The rapid advancement of Artificial Intelligence (AI) is dramatically transforming industries and society, dramatically changing our interactions with technology.
New developments will improve lives in numerous ways.
The updated curriculum for classes XI and XII offers a thorough exploration of AI, covering core concepts, applications, and potential impacts.
The curriculum was created with teacher advisors, managed by 1M1B and supported by IBM.
It aligns with National Skills Qualification Framework (NSQF) Levels 3 & 4.
- This curriculum encourages educators to deliver AI learning effectively, fostering a fundamental understanding and application of AI.
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Index
Unit 1: Introduction - AI for Everyone - Page 1
Unit 2: Unlocking your Future in AI - Page 18
Unit 3: Python Programming - Page 28
Unit 4: Introduction to Capstone Project - Page 55
Unit 5: Data Literacy - Data Collection to Data Analysis - Page 71
Unit 6: Machine Learning Algorithms - Page 99
Unit 7: Leveraging Linguistics and Computer Science - Page 123
Covers aspects such as definition, evolution, types, domains, terminologies, and applications of AI.
Discusses supervised learning, Natural Language Processing (NLP), computer vision, machine learning (ML), and deep learning (DL).
Outlines benefits/limitations and addresses concerns about job displacement, ethics, explainability, data privacy.
Learning Objectives:
Understand basic concepts/principles of AI.
Explore AI's evolution and identify its types.
Learn AI domains such as statistical data, NLP, and computer vision.
Understand AI terminologies including machine learning and deep learning.
Key Concepts:
Definition of AI
Evolution of AI
Types of AI
Domains of AI
AI Terminologies
Benefits/limitations of AI
Learning Outcomes:
Effectively communicate AI concepts/applications.
Describe AI's historical development.
Differentiate various AI types/domains and their applications.
Recognize key terms/concepts related to ML/DL.
Formulate informed opinions on AI's potential in various contexts.
Pre-requisites:
- Reasonable fluency in English and basic computer skills.
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What is Artificial Intelligence (AI)?
AI refers to a technology that enables machines to learn patterns and predict outcomes, incorporating computer science and robust datasets.
AI enhances human judgment rather than replacing decisions, acting as a "smart helper" capable of learning, understanding language, recognizing images, making predictions, playing games, and autonomously driving cars.
Exclusions of AI:
Traditional rule-based systems, simple automation tools, mechanical devices, fixed-function hardware, non-interactive systems, basic sensors do not qualify as AI due to lack of learning from data.
Evolution of AI:
Dates back to ancient philosophical discussions about intelligence, modern advancements began mid-20th century.
Significant Milestones:
1950: Turing's paper questioned machine intelligence; introduced Turing test.
1956: Dartmouth conference coined "Artificial Intelligence"; AI became a field of study.
1960-1970: Progress in expert systems, neural networks, symbolic reasoning emerged.
1980-1990: Breakthroughs in ML and neural networks but led to "AI winter".
- 21st Century: Resurgence; breakthroughs in DL, machine learning, and reinforcement learning led to applications in various industries.
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Types of AI:
Narrow AI:
Focuses on single tasks—rapidly growing in consumer applications.
Lacks general understanding, examples include voice assistants (e.g., Siri).
Broad AI:
Midpoint between Narrow and General AI.
Handles a wider range of tasks, often requiring domain-specific knowledge.
General AI:
Capable of performing any intellectual task like a human.
Current capabilities fall short; potential future emergence of artificial superintelligence (ASI).
Types of Data:
Data defined as factors that shape experiences, decisions, interactions.
Titled "new oil of the 21st century"; Facts, statistics, opinions, etc.
Classification of Data:
Structured Data: Organized in rows/columns, easy to analyze.
Unstructured Data: Lacks organization, difficult to analyze (e.g., images, text).
3. Semi-structured Data: Combination of both (e.g., social media videos with hashtags).
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Domains of AI:
Statistical Data:
Involves numerical data collection, analysis, interpretation using statistical methods and machine learning.
Applications: Search recommendations, personalized recommendations, social media, etc.
Natural Language Processing (NLP):
Focuses on text and speech processing, enabling computers to interpret and generate human language.
Tasks include sentiment analysis, language translation, and speech recognition.
Differences in NLP, NLU (Natural Language Understanding), NLG (Natural Language Generation).
Computer Vision:
Deals with understanding and interpreting visual data through algorithms.
- Applications: Object detection, image recognition, autonomous vehicles, medical imaging.
- Discuss AI capabilities with various real-world applications.
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Benefits & Limitations of AI:
Benefits:
Increased efficiency and productivity across sectors.
Improved decision-making through data patterns.
Fostered innovation and creativity by automating routine tasks.
Contributions to medical advancements, scientific discoveries, and more.
Limitations:
Job displacement concerns with automation.
Ethical implications around data bias and usage.
Lack of model explainability.
4. Data privacy and security vulnerabilities.
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Extension Activities:
AI in the News: Research recent AI advancements, ethical dilemmas, and AI applications.
AI Applications Showcase: Groups research a specified AI application and present findings.
AI Coding Projects: Introduction to basic coding concepts and libraries (e.g., Python).
4. AI Film Analysis: Watch and analyze films on AI themes, leading to ethical discussions.
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Exercise Types:
- Multiple-Choice Questions (MCQs), Fill in the Blanks, True/False Questions, Short Answer Questions, Long Answer Questions, and Case-study/Application Oriented Questions.
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- Unit 2: Unlocking Your Future in AI: Discusses global demand for AI professionals, common roles, essential skills, and industry opportunities.
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Unit 3: Python Programming
- Introduces Python fundamentals, libraries like NumPy, Pandas, and Scikit-learn, and applies to Data Science projects.
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- Unit 4: Introduction to Capstone Project: Focuses on design thinking, empathy maps, capstone project significance, and links to sustainable development goals.
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- Unit 5: Data Literacy – Data Collection to Data Analysis: Teaches data collection methods, statistical analysis, and data preprocessing.
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- Unit 6: Machine Learning Algorithms: Covers supervised learning (regression/classification), unsupervised learning (clustering), and derivations of types of ML.
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- Unit 7: Leveraging Linguistics and Computer Science: Highlights application of NLP in recognizing speech and translating text as it encompasses various fields.
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Unit 8: AI Ethics and Values: Discusses ethical implications in AI development, laws, principles, and fairness across various scenarios.