Dr. Aravinda Thejas Chandra

Generative AI Tools: A Practical Guide for Information Science Engineering Students

Introduction

Artificial Intelligence (AI) has moved far beyond rule-based systems and simple automation. One of the most transformative developments in recent years is Generative Artificial Intelligence (Generative AI). Unlike traditional AI systems that only analyze or classify data, Generative AI can create new content—such as text, images, code, audio, and videos—by learning patterns from large datasets.

For Information Science and Engineering (ISE) students, especially in the 6th semester, Generative AI is not just a trending topic but a career-defining technology. It directly connects with subjects like Machine Learning, Data Science, Natural Language Processing, Software Engineering, Cloud Computing, and Full Stack Development.

This blog introduces the concept of Generative AI, explains how it works, and provides a detailed overview of popular Generative AI tools with practical examples relevant to engineering students.

 

What is Generative AI?

Generative AI refers to a class of AI models that can generate new data similar to the data they were trained on.

Definition

Generative AI is a type of artificial intelligence that learns patterns from existing data and generates new content such as text, images, code, audio, or video.

Key Characteristics

• • Produces original content, not copied data
  • Learns from massive datasets
  • Uses deep learning models, especially neural networks
  • Improves over time with better data and feedback

Simple Example

• • A traditional AI system: Classifies emails as spam or not spam
  • A Generative AI system: Writes a complete professional email

 

How Does Generative AI Work?

At the core of Generative AI are Deep Learning models, especially:

• • Artificial Neural Networks (ANN)
  • Transformers
  • Large Language Models (LLMs)
  • Diffusion Models

High-Level Workflow

1. 1. Training Phase
      • Model is trained on massive datasets (text, images, code, etc.)
      • Learns grammar, structure, patterns, and relationships
   2. Learning Representations
      • Converts data into numerical vectors (embeddings)
      • Understands context and semantics
   3. Generation Phase
      • Given a prompt, the model predicts the next token (word, pixel, or sound)
      • Repeats the process to generate full content

Example

Prompt:

"Explain TCP/IP in simple terms"

Model Output:

• • Generates a well-structured explanation using learned patterns

 

Why Should ISE Students Learn Generative AI?

Academic Relevance

• • Helps in seminars, assignments, lab records, and mini-projects
  • Supports understanding of complex topics through explanations and examples

Industry Relevance

• • Used in software development, testing, cybersecurity, data analytics
  • Companies expect engineers to use AI tools productively

Career Advantage

• • Enhances productivity
  • Improves coding, documentation, and debugging
  • Opens roles such as:
    • AI Engineer
    • Prompt Engineer
    • ML Engineer
    • Data Scientist
    • Full Stack Developer

 

Categories of Generative AI Tools

Generative AI tools can be broadly classified into:

1. 1. Text Generation Tools
   2. Code Generation Tools
   3. Image Generation Tools
   4. Video Generation Tools
   5. Audio & Speech Tools
   6. Multimodal AI Tools

Let us explore each category with popular tools and engineering-focused examples.

 

1. Text Generation Tools

ChatGPT (OpenAI)

ChatGPT is one of the most widely used Generative AI tools for text-based tasks.

Capabilities

• • Answer technical questions
  • Generate notes and summaries
  • Explain algorithms and protocols
  • Create project documentation
  • Assist in exam preparation

Example for ISE Students

Prompt:

"Explain RSA algorithm with steps and example for 6th semester students"

Use Cases

• • Writing seminar reports
  • Understanding networking and security concepts
  • Preparing viva questions
  • Debugging logic errors conceptually

 

Google Gemini

Gemini is Google’s AI model integrated with search and productivity tools.

Strengths

• • Strong factual grounding
  • Good at explanations with examples
  • Integrates well with Google Docs and Slides

Example

Prompt:

"Compare supervised and unsupervised learning with real-world examples"

 

2. Code Generation Tools

GitHub Copilot

GitHub Copilot is an AI-powered coding assistant trained on large amounts of code.

Supported Languages

• • Python
  • Java
  • C, C++
  • JavaScript
  • SQL
  • HTML, CSS

Example

While writing a Python program for machine learning lab:

# Function to calculate accuracy

Copilot auto-suggests:

def accuracy(y_true, y_pred):

    correct = sum(1 for a, b in zip(y_true, y_pred) if a == b)

    return correct / len(y_true)

Use Cases

• • Faster coding
  • Learning syntax
  • Completing boilerplate code
  • Understanding APIs

 

ChatGPT for Coding

ChatGPT can:

• • Generate full programs
  • Explain code line by line
  • Convert code between languages

Example

Prompt:

"Write a Python program to implement Dijkstra’s algorithm"

3. Image Generation Tools

DALL·E

DALL·E generates images from textual descriptions.

Example Prompts

• • "Neural network architecture diagram"
  • "Cybersecurity attack flow illustration"

Use in Engineering

• • Creating diagrams for presentations
  • Visualizing abstract concepts
  • Designing posters for tech events

 

Midjourney

Popular for high-quality visuals.

Example

• • Creating UI mockups for software engineering projects
  • Generating creative illustrations for project reports

 

4. Video Generation Tools

Synthesia

Creates AI-generated videos using virtual avatars.

Example Use Case

• • Creating project explanation videos
  • Recording seminar presentations without a camera

Prompt Example:

"Explain cloud computing in 2 minutes for engineering students"

Pictory

• • Converts text or blogs into short videos
  • Useful for tech awareness content

5. Audio and Speech Generation Tools

ElevenLabs

Generates realistic human-like speech.

Example

• • Creating narration for project demos
  • Voice-over for technical videos

Whisper (OpenAI)

Used for speech-to-text.

Example

• • Transcribing recorded lectures
  • Converting seminar discussions into text

 

6. Multimodal Generative AI Tools

ChatGPT (Text + Image + Code)

Modern Generative AI tools can handle multiple data types.

Example

• • Upload a network diagram and ask:

"Explain this network architecture and identify bottlenecks"

 

Generative AI in Software Engineering Lifecycle

Phase	How GenAI Helps
Requirement Analysis	Generate user stories
Design	Create UML explanations
Development	Write and review code
Testing	Generate test cases
Documentation	Create reports and manuals
Maintenance	Debug and refactor code

 

Ethical Issues and Limitations

Challenges

• • Hallucinations (incorrect output)
  • Bias in training data
  • Copyright concerns
  • Over-dependence by students

Responsible Use for Students

• • Use AI as a learning assistant, not a shortcut
  • Always verify outputs
  • Avoid plagiarism
  • Understand concepts before submitting work

 

How ISE Students Can Start Using Generative AI

1. 1. Create free accounts (ChatGPT, Gemini)
   2. Practice writing clear prompts
   3. Use AI for:
      • Concept clarification
      • Coding practice
      • Project ideation
   4. Combine AI output with your own understanding

 

Future of Generative AI

Generative AI will:

• • Become part of every software tool
  • Automate repetitive engineering tasks
  • Create demand for AI-literate engineers
  • Transform education, healthcare, cybersecurity, and networking

As future engineers, students who understand and responsibly use Generative AI will have a significant advantage.

 

Conclusion

Generative AI is not just another technology trend—it is a fundamental shift in how software is developed and knowledge is created. For 6th semester Information Science Engineering students, learning Generative AI tools is essential for academic excellence, industry readiness, and lifelong learning.

By understanding the tools, practicing with examples, and using them ethically, students can turn Generative AI into a powerful ally in their engineering journey.