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Certified Artificial Intelligence Professional with Specialization in Generative AI

  1. NSQF Level: 4
  2. Duration: 440 Hours
  3. Format: 4 hours/day, 5 days/week
  4. Period: June 16 – December 27, 2025
  5. Target Group: Persons with Disabilities (PWDs) – Hearing Impaired or other categories (excluding VI)

CONTENT OF THIS DOCUMENT

A.Curriculum Structure for Core: 380 HOURS:

No. Module Name Hours Subtopics Tools Covered
1 Introduction to Artificial Intelligence 15
  • What is AI
  • Types of AI (Narrow, General, Super)
  • Real-life applications
  • Intro to visual programming concepts
  • Google Teachable Machine
2 Python for AI 40
  • Python syntax and variables
  • Data structures (lists, dicts)
  • Functions and loops
  • Pandas & NumPy basics
  • Practice with small datasets
  • Python
  • Jupyter Notebook
  • Google Colab
3 Introduction to Generative AI 20
  • Generative AI overview
  • Types (LLMs, Diffusion Models)
  • Use cases in text, code, image
  • Exploring tools via demos
  • ChatGPT
  • Claude
  • Gemini
  • Copilot
4 Prompt Engineering for LLMs 25
  • Prompt types and structure
  • Instruction tuning
  • Prompt chaining & templates
  • Evaluating AI outputs
  • ChatGPT
  • PromptPerfect
  • Gemini
5 Generative AI for Text Creation 25
  • Text summarization
  • Content rewriting
  • Style transformation
  • Blog/article creation
  • ChatGPT
  • GrammarlyGO
6 AI for Image Generation 30
  • Prompt-to-image basics
  • Visual refinement
  • Thematic creativity
  • Practical application use cases
  • DALL·E
  • Leonardo AI
7 AI for Video & Animation 30
  • AI in video design
  • Motion & lip-sync
  • Image-to-video workflows
  • Export & usage
  • Runway ML
  • Pika Labs
8 No-Code AI Model Building 25
  • Intro to classification & training
  • Model creation with visuals
  • Testing with custom inputs
  • Lobe AI
  • IBM Watson Studio
  • Microsoft Azure AI Studio
9 Conversational AI & Chatbots 25
  • Intent & entity concepts
  • Dialogue flow design
  • Bot testing & feedback loop
  • Rasa AI
  • Hugging Face Transformers
10 AI Tools for Accessibility 20
  • Live transcription
  • Caption integration
  • Communication support tools
  • Otter.ai
11 AI in Creative Storytelling 20
  • Plot generation
  • Character prompts
  • Genre experimentation
  • AI Dungeon
12 Data Visualization with AI 20
  • Data cleaning
  • Chart selection
  • Dashboard design
  • Google Looker Studio
  • Python Plotly
13 Real-Time Collaboration with AI 20
  • Shared notebooks
  • Cloud-based writing
  • Editing assistance
  • Google Colab
  • GrammarlyGO
14 AI Ethics & Fairness 10
  • Bias in datasets
  • Real-world ethical challenges
  • Fairness tools exploration
  • Aequitas
  • Fairlearn
  • Google PAIR
15 Use Case Implementation & Labs 55
  • Project selection
  • Tool integration
  • Iterative testing
  • Feedback loop
  • Final presentation
  • All tools from earlier modules

A1) LEARNING OUTCOME AND ASSESSMENT CRITERIA:

No. Module Name Learning Outcomes Assessment Criteria
1 Introduction to Artificial Intelligence Understand foundational AI concepts and create simple visual programs. Concept quiz, visual app using Scratch or App Inventor
2 Python for AI Write AI-ready Python programs and use essential libraries. Coding assignments, mini- projects
3 Introduction to Generative AI Explain how Gen AI works and demonstrate basic applications. Use-case report, reflection note
4 Prompt Engineering for LLMs Design, refine, and test prompts for varied LLM responses. Prompt design exercise, peer-reviewed tasks
5 Generative AI for Text Creation Generate written content using AI tools for various needs. Portfolio of AI-generated content
6 AI for Image Generation Create text-to-image visuals and apply them in storytelling/design. Visual asset project, creative review
7 AI for Video & Animation Generate simple AI videos with applied styles and effects. Video creation project
8 No-Code AI Model Building Build and test simple no-code AI models. No-code AI model presentation
9 Conversational AI & Chatbots Create basic chatbots and understand NLP flows. Chatbot design task
10 AI Tools for Accessibility Use AI tools to assist hearing- impaired communication. Demo + scenario simulation
11 AI in Creative Storytelling Build creative narratives using generative AI tools. Short story assignment
12 Data Visualization with AI Visualize and interpret datasets effectively. Data viz mini-project
13 Real-Time Collaboration with AI Collaborate on coding and writing tasks using AI. Group assignment
14 AI Ethics & Fairness Recognize ethical risks and explore fairness in AI. Ethics case analysis
15 Use Case Implementation & Labs Solve a real-world problem using combined AI tools and methods. Capstone project + viva

B.Core modules: Hourly Weight Summary

Module Type Modules Included Total Hours
Simple Tools & Theory Modules 1, 3, 10, 14 65 hrs
Medium Complexity Tools Modules 4, 5, 8, 9, 11, 12, 13 155 hrs
High Complexity Tools Modules 2, 6, 7, 15 160 hrs
TOTAL (CORE) 380 hrs

C.NSQF Framework Cross-Mapping (AI Associate – Level 4) NSQF Key Outcomes at Level 4

NSQF Criteria Requirements Covered in the Curriculum
Process Work in familiar, predictable,routine AI-related tasks under guidance Modules include structured tasks like chatbot design, data visualization, prompt creation
Professional Knowledge Basic AI/ML concepts, tools, applications, and ethical understanding Covered in Modules 1, 2, 3, 4, 5, 6,14
Professional Skills Practical skills in coding, tool usage, AI model building, LLMs, visualization Hands-on use of Python, Jupyter, Colab, LLMs, Hugging Face, Plotly
Responsibility Work independently on tasks and take responsibility for outputs Final capstone project (Module 15) ensures independent responsibility

Key NSQF QF Components Mapped to Modules
Qualification File Element Corresponding Module(s)
Basics of AI & Applications Module 1 – Introduction to AI
Python Programming Module 2 – Python for AI
Generative AI/LLMs Module 3, 4, 5
Data Handling, Visualization Modules 2, 12
No-Code Model Building Module 8
Conversational AI (NLP) Module 9
AI Ethics & Fairness Module 14
Capstone Use Case Module 15
Accessibility, Inclusivity Tools Module 10
Communication & Collaboration Module 13
Assessments via Projects & Presentations Embedded throughout modules

D.Generative AI Tools covered:

Tool Name Purpose License Type Software/Platform Needed Website / Access Link
ChatGPT Large language model for text generation Freemium / Paid Web browser / API https://chat.openai.com/
Claude AI assistant for text generation Paid Web browser https://www.anthropic.com/cla ude
Gemini Advanced LLM for text generation Paid Web browser https://gemini.google/
Copilot AI code assistant Paid subscription IDE plugin (VS Code, etc.) https://github.com/features/copilot
PromptPerfect Optimizes prompts for LLMs Freemium Web browser https://promptperfect.jina.ai
GrammarlyGO AI-enhanced writing improvement Freemium Browser extension / app https://www.grammarly.com/g rammarlygo
DALL·E Text-to-image AI generation Freemium / Paid Web browser / API https://openai.com/dall-e
Leonardo AI Text-to- image AIgeneration Freemium Web browser https://leonardo.ai/
Runway ML AI-powered video, animation, image editing Freemium / Paid Web browser https://runwayml.com/
Pika Labs AI video generation with lip-sync Freemium Web browser https://pikalabs.com/
AI Dungeon AI-driven interactive storytelling Freemium Web / mobile app https://play.aidungeon.io/
Copy.ai AI writing assistant for marketing and blogs Freemium Web browser https://www.copy.ai/

E.Other Core AI Tools covered

Tool Name Purpose License Type Software/Platform Needed Website / Access Link
Google Teachable Machine Visual no-code AI model training for beginners Free Web browser https://teachablemachine.withgoogle.com/
IBM Watson Studio Cloud-based AI model building and AutoAI Free Tier / Paid Web browser https://www.ibm.com/cloud/watson-studio
Microsoft Azure AI Studio No-code AI tools and cognitive services Free Tier / Paid Web browser https://azure.microsoft.com/en-us/services/machine-learning/
Python Programming language for AI and data science Free Python interpreter + IDE (VS Code,Jupyter) https://www.python.org/
Jupyter Notebook Interactive coding environment Free Jupyter / Anaconda/ Google Colab https://jupyter.org/
Google Colab Cloud Python coding with GPU support Free Google account + web browser https://colab.research.google.com/
Lobe AI No-code AI model building Free Windows/macOS app https://www.lobe.ai/
Rasa AI Open-source conversational AI platform Free Python environment https://rasa.com/
Hugging Face Transformers Access pre-trained NLP/ML models Free Python environment https://huggingface.co/transformers
Google Looker Studio Data visualization and dashboarding Free Web browser https://lookerstudio.google.com/
Python Plotly Python library for interactive charts Free Python environment https://plotly.com/python/

F.AI Tools for Accessibility

Tool Name Purpose License Type Software/Platform Needed Website / Access Link
Otter.ai Real-time transcription and notes Freemium Web app / Mobile apps https://otter.ai/

AI Ethics Tools

Tool Name Purpose License Type Software/Platform Needed Website / Access Link
Aequitas Bias and fairness auditing for AI models Free / Open Source Python library https://www.aequitas-project.eu
Fairlearn Fairness assessment and mitigation Free / Open Source Python library https://fairlearn.org/
Google PAIR Responsible AI practices and bias detection Free Web resources https://pair.withgoogle.com/

G.Curriculum Structure for Non-Core HOURS: 60 hours

Module No. Module Name Hours Learning Objectives Key Activities/Tools Covered
1 Student Bootcamp: Foundation & Psychometric Assessment 7 Introduce course expectations and mindset preparation- Conduct psychometric tests to understand student personality, learning style, and strengths Orientation sessions, psychometric tests
2 Mid-Program Assessments & Feedback 8 Conduct formative assessments to evaluate knowledge and skills gained- Provide personalized feedback Quizzes, MCQs, practical AI tool exercises
3 Advanced Soft Skills: Interview 15 Prepare students for technical and HR interviews- Develop confidence and articulation in interviews- Mock interview simulations Mock interviews, role plays, interview Preparation & Mock Interviews question banks, video recordings
4 Workplace Readiness: Employability Skills- ON-THE- JOB TRAINING 10 Build problem-solving frameworks and strategy discussion on adapting to workplace for DAPs. Group strategy discussions and critical thinking
5 Resume Building & Professional Branding 7 Teach resume writing tailored to AI roles- Building LinkedIn profiles and personal branding Resume templates, LinkedIn tutorials, personal branding workshops
6 Hackathon & Collaborative Challenges 8 Engage students in team- based AI challenges- Foster collaboration and innovation Team hackathons, problem-solving challenges
7 Final Capstone Project Preparation & Presentation Skills 5 Guide students in final project preparation- Teach presentation and demo skills Project planning sessions, presentation rehearsals

H.Assessment Plan

Assessment Type Methodology Weightage
Attendance Percentage Attendance must be above 70% throughout the program to be eligible for certification. Strictly monitored via AEBAS 10%
Midterm Evaluation Written + Practical Test covering:
  • –Python Programming
  • –Machine Learning Basics
  • –Prompt Engineering Techniques
30%
Project Presentation 3-Stage Project Evaluation:
  • Proposal Submission (Idea Review)
  • Prototype Development (Tool/Application Building)
  • Final Presentation (evaluated with structured rubric by panel)
30%
4.Final Assessment + Hackathon
    Includes:
  • –Final Written Exam (Individual)
  • –Team-Based Hackathon with real-world AI problem
  • –Scoring based on creativity, implementation, and teamwork
30%
  • Passing Criteria: Minimum 60% overall aggregate to qualify for certification