CTAI, Class 8

Stage: Middle | Hours/Year: 100 Components: Advanced CT Skills (40 hrs) + Introductory AI Concepts (20 hrs) + Interdisciplinary Projects (40 hrs) Delivery: CT worksheets + AI Foundation Handbook + Project-based learning modules Taught by: Subject teachers (for CT) and Computer teachers (for AI Literacy); projects assessed by Computer teacher

Curricular Goals

Goal	Description
CG-1	Develops CT skills: decomposition, pattern recognition, data representation, generalisation, abstraction, and algorithms to solve problems where such techniques of CT are effective.
CG-2	Develops spatial and visual reasoning.
CG-3	Gain foundational knowledge of AI, its types, and domains.
CG-4	Understand key ethical terms such as bias and fairness in relation to AI.
CG-5	Demonstrates proficiency in using a computer and other devices, computer applications for learning and practical purposes, such as data analysis, preparation of visual representations and communication of ideas.

Competencies

Code	Competency
C-1	Approaches problems using programmatic thinking techniques such as iteration, symbolic representation and logical operations, and reformulates problems into a series of ordered steps.
C-2	Learns systematic arithmetic reasoning, iterative patterns, and multiple data representations, to devise and follow algorithms, with an eye towards understanding correctness, effectiveness, and efficiency of algorithms.
C-3	Learns to visualise, manipulate, represent, and understand spatial relationships between objects.
C-4	Applies abstraction and generalisation to identify core structures and patterns enabling reusable procedures.
C-5	Demonstrate knowledge of AI tools through different projects and activities.
C-6	Identifies ethical issues and applies ethical principles to make informed decisions regarding AI usage.
C-7	Uses computers or any other appropriate devices and software/applications for creating visual representations of ideas, organising and analysing data, conducting simple online research, gathering images, and designing infographics.

Note: CG and Competency codes are shared across the Middle Stage (Classes 6-8). The learning outcomes below represent the highest level of the Middle Stage.

Learning Outcomes, CT

Abstract Thinking

Students will be able to solve advanced, multi-layered problems involving abstract relationships and hidden structures, using:

Properties and relationships of numbers (powers, factors, remainders, divisibility)
Generalisation across different number systems (decimal, binary, ternary, Roman, Chinese numerals) (NEW), Spatial visualisation of 2-D/3-D figures, including overlaps, intersections, and transformations
Logical interpretation of symbols, codes, and operations representing numerical or algebraic ideas (NEW)
Identification of essential information by ignoring irrelevant or misleading data (NEW)

Content creator note: Major shift at Class 8: abstract thinking moves beyond spatial/visual reasoning to include number theory (powers, factors, remainders, divisibility), multiple number systems (binary, ternary, Roman, Chinese), symbol/code interpretation, and, critically, the ability to distinguish essential from irrelevant/misleading data. This prepares students for higher-order analytical thinking.

Pattern Recognition

Students will be able to identify, compare, and extend complex patterns involving multiple simultaneous changes, formed using:

Powers, exponents, and numerical structures (NEW)
Relationships across different representations of the same number (NEW), Geometric configurations and shape-based sequences, Conditional patterns based on rules, constraints, or dependencies, Mixed patterns involving numbers, symbols, shapes, and movement

Content creator note: Key advances from Class 7: (1) "compare" is added to identify and extend, (2) powers and exponents as pattern elements, (3) patterns across different representations of the same number (e.g., fraction/decimal/percentage equivalences), (4) movement-based patterns are added. Notably, "predict" from Class 7 is replaced by "compare", suggesting analysis and comparison across pattern types.

Decomposition

Students will be able to break down high-order logical problems into manageable components by:

Separating given conditions, constraints, and goals, Analysing multi-step processes such as distribution, transfers, and exchanges
Breaking numerical expressions into simpler equivalent forms (NEW), Interpreting tables, grids, networks, and diagrams with multiple dependencies (NEW: networks), Structuring problems involving multiple variables, positions, or cases (NEW)

Content creator note: Key advances from Class 7: (1) "high-order logical problems" framing, (2) breaking numerical expressions into equivalent forms (algebraic decomposition), (3) networks are introduced alongside tables/grids/diagrams, (4) multiple variables and cases, approaching systematic case analysis. The framing shifts from "real-world and abstract" to specifically "logical problems," emphasising rigorous reasoning.

Algorithmic Thinking

Students will be able to design, follow, and evaluate multi-step logical procedures to solve problems involving:

Rule-based transformations of numbers or symbols, Step-wise movement on grids, tracks, or paths with constraints
Conditional instructions (if-then, either-or, must/must not) (expanded conditionals)
Sequential decision-making under given limitations (NEW)
Optimisation problems involving maximum or minimum outcomes (NEW, explicit)

Content creator note: Class 8 adds "evaluate" to design and follow, students must now assess the quality of procedures. Key advances: (1) expanded conditional types (either-or, must/must not alongside if-then), (2) sequential decision-making under constraints, (3) explicit optimisation (max/min). This is the capstone of the Middle Stage algorithmic thinking.

Learning Outcomes, AI

Learners will be able to:

Describe the stages of the AI project cycle: Define Problem, Collect Data, Test AI Tools, Reflect and Improve
Apply no-code tools to tackle real-world problems and reflect on their utility/effectiveness
Explain how AI uses data, find and research sources of bias in datasets, and apply basic strategies to ensure fairness and inclusivity
Recognise how bias in AI leads to unfair conclusions and realise the importance of accountability, privacy, and serving human interests
Explain the uses of AI in daily life and understand AI as a specific type of algorithm that uses datasets, learning, and prediction
Analyse contributions of AI to fields like healthcare, automation, and education, understanding both benefits and risks
Describe AI ethics as the values and guidelines that ensure AI is created and used responsibly

AI Syllabus (20 hours)

#	Topic	Hours	Key Content
1	AI Project Lifecycle (Conceptual)	5	Understanding stages: Define Problem, Collect Data, Test AI Tools, Reflect and Improve; how AI learns from patterns in data
2	Deeper Dive into AI Applications	5	AI in environment, healthcare, automation, and education; connecting AI to real-world problem-solving; hands-on experience with simple no-code AI tools (image classifiers, chatbots, data prediction apps)
3	Data and Fairness	5	How AI uses data; identifying bias in datasets; simple strategies to ensure fairness and inclusivity
4	Ethics and Responsible AI	5	Recognising privacy issues, misinformation, and social impact; responsible use of AI and digital tools; reflection on real-world challenges

Pedagogy Suggestions

For CT, Complex puzzles, riddles, and games at the most advanced Preparatory-to-Middle level, Problems involving multiple number systems, algebraic decomposition, and optimisation, CT resource book aligned chapter-by-chapter with Class 8 Mathematics textbook, Activities where students design, execute, and evaluate their own algorithms, Problems requiring identification of essential vs irrelevant/misleading information

For AI, Hands-on experience with no-code AI tools: image classifiers, chatbots, data prediction apps, Walk through the AI project lifecycle with a real problem, Research activities: finding sources of bias in datasets, Case studies on AI contributions and risks in healthcare, automation, education, environment, Reflection exercises on real-world ethical challenges, Discussions on privacy, misinformation, accountability, and serving human interests

For Interdisciplinary Projects (40 hours), Cross-subject projects applying CT and AI to authentic real-world contexts, Projects should demonstrate the full AI project lifecycle: problem definition, data collection, tool testing, reflection, Include analysis of both benefits and risks of AI applications, Projects assessed by Computer teacher using clear rubrics

Assessment Methods

Written tests, Interactive group activities, Practical examinations, Teacher Observation Journal, Thematic projects, Reflections and group discussions, Project presentations, assignments, reflective journals

Key Content Areas for Teaching

CT Content

Number theory: Powers, factors, remainders, divisibility as abstract thinking tools
Multiple number systems: Binary, ternary, Roman numerals, Chinese numerals, generalisation across representations
Overlaps and intersections: Spatial reasoning with overlapping 2D/3D figures
Symbol and code interpretation: Logical decoding of symbols representing numerical/algebraic ideas
Essential vs irrelevant data: Critical skill of filtering out misleading or unnecessary information
Powers and exponents in patterns: Recognising patterns formed by exponential structures
Cross-representation patterns: Same number in different forms (fraction, decimal, percentage, etc.)
Algebraic decomposition: Breaking expressions into simpler equivalent forms
Networks and graphs: Interpreting network diagrams alongside tables/grids
Multiple variables and cases: Structuring problems with several unknowns and systematic case analysis
Expanded conditionals: If-then, either-or, must/must not in algorithmic procedures
Optimisation: Finding maximum or minimum outcomes under constraints
Sequential decision-making: Making a series of decisions under limitations

AI Content

AI project lifecycle: Four stages, Define Problem, Collect Data, Test AI Tools, Reflect and Improve
No-code AI tools: Hands-on with image classifiers, chatbots, data prediction apps
AI as algorithm: Understanding AI as algorithms that use datasets, learning, and prediction
Bias in datasets: Finding sources of bias, understanding how bias leads to unfair conclusions
Fairness strategies: Basic approaches to ensure fairness and inclusivity in AI
AI in multiple sectors: Environment, healthcare, automation, education, both benefits and risks
AI ethics: Values and guidelines for responsible AI creation and use
Accountability and privacy: Who is responsible when AI makes mistakes; protecting personal data
Misinformation: Recognising AI-generated misinformation and its social impact

Full Middle Stage Progression (Classes 6-8)

CT Progression

CT Skill	Class 6	Class 7	Class 8
Abstract Thinking	Multi-step, layered clues; cross-sections, multi-axis symmetry, scale/proportion	Complex, multi-layered; nets, congruence, advanced visual scenarios	Advanced, abstract relationships; number systems, symbol/code interpretation, filtering irrelevant data
Pattern Recognition	Identify, extend, justify; cyclic, dependency, mixed operations	Recognise, extend, predict; algebraic patterns, variables, growth rules	Identify, compare, extend; powers/exponents, cross-representation, movement
Decomposition	Interdependent clues; factors/multiples, cross-referencing, elimination	Real-world and abstract; ratios/percentages/powers, flow-based, data translation	High-order logical; algebraic decomposition, networks, multiple variables/cases
Algorithmic Thinking	Follow, analyse, apply; necessary vs redundant info	Design and follow; conditional branching, decision points, optimisation	Design, follow, evaluate; expanded conditionals, sequential decisions, max/min optimisation

AI Progression

Aspect	Class 6	Class 7	Class 8
Core AI Concepts	What is AI, AI vs automation, 3 learning types	AI domains (CV, NLP, Data Science), predictive techniques	AI project lifecycle, AI as algorithm
Data Skills	Data types, basic organisation	Data collection, visualisation (charts), pattern interpretation	How AI uses data, finding bias in datasets
Applications	Everyday AI examples	AI in healthcare, education, transport, communication	Deeper dive: environment, healthcare, automation, education; hands-on with no-code tools
Ethics	Digital safety, privacy, passwords, footprints	Bias awareness, fair use, digital citizenship	Bias and fairness strategies, accountability, misinformation, responsible AI

Transition to Higher Classes

After Class 8, students will have:

Strong CT foundations across all four skill areas (Abstract Thinking, Pattern Recognition, Decomposition, Algorithmic Thinking), Conceptual understanding of AI fundamentals, domains, and project lifecycle, Hands-on experience with no-code AI tools, Awareness of AI ethics, bias, fairness, and responsible use, Experience with interdisciplinary projects integrating CT and AI

This prepares them for more formal CS/AI education in Classes 9-10 and beyond, where programming, data science, and machine learning concepts are introduced in greater depth.