Unit 1 Fundamentals of AI Products & Emerging AI Systems

8 lecture hours

• 1.1 Introduction to AI products 1.2 Difference between AI tool and AI product 1.3 Components of AI systems: 1.3.1 Data 1.3.2 Model 1.3.3 Interface 1.3.4 Feedback loop 1.4 Overview of Generative AI 1.5 Introduction to Large Language Models (LLMs) 1.6 Types of AI models: Text
• Embeddings (conceptual overview only) 1.7 Generative AI vs Analytical AI 1.8 AI model selection for product use 1.9 Introduction to Multi-Agent AI (basic idea and applications) 1.10 Human-in-the-loop systems 1.11 Basic AI system architecture (conceptual diagram)

Unit 2 Design Thinking & Human-Centred AI

7 lecture hours

• 2.1 Introduction to Design Thinking 2.2 Empathy Mapping 2.3 User Persona Development 2.4 Problem Statement Writing 2.5 Customer Journey Mapping Teaching Scheme (in Hours) Total Credits L+T+ (PR/2) Assessment Pattern and Marks Total Marks L T PR C Theory Tutorial / Practical ESE(E) PA(M) PA(I) ESE(V) 3 0 2 4 70 30 20 30 150 Program Name: Diploma Engineering Level: Diploma Branch: Information Technology Subject Code: DI05016021 Subject Name: AI Product Design 2.6 UX principles for AI systems 2.7 Human-AI interaction design 2.8 Bias in AI systems (basic understanding) 2.9 Explainable AI (conceptual overview)

Unit 3 AI Product Strategy & OpenAI Integration

8 lecture hours

• 3.1 Product lifecycle management 3.2 Minimum Viable Product (MVP) 3.3 Feature prioritization 3.4 AI monetization models (SaaS
• subscription) 3.5 Business Model Canvas 3.6 Introduction to API concept 3.7 Understanding AI APIs (e.g.
• OpenAI API – concepts only) 3.8 Basic API key and security awareness 3.9 Cost awareness in AI products (token concept) 3.10 Basic AI evaluation methods: 3.10.1 Output quality checking 3.10.2 User feedback 3.10.3 Simple testing checklist

Unit 4 AI in Social Media & Digital Experience

10 lecture hours

• 4.1 AI in content creation 4.2 AI recommendation systems (concept) 4.3 AI chatbots for customer service 4.4 AI-driven ad targeting (concept) 4.5 Virtual influencers and AI avatars 4.6 Risks in Social Media AI: 4.6.1 Deep fake content 4.6.2 Fake news generation 4.6.3 Bot networks 4.6.4 Algorithm manipulation 4.7 AI Tools & Prototyping 4.7.1 Overview of no-code AI tools 4.7.2 AI-based presentation tools 4.7.3 Logo and content generation tools 4.7.4 Basic UI mock-up design 4.7.5 Demo video preparation

Unit 5 Privacy, Data Governance & Responsible AI

• 5.1 Data privacy concepts 06 13 Program Name: Diploma Engineering Level: Diploma Branch: Information Technology Subject Code: DI05016021 Subject Name: AI Product Design 5.2 Personal vs. sensitive data 5.3 Data lifecycle in AI systems 5.4 Data minimization principle 5.5 Data anonymization (basic concept) 5.6 Transparency and user consent 5.7 Responsible AI principles 5.8 Overview of Indian data protection law (basic idea) 5.9 Basic AI governance principles (organizational level overview)

Unit 6 AI Threats & Mitigation Strategies

6 lecture hours

• 6.1 AI Threats 6.1.1 Data breaches 6.1.2 Prompt injection attacks 6.1.3 Model misuse 6.1.4 Bias and discrimination 6.1.5 AI-generated phishing and scams 6.1.6 Deep-fakes and misinformation 6.2 Mitigation Strategies 6.2.1 Input validation 6.2.2 Output moderation 6.2.3 Secure API key handling 6.2.4 Access control basics 6.2.5 Basic logging and monitoring awareness 6.2.6 Human-in-the-loop review 6.2.7 AI risk assessment checklist