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Artificial Intelligence Curriculum aims at developing the learner’s mindset and skill set towards Artificial Intelligence and how it is understood and applied. Students can be assessed and evaluated on the basis of following skills:
Enriched with updated AI tech and social concepts, real-life examples, and AI project development guides using no-code tools, this book is designed to inspire students to not only understand AI but also to leverage it to drive positive social change.
The document provides the curriculum for Artificial Intelligence (subject code 417) for classes 9 and 10. It outlines the objectives, learning outcomes, skills developed and scheme of studies. The objectives are to develop an understanding of AI and its applications.
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- ARTIFICIAL INTELLIGENCE (CODE 417)
- OBJECTIVE
- LEARNING OUTCOMES
- Purpose
- Recommended Activity: Go Goals Board Game
- Session: Theme-based research and Case Studies
- Recommended Activity: Ethics Awareness
- Session: AI Bias and AI Access
- Recommended Activity: Balloon Debate
- Session: Introduction to AI Project Cycle
- Activity: Data and Analysis
- Data Acquisition
- Session: Data Visualisation
- Recommended Activity: Let’s use Graphical Tools
- Session: Decision Tree
- Recommended Activity: Decision Tree
- Recommended Activity: Pixel It
- Session: Introduction to neural network
- Session: Introduction to Python language
- Practical: Python Basics
- Practical: Python Lists
CURRICULUM FOR CLASS IX (INSPIRE AND ACQUIRE MODULE)
The objective of this module/curriculum - which combines both Inspire and Acquire modules is to develop a readiness for understanding and appreciating Artificial Intelligence and its application in our lives. This module/curriculum focuses on: Helping learners understand the world of Artificial Intelligence and its applications through games, activ...
Learners will be able to Identify and appreciate Artificial Intelligence and describe its applications in daily life. Relate, apply and reflect on the Human-Machine Interactions to identify and interact with the three domains of AI: Data, Computer Vision and Natural Language Processing and Undergo assessment for analysing their progress towards a...
Session: Introduction to sustainable development goals
Learners to answer questions on Sustainable Development Goals To understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship. Possibilities
Learners will listen to various case-studies of inspiring start-ups, companies or communities where AI has been involved in real-life. Learners will be allotted a theme around which they need to search for present AI trends and have to visualise the future of AI in and around their respective theme.
Students play the role of major stakeholders and they have to decide what is ethical and what is not for a given scenario.
Discussing about the possible bias in data collection Discussing about the implications of AI technology
Students divide in teams of 3 and 2 teams are given same theme. One team goes in 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. To research and develop awareness of skills required for jobs of the future. To imagine, examine and refl...
Identify the AI Project Cycle framework. Problem Scoping Data Acquisition Data Exploration Modelling Evaluation Activity: Brainstorm around the theme provided and set a goal for the AI project. Discuss various topics within the given theme and select one. List down/ Draw a mindmap of problems related to the selected topic and choose one problem to ...
What are the data features needed? Where can you get the data? How frequent do you have to collect the data? What happens if you don’t have enough data? What kind of analysis needs to be done? How will it be validated? How does the analysis inform the action? Presentation: Presenting the goal, actions and data. Learn problem scoping and ways to set...
Activity: Introduction to data and its types. Students work around the scenarios given to them and think of ways to acquire data. Identify data requirements and find reliable sources to obtain relevant data.
Need of visualising data Ways to visualise data using various types of graphical tools. To understand the purpose of Data Visualisation
To decide what kind of data is required for a given scenario and acquire the same. To select an appropriate graphical format to represent the data acquired. Presenting the graph sketched. Use various types of graphs to visualise acquired data.
To introduce basic structure of Decision Trees to students.
To design a Decision Tree based on the data given.
To create an “AI Model” to classify handwritten letters. Students develop a model to classify handwritten letters by diving the alphabets into pixels. Pixels are then joined together to analyse a pattern amongst same alphabets and to differentiate the different ones.
Relation between the neural network and nervous system in human body Describing the function of neural network.
Introducing python programming and its applications
Students go through lessons on Python Basics (Variables, Arithmetic Operators, Expressions, Data Types - integer, float, strings, using print() and input() functions) Students will try some simple problem solving exercises on Python Compiler.
Students go through lessons on Python Lists (Simple operations using list) Students will try some basic problem Acquire introductory Python programming skills in a very user-friendly format. solving exercises using lists on Python Compiler. SKILLS TO BE DEVELOPED
Artificial Intelligence for Class 9 for CBSE Includes all the Syllabus and the content provided by CBSE through Handbooks and Facilitators. As a beginning in this direction, CBSE introduced Artificial Intelligence as an optional subject at Class IX from the Session 2019-2020 onwards.
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4 days ago · EduRev's Artificial Intelligence (AI) for Class 9 Course is designed to introduce students to the fascinating world of AI. This course covers the basics of AI, including machine learning, neural networks, and natural language processing.
