Conversational AI and Intelligent Agents
Why colleges run this
Chatbots and voice assistants are now standard business interfaces; students who can design intents and ship a working assistant meet a fast-rising demand.
Course outcomes
CO1
Describe the architecture of a conversational AI system
K2CO2
Design intents and entities for a stated business use case
K3CO3
Implement a multi-turn dialogue flow with context
K3CO4
Integrate a chatbot with a live data source through an API
K3CO5
Deploy and test a chatbot on a messaging channel
K3Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01Conversational AI foundations4 h
Theory
Use cases, architecture, natural-language-understanding concepts
Lab
Map a use case to an assistant architecture
Output
Architecture sketch
02Intent and entity design4 h
Theory
Designing intents, entities, training phrases
Lab
Design an intent-and-entity model
Output
Intent model
03Dialogue management4 h
Theory
Context, slots, multi-turn flows
Lab
Build a multi-turn dialogue
Output
Dialogue flow
04Integration4 h
Theory
Connecting to APIs and live data sources
Lab
Integrate the assistant with an API
Output
Integrated assistant
05Deployment and channels4 h
Theory
Deploying to web and messaging channels, testing
Lab
Deploy and test on a channel
Output
Deployed assistant
06Assistant capstone4 h
Theory
A live-data chatbot end to end
Lab
Ship a chatbot on live data
Output
Capstone chatbot
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment