CBSE Class 9 Introduction to Generative AI Important Subjective Questions & Answers (2026–27) | Unit 4 Artificial Intelligence
Class 9 · Artificial Intelligence
Prepare for your CBSE Class 9 Artificial Intelligence examination with these 25 important subjective questions and answers from Unit 4: Introduction to Generative AI. These questions are prepared according to the latest CBSE syllabus (2026–27) and are ideal for Unit Tests, Half-Yearly, Annual Examinations, competency-based assessments, and viva preparation.
Topics Covered
- Introduction to Generative AI
- Conventional AI vs Generative AI
- GAN (Generative Adversarial Network)
- RNN and VAE
- Applications of Generative AI
- Content Creation and Coding
- AI Hallucinations
- Ethical Issues in Generative AI
- Bias, Privacy and Copyright
- Competency-Based & HOTS Questions
CBSE Exam Tip: Focus on understanding the working of GAN, Generator and Discriminator, applications of Generative AI, and ethical issues such as hallucinations, bias, privacy, copyright, and responsible AI usage. These topics are frequently asked in competency-based questions.
Subjective Questions with Answers
Q1. Define Generative AI. What is its primary purpose?
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Answer:
Generative AI is a branch of Artificial Intelligence that creates new and original content such as text, images, audio, videos, computer code, and music.
Its primary purpose is to generate new content by learning patterns from large datasets instead of only analyzing or classifying existing data.
Q2. Differentiate between Conventional AI and Generative AI.
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| Conventional AI | Generative AI |
|---|---|
| Analyzes, classifies, or predicts existing data. | Creates new and original content. |
| Works mainly on predefined tasks. | Learns patterns and generates new outputs. |
| Example: Spam detection, face recognition. | Example: ChatGPT, Gemini, AI image generators. |
Q3. Besides text, what other types of content can Generative AI produce?
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Answer:
Generative AI can create various types of content, including:
- Images
- Audio and speech
- Music compositions
- Videos
- Computer programs and code
- 3D models and digital artwork
Q4. How does the learning approach of Generative AI differ from rule-based systems?
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Answer:
In a rule-based system, developers write fixed rules that the computer follows.
In Generative AI, the model learns patterns from large amounts of data and automatically develops internal mathematical relationships to generate new content without relying solely on predefined rules.
Q5. What is Fine-tuning? Why is it important in Generative AI?
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Answer:
Fine-tuning is the process of further training a pre-trained AI model using specific data for a particular task.
It improves the model's accuracy, makes responses more relevant, and helps generate better results for specialized applications.
Q6. What does GAN stand for? Name its two main components.
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Answer:
GAN stands for Generative Adversarial Network.
It consists of two components:
- Generator
- Discriminator
Q7. Explain the role of the Generator in a GAN.
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Answer:
The Generator creates new synthetic content such as images or text by learning patterns from training data.
Its goal is to generate content that looks so realistic that the Discriminator cannot distinguish it from real data.
Q8. Explain the role of the Discriminator in a GAN.
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Answer:
The Discriminator acts like a judge.
It compares real data with the content generated by the Generator and decides whether the content is genuine or artificially created. Its feedback helps the Generator improve over time.
Q9. What is an RNN? What type of content is it mainly used to generate?
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Answer:
RNN (Recurrent Neural Network) is a type of neural network designed to process sequential data.
It is mainly used for generating text, speech, music, language translation, and other sequence-based applications where previous information is important.
Q10. What is a Variational Autoencoder (VAE)?
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Answer:
VAE (Variational Autoencoder) is a generative AI model that learns the underlying patterns of data and generates new data that is similar to the original dataset.
VAEs are commonly used for image generation, data compression, and anomaly detection.
Q11. How can Generative AI be used in the field of Architecture?
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Answer:
Generative AI helps architects create innovative building designs quickly by generating multiple design options based on user requirements.
It can optimize space utilization, improve energy efficiency, reduce design time, and help architects explore creative ideas before construction begins.
Q12. State two ways in which Generative AI helps software developers in coding.
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Answer:
Generative AI assists programmers by:
- Generating code snippets automatically.
- Detecting and fixing programming errors (debugging).
It also explains code, suggests improvements, and increases software development productivity.
Q13. Discuss the impact of Generative AI on the music industry.
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Answer:
Generative AI can compose original melodies, create background music, suggest lyrics, and generate complete musical pieces.
It supports musicians by providing creative ideas and speeding up music production while allowing artists to experiment with new styles.
Q14. How has Generative AI transformed content creation and digital marketing?
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Answer:
Generative AI helps create blogs, advertisements, product descriptions, emails, social media posts, and marketing content within seconds.
This reduces manual effort, improves productivity, and enables businesses to generate high-quality content quickly.
Q15. Name any three popular Generative AI text-generation tools.
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Answer:
Three popular Generative AI tools are:
- ChatGPT
- Google Gemini
- Microsoft Copilot
Q16. What is AI Hallucination? Why is it considered a limitation of Generative AI?
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Answer:
AI Hallucination occurs when an AI model generates incorrect or false information that appears to be accurate and convincing.
It is considered a limitation because users may unknowingly trust incorrect information, leading to misinformation and poor decision-making. Therefore, AI-generated content should always be verified.
Q17. Explain the ethical concern of Job Displacement due to Generative AI.
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Answer:
Generative AI can automate repetitive tasks such as content writing, coding, customer support, and graphic design.
While it improves efficiency, it may reduce employment opportunities in certain fields. Therefore, individuals should continuously upgrade their skills to work effectively alongside AI.
Q18. Why should AI-generated content be checked for Bias?
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Answer:
AI learns from the data used during training. If the training data contains bias or does not represent all groups fairly, the AI may produce unfair or discriminatory outputs.
Reviewing AI-generated content helps ensure fairness, accuracy, and responsible use of Artificial Intelligence.
Q19. How can User Privacy and Consent be affected by Generative AI?
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Answer:
Generative AI systems may unintentionally collect, store, or reveal sensitive personal information if proper privacy safeguards are not followed.
Developers must obtain user consent, protect personal data, and follow privacy laws to ensure responsible use of AI technologies.
Q20. Discuss the challenge of Ownership and Attribution in AI-generated content.
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Answer:
When Generative AI creates artwork, music, or written content, it is often difficult to determine who owns the final creation.
Questions may arise about whether the ownership belongs to the user, the AI developer, or the creators of the original training data. This creates legal and ethical challenges related to copyright and intellectual property.
Q21. [Competency] Sakshi's friend suggests using a text generator for her essay on "Climate Change." List two guidelines she should follow to use this tool responsibly.
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Answer:
Sakshi should follow these guidelines:
- Use the AI tool only as a learning and research assistant, not to copy the entire essay.
- Verify all facts from reliable sources and write the final essay in her own words.
This promotes responsible AI usage and improves learning.
Q22. [HOTS] "Generative AI creates, while Conventional AI classifies." Justify this statement with a real-life example.
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Answer:
Conventional AI analyzes existing information and classifies or predicts results. For example, an email spam filter classifies emails as "Spam" or "Not Spam".
Generative AI, on the other hand, creates entirely new content. For example, ChatGPT can write an original essay or an AI image generator can create a completely new artwork from a text prompt.
Thus, Conventional AI focuses on analysis, whereas Generative AI focuses on creation.
Q23. [Competency] You are comparing ChatGPT, Gemini, and Microsoft Copilot. If authenticity of information is your main concern, how should you evaluate these tools?
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Answer:
I would evaluate each tool by:
- Checking whether the information is factually correct.
- Verifying important facts using trusted books or official websites.
- Comparing the responses from different AI tools.
- Ensuring the information is up to date before using it.
Authenticity should always be verified because AI tools can sometimes generate incorrect information.
Q24. [HOTS] Explain how Generative AI can benefit the fashion industry while also creating challenges for designers.
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Answer:
Generative AI can quickly create thousands of clothing designs, suggest new colour combinations, and help designers develop innovative fashion collections.
However, it can also imitate existing design styles, creating copyright concerns and reducing opportunities for entry-level designers. Therefore, AI should be used as a creative assistant rather than a replacement for human creativity.
Q25. [Competency/HOTS] How can society balance the benefits of Generative AI with its risks?
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Answer:
Society can balance the advantages and risks of Generative AI by:
- Creating clear laws and ethical guidelines for AI usage.
- Protecting user privacy and personal data.
- Using diverse and unbiased training data.
- Encouraging transparency and accountability in AI systems.
- Educating people about responsible AI usage and digital literacy.
Responsible development and proper regulations will help ensure that Generative AI benefits individuals as well as society.
❓ Quick Revision
- Generative AI creates new content such as text, images, music, videos, and computer code.
- Conventional AI analyzes or classifies existing data, while Generative AI produces new content.
- GAN consists of two components: Generator and Discriminator.
- RNN is mainly used for sequential data such as text, speech, and music.
- VAE is a generative model that learns data patterns to create similar new data.
- Popular Generative AI tools include ChatGPT, Google Gemini, and Microsoft Copilot.
- AI Hallucination refers to incorrect information generated confidently by AI.
- Important ethical concerns include Bias, Privacy, Job Displacement, Copyright, Ownership, and AI Hallucinations.
- Always verify AI-generated information before using it in assignments or decision-making.
- Generative AI should be used responsibly to support creativity, learning, and innovation.
? Exam Tip
For the CBSE Class 9 Artificial Intelligence examination, remember the differences between Conventional AI and Generative AI, the components of GAN (Generator and Discriminator), the uses of RNN and VAE, and important ethical issues such as AI Hallucination, Bias, Privacy, Copyright, and Responsible AI. Competency-based questions are often application-oriented, so explain concepts with suitable real-life examples.