school students, the availability of qualified facilitators remains limited — creating a significant gap between policy intent and on-ground implementation.
The CIR at IIIT Allahabad brings strong academic and research expertise in AI-enabled robotics, embedded systems, and intelligent automation. AI-TRAC 2026 integrates this with STEMLearn.AI’s curriculum design, LMS, and school outreach capabilities to create a high-quality, scalable mentor-training pipeline.
2.1 Key Drivers
- India needs 1 lakh+ trained AI facilitators for school-level delivery by 2027 (NASSCOM estimate).
- 80% of existing school teachers are untrained in AI/ML pedagogy (NAS 2021).
- UG/PG students and working professionals represent an underutilised talent pool for school-level AI education.
- CIR, IIIT Allahabad, provides world-class laboratory infrastructure and research oversight to validate all technical content.
2.2 Alignment with National Priorities
| National Policy / Initiative | Alignment with This Program |
|---|---|
| NEP 2020 | Competency-based, experiential AI learning aligned to NCERT principles |
| NSQF Level 3 & 4 | Intern curriculum mirrors NSQF modules for Grades 9–12 delivery |
| IndiaAI Mission | Builds grassroots AI literacy infrastructure via trained school mentors |
| CM’s AI-for-All (UP) | Creates AI-first workforce pipeline starting from Grades 9–12 |
3. Program Objectives
▼- Equip interns with comprehensive, hands-on knowledge of AI, Data Science, Machine Learning, and Smart Systems aligned with NSQF Levels 3 & 4.
- Develop structured pedagogy and teaching skills so interns can effectively deliver AI courses to Grades 9–12 students in school settings.
- Build practical proficiency in Python programming, data analytics, ML model implementation, and Generative AI tools.
- Train interns to design and execute capstone AI projects, and to guide school students through similar project-based learning.
- Create a certified pool of 50+ AI & Robotics mentors available for deployment through STEMLearn.AI’s network.
- Promote interdisciplinary collaboration between academia (IIITA) and industry (STEMLearn.AI) in building scalable AI education infrastructure.
4. Eligibility Criteria
▼| Participant Category | Minimum Qualification | Preferred Background |
|---|---|---|
| UG Students | 2nd year onwards — B.Tech / B.Sc (CS, IT, ECE, EE, or allied) | Programming or Math coursework |
| PG Students | M.Tech / M.Sc (CS, AI, Data Science, Electronics, or allied) | Prior ML / Python exposure |
| Teachers / Professionals | Graduate degree with active teaching or industry role | Interest in school-level AI education |
5. Curriculum & Module Structure
▼The program spans 80 instructional hours over four weeks, structured into eight modules combining theory, lab practicals, and guided teaching practice — directly mapped to NSQF Level 3 & 4 AI course content.
5.1 Hour Distribution
| Component | Hours |
|---|---|
| Theory | 32 Hours |
| Practical / Lab | 28 Hours |
| Project + Teaching Practice | 20 Hours |
| Total | 80 Hours |
5.2 Module-wise Curriculum
| Week | Module | Title | Total | Theory | Practical | Project |
|---|---|---|---|---|---|---|
| Week 1 | M1 | AI Foundations & Smart Systems | 8 | 5 | 3 | – |
| Week 1 | M2 | Problem Solving & System Design | 8 | 4 | 4 | – |
| Week 1 | M3 | Data Literacy, Statistics & Analytics | 12 | 6 | 6 | – |
| Week 2 | M4 | Python Programming for AI | 14 | 6 | 8 | – |
| Week 2–3 | M5 | Machine Learning: Fundamentals & Applications | 14 | 7 | 7 | – |
| Week 3 | M6 | Generative AI & Responsible AI | 8 | 4 | 4 | – |
| Week 3 | M7 | AI Applications & Smart Systems | 6 | 4 | 2 | – |
| Week 4 | M8 | Capstone Project + Mentor Training | 10 | – | – | 10 |
| TOTAL | 80 | 36 | 34 | 10 | ||
5.3 Module Details (click to expand)
- What is AI? AI vs Human Intelligence; Types of AI (conceptual)
- AI in real-world domains: healthcare, agriculture, robotics, automation
- Smart systems basics — Sense → Think → Act framework
- Practical: AI use-case mapping, case study discussions
- Problem decomposition; Input → Process → Output model
- Designing AI workflows; Introduction to flowcharts
- Practical: AI system architecture design exercises
- Types of data; data collection, cleaning, and preprocessing
- Data visualization; Mean, Median, Mode; Introduction to EDA
- Data bias & fairness; structured vs. unstructured data
- Practical: Dataset handling and visualization using Python
- Basics: variables, loops, conditions, functions
- Working with datasets (CSV); Introduction to NumPy & Pandas
- Practical: Program writing, data manipulation, and visualization
- Supervised vs. Unsupervised learning; Classification & Regression
- Model accuracy, confusion matrix; Decision Trees, KNN, Clustering
- Neural Networks (conceptual); Train-Test split; model comparison
- Practical: Simple ML model implementation and evaluation
- Generative AI and Large Language Models (LLMs)
- Prompt engineering; Ethics, bias, and privacy in AI
- Responsible AI practices; Safe deployment guidelines
- Practical: Prompt design, bias detection exercises
- Computer Vision basics; NLP basics; Chatbots
- AI in smart devices and intelligent automation
- Practical: Demo-based exploration of CV and NLP tools
- Capstone Themes: Smart City/Environment; Healthcare/Accessibility AI; Chatbot/Responsible AI Tool
- Pedagogy sessions: How to teach AI to school students (Grades 9–12)
- Lesson plan creation aligned to NSQF Level 3 & 4 modules
- Micro-teaching sessions with peer feedback
- Deliverables: AI capstone project + teaching module + demo session
6. Week-wise Schedule
▼7. Pedagogy & Delivery Methodology
▼The program employs blended, active-learning pedagogy mirroring the school delivery model — ensuring interns experience the exact instructional format they will later replicate as mentors.
Concept-first delivery followed by real-world application discussion for each topic. Each module opens with a contextual lecture grounding the technical content in practical relevance before hands-on lab work.
Python coding, data analysis, and ML model implementation in IIIT-A computer labs. Every theory module is paired with a structured lab session where interns build and run real code on real datasets.
Analysis of AI deployments in healthcare, agriculture, governance, and smart cities. Real-world cases connect theory to societal impact and equip interns with rich examples for their school teaching.
Capstone project developed across Week 4 with structured checkpoints and mentoring. Interns choose a theme, define the problem, implement a solution, and present to an evaluation committee.
Interns design and deliver 15-minute teaching demonstrations to peers, simulating school delivery. Each session is followed by structured peer feedback aligned to pedagogical rubrics.
Group activities, peer code reviews, and collaborative problem-solving exercises. Structured team tasks build the collaborative skills essential for classroom facilitation in school environments.
Guest lectures by IIIT-A faculty and STEMLearn.AI industry experts on advanced topics including AI in robotics, Generative AI frontiers, and school-level curriculum strategy and assessment design.
8. Roles & Responsibilities
▼8.1 IIIT Allahabad — CIR
- Overall academic oversight, quality assurance, and institutional hosting
- Provision of campus infrastructure: computer labs, lecture halls, hostel accommodation
- Faculty-led instruction for Modules 1, 5, 7 and all robotics/smart systems sessions
- Academic supervision of capstone projects by Prof. Sonali Agarwal and Dr. Surya Prakash
- Conduct of final assessments and issuance of joint certification
- Integration of interns into ongoing CIR research activities in AI-enabled robotics
8.2 STEMLearn.AI
- Curriculum design and provision of all learning materials — Techno-Books, LMS access, digital resources
- Delivery of Modules 2, 3, 4, 6, and pedagogy/mentor training sessions in Module 8
- LMS with video modules, coding notebooks, and project templates
- Design and conduct of structured assessments and evaluation rubrics
- Post-internship deployment support: connecting certified interns with Project Praveen schools
- Management of fee collection, logistics coordination, and participant communication
8.3 Interns
- Attend all 80 hours (minimum 85% attendance mandatory for certification)
- Complete all module-level assignments, lab exercises, and quizzes on the LMS
- Develop and present a capstone AI project by end of Week 4
- Design a lesson plan and deliver a micro-teaching session as part of Module 8
- Adhere to IIIT-A campus rules, hostel regulations, and program code of conduct
9. Assessment & Certification
▼9.1 Assessment Structure
| Component | Description | Weightage |
|---|---|---|
| Module Quizzes | Online quizzes at end of each module via LMS | 20% |
| Lab & Practical Exercises | Python coding tasks, dataset exercises, ML implementation | 30% |
| Capstone Project | AI project: problem definition, implementation, evaluation, presentation | 30% |
| Micro-Teaching Session | Lesson plan quality and teaching demonstration to peers | 20% |
| Total | 100% | |
9.2 Certification Criteria
- Minimum 85% attendance across all sessions
- Minimum 60% aggregate score across all assessment components
- Submission of capstone project and teaching module within program timeline
Interns meeting all criteria receive a Joint Certificate of Completion issued by IIIT Allahabad (CIR) and STEMLearn.AI, co-branded with institutional logos and signed by faculty leads.
10. Fee Structure
▼| Fee Component | Amount | Notes |
|---|---|---|
| Program Fee (Tuition) | ₹6,000 | Per intern |
| GST @ 18% | ₹1,080 | As applicable |
| Total Program Fee (incl. GST) | ₹7,080 | |
| Hostel Accommodation | As per IIIT-A rates | Charged separately |
| Meals / Food | As per IIIT-A rates | Charged separately |
✅ What the Program Fee Includes
- Full 80-hour structured instruction by IIIT-A faculty and STEMLearn.AI experts
- Lab access at IIIT-A campus for all practical sessions
- Joint Certification (IIIT Allahabad + STEMLearn.AI)
- Study materials, datasets, and project templates
- Full fee payment required at time of registration confirmation
- Fee is non-refundable after program commencement (June 01, 2026)
- Hostel and food charges paid directly to IIIT Allahabad as per institute norms
- Group Discount: 5+ from same institution → 10% concession on program fee
🏠 Bank Payment Details
Bank Name: Indian Overseas Bank, Civil Lines Allahabad (Uttar Pradesh) 211001
Account Name: IIIT-A General A/C
Account Number: 035001000060976
IFSC Code: IOBA0000350
Total payable: ₹7,080 (Non-refundable after program start)
11. Expected Outcomes & Impact
▼🎓 For Interns
- Hands-on proficiency in AI, Python, ML, and Generative AI tools
- NSQF-aligned pedagogical skills to teach AI to Grades 9–12 students
- Completed, presentable AI capstone project for portfolio/academic use
- Joint IIIT-A + STEMLearn.AI certificate, nationally recognised
- Priority pathway to paid mentor deployment through Project Praveen
- Access to CIR’s research ecosystem and STEMLearn.AI’s school network
🏠 For the School AI Ecosystem
- 50+ trained, certified AI mentors ready for school deployment in Year 1
- Direct impact on 2,500+ school students in Grades 9–12
- Scalable mentor pipeline: program repeated annually with increasing intake
- Strengthened Project Praveen delivery across 500+ UP government schools
🏫 For IIIT Allahabad — CIR
- Establishment of a nationally visible mentor-training program anchored at IIIT-A
- Integration of summer interns into CIR’s AI-enabled robotics research activities
- Enhanced industry partnership with STEMLearn.AI for content, placement, and outreach
- Contribution to NEP 2020 implementation and national AI literacy objectives
13. Faculty & Mentors
▼13.1 Academic Faculty — IIIT Allahabad
The program is further strengthened by IIIT-A faculty contributing domain expertise across AI, machine learning, and intelligent systems. CIR research staff facilitate lab sessions, hands-on robotics demonstrations, and technical project support.
13.2 Industry Faculty — STEMLearn.AI
| Profile | Organisation | Role in Program |
|---|---|---|
| AI Curriculum Experts | STEMLearn.AI | Delivery of Python, Data Science, ML, and GenAI modules |
| Pedagogy & Mentor Trainers | STEMLearn.AI | Teaching practice, lesson plan design, micro-teaching sessions |
| LMS & Assessment Team | STEMLearn.AI | LMS setup, quiz design, project evaluation, progress tracking |
14. Application & Registration
▼📝 AI-TRAC 2026 — Course Registration Form
Centre for Intelligent Robotics (CIR), IIIT Allahabad ✕ STEMLearn.AI
📋 How Registration Works
📄 What the Form Covers (27 Questions · 7 Sections)
👤 Personal Information
- Full Name, Gender, Date of Birth
- Mobile, WhatsApp & Email
- Aadhaar / Govt ID (optional)
- Passport Photo upload
- City & State
🏫 Academic / Professional Details
- Category (UG / PG / Teacher / Professional)
- Institution, Department, Year/Semester
- Highest qualification
- Python & AI/ML experience level
- Areas of interest (10 options)
💡 Motivation & Background
- Why AI-TRAC 2026? (paragraph)
- Prior projects or internships
- Interest in school mentoring
🏠 Accommodation & Logistics
- Hostel requirement & type
- Food / mess facility
- Payment status & UTR number
- Payment receipt upload
Opens in a new tab · All responses recorded automatically in Google Sheets
🔗 Direct link if button doesn’t open
Queries: cir@iiita.ac.in | stemlearn@speedlabs.in
15. Concluding Remarks
▼AI-TRAC 2026 represents a timely and impactful collaboration between two of India’s leading AI education stakeholders — the Centre for Intelligent Robotics at IIIT Allahabad and STEMLearn.AI. At a moment when India urgently needs trained AI educators for its school system, this program offers a structured, rigorous, and nationally scalable solution.
By training 50+ interns annually as certified AI and Robotics mentors — grounded in both academic depth and practical pedagogy — this program directly strengthens the knowledge delivery pipeline and contributes to India’s broader AI literacy mission.
We invite eligible students, educators, and professionals to be part of this transformative initiative and join the ranks of India’s next generation of AI educators.
Centre for Intelligent Robotics (CIR)
IIIT Allahabad
Coordinator, AI-TRAC 2026
Centre for Intelligent Robotics (CIR)
IIIT Allahabad
Co-Coordinator, AI-TRAC 2026
📍 Contact Us
Centre for Intelligent Robotics (CIR)
Computer Center-1, IIIT-Allahabad, Jhalwa Campus, Allahabad – 211 015, India
