Class 8 CBSE Artificial Intelligence Facilitator Handbook (Q&A)
Q1: What
is a project cycle in the context of AI?
A1: The AI project cycle is a cyclical process
followed to complete an AI project effectively, guiding through phases like
problem scoping, data acquisition, data exploration, modelling, evaluation, and
deployment.
Q2: What are the key stages of the AI project
cycle?
A2: The six stages are: 1) Problem Scoping, 2)
Data Acquisition, 3) Data Exploration, 4) Modeling, 5) Evaluation, and 6)
Deployment.
Q3: Why
is problem scoping considered important in the AI project cycle?
A3: Problem scoping defines the project goal,
understands the problem, and develops a vision to solve it; it’s the foundation
of the AI project.
Q4: What does data acquisition entail in AI
projects?
A4: Data acquisition involves collecting
relevant, reliable data from primary or secondary sources to train AI models.
Q5: How does data exploration benefit an AI
project?
A5: Data exploration visualizes data to identify
useful trends and patterns, facilitating better decision-making and understanding.
Q6: What is the objective of the modelling
stage?
A6: Modelling involves selecting and training AI
models/algorithms to address the defined problem and make predictions or
decisions.
Q7: How is the evaluation stage conducted in an
AI project?
A7: Evaluation tests different models using data
to determine the best performing AI solution.
Q8: What happens during deployment in the AI
project cycle?
A8: Deployment
makes the AI solution accessible to users via platforms like mobile
applications or websites.
Q9: What is AI ethics?
A9:AI Ethics is a moral framework guiding the
responsible development and use of AI, ensuring fairness, privacy, and
sustainability.
Q10: Name some ethical concerns associated with
AI.
A10: Concerns include AI bias, privacy issues,
job replacement, and misinformation.
Q11: What are the parameters to ensure ethical
integration in AI?
A11: Human-centric design, unbiased systems,
data protection, and sustainable AI solutions.
Chapter 2: AI Project Cycle Detailed Activities and Use Cases
Q1: What is the 4Ws method used in problem
scoping?
A1: The 4Ws stand for Who, What, Where, and Why
to define stakeholders, the nature of the problem, context, and significance of
solving it.
Q2: Give an example of data types collected
during data acquisition?
A2: Textual data, numeric data, and visual data
(images/videos) are common data types collected.
Q3: What is a system map in systems
thinking?
A3: A system map visually represents
interconnected components within a system showing causes, effects, and their
relationships.
Q4: Differentiate rule-based AI and
learning-based AI.
A4: Rule-based AI operates on predefined rules
("If X then Y"), whereas learning-based AI learns patterns from data
without explicit instructions.
Q5: What is the role of data visualization in
data exploration?
A5: Visualization using charts or graphs makes
complex data understandable and helps identify trends and insights.
Chapter 3: AI Ethics and Social Implications
Q1: Why is bias a significant issue in AI?
A1: Bias in AI leads to unfair results based on
race, gender, or socioeconomic status due to biased training data or developer
influence.
Q2: How can AI ethics help mitigate bias?
A2: By designing unbiased algorithms and
equitable AI systems that promote fairness and inclusiveness.
Q3: What makes AI ethics important for
society?
A3: It ensures AI benefits all fairly, protects
privacy, avoids harm, and promotes sustainable development.
Q4: What is human-centric AI?
A4: AI that prioritizes human well-being,
values, and societal benefits.
Q5: How does AI impact employment?
A5: AI can replace some jobs by automating
tasks, which raises ethical considerations about workforce displacement.
Chapter 4: AI Domains and Applications
Q1: What are the three main domains of AI?
A1: Computer Vision (machines seeing), Natural
Language Processing (machines understanding human language), and Statistical
Data (machines analyzing numerical data).
Q2: Give examples of AI in everyday life.
A2: YouTube video suggestions, Google Maps
navigation, virtual assistants like Alexa/Siri, self-driving cars, AI fitness
apps.
Q3: How is AI different from automation?
A3: Automation enables machines to perform tasks
without human intervention; AI enables machines to think, learn, and make
decisions like humans.
Chapter 5: Group Projects and Presentation Guidelines
Q1: What is the purpose of Group Project 1 in
the AI curriculum?
A1: Groups select themes, identify problems,
create problem statement templates, and explore AI-enabled solutions addressing
those problems.
Q2: How should students prepare for the AI
dialogue presentation?
A2: Using the provided AI project template,
students define AI concepts, map use cases, understand project stages, and
include ethical considerations.
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