Page 12 - Artificial Intellegence_v2.0_Class_9
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UNIT SUB-UNIT LEARNING OUTCOMES SESSION / ACTIVITY / PRACTICAL
To research and develop awareness of Session: Theme-based research and Case-Studies
skills required for jobs of the future. • Learners will listen to various case-studies of inspiring start-ups, companies or
To imagine, examine and reflect on communities where AI has been involved in real-life.
the skills required for the futuristic • Learners will be allotted a theme around which they need to search for present AI trends
Possibilities opportunities. and have to visualise the future of AI in and around their respective theme.
To develop effective communication Recommended Activity: Job Ad Creating activity
and collaborative work skills. • Learners to create a job advertisement for a firm describing the nature of job available and
the skill set required for it 10 years down the line. They need to figure out how AI is going
AI PROJECT CYCLE To understand and reflect on the Video Session: Discussing about AI Ethics
to transform the nature of jobs and create the Ad accordingly.
ethical issues around AI.
Recommended Activity: Ethics Awareness
• Students play the role of major stakeholders, and they have to decide what is ethical and
what is not for a given scenario.
• Students to explore Moral Machine (https://www.moralmachine.net/) to understand
more about the impact of ethical concerns.
AI Ethics To gain awareness around AI bias and Session: AI Bias and AI Access
AI access. • Discussing about the possible bias in data collection
• Discussing about the implications of AI technology
To let the students analyse the Recommended Activity: Balloon Debate
advantages and disadvantages of • Students divide in teams of 3 and 2 teams are given same theme. One team goes in
Artificial Intelligence. affirmation to AI for their section while the other one goes against it.
• They have to come up with their points as to why AI is beneficial/harmful for the society.
Identify the AI Project Cycle Session: Introduction to AI Project Cycle
framework. • Problem Scoping • Data Acquisition • Data Exploration
• Modelling • Evaluation
Learn problem scoping and ways to set Activity: Brainstorm around the theme provided and set a goal for the AI project.
goals for an AI project. • Discuss various topics within the given theme and select one.
• Fill in the 4Ws problem canvas and a problem statement to learn more about the
problem identified in the community/society.
• List down/ Draw a mind map of problems related to the selected topic and choose one
problem to be the goal for the project.
Identify stakeholders involved in the Activity: To set actions around the goal.
Problem problem scoped. • List down the stakeholders involved in the problem.
Scoping Brainstorm on the ethical issues • Search on the current actions taken to solve this problem.
involved around the problem selected. • Think around the ethics involved in the goal of your project.
Understand the iterative nature of Activity: Data and Analysis
problem scoping for in the AI project • What are the data features needed? • How will the features collected affect the problem?
cycle. • Where can you get the data? • How frequent do you have to collect the data?
Foresee the kind of data required and • What happens if you don’t have enough data?
the kind of analysis to be done. • What kind of analysis needs to be done? • How will it be validated?
• How does the analysis inform the action?
Share what the students have Presentation: Presenting the goal, actions and data.
AI PROJECT CYCLE Data Identify data requirements and find Activity: Introduction to data and its types.
discussed so far.
Teamwork Activity:
• Brainstorming solutions for the problem statement.
reliable sources to obtain relevant • Students work around the scenarios given to them and think of ways to acquire data.
Activity: Data Features
data.
Acquisition
• Identifying the possible data features affecting the problem.
Activity: System Maps
• Creating system maps considering data features identified.
To understand the purpose of Data Session: Data Visualisation
Visualisation • Need of visualising data
• Ways to visualise data using various types of graphical tools.
• Quiz Time
Use various types of graphs to visualise Recommended Activities: Let’s use Graphical Tools
Data acquired data. • Selecting an appropriate graphical format and presenting the graph sketched.
Exploration • Understanding graphs using ( https://datavizcatalogue.com/ )
• Listing of newly learnt data visualization techniques.
• Top 10 Song Prediction: Identify the data features, collect the data and convert into
graphical representation.
• Collect and store data in a spreadsheet and create some graphical representations to
understand the data effectively.
Understand modeling (Rule-based & Session: Modeling
Learning-based) • Introduction to modeling and types of models (Rule-based & Learning-based)
Recommended Activity: Rule-based & Learning-based)
Modelling • Rule-based: Students can be asked to create text to speech bot using
(https://theaiplayground.com/blocks/new)
• Learning-based Activity: Students can be asked to use
(https://teachablemachine.withgoogle.com/)
(x)

