CTAI, Class 7

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 differentiate the Class 7 level.

Learning Outcomes, CT

Abstract Thinking

Students will be able to interpret and solve complex, multi-layered problems by:

Visualising and analysing 3-D objects and their transformations, including rotations, reflections, cross-sections, and nets (NEW), Understanding compound transformations involving multiple flips, turns, folds, and rearrangements, Identifying hidden relationships and constraints within incomplete figures, patterns, or logical setups, Analysing symmetry, congruence, and proportional reasoning across different representations, Interpreting relative positions, orientations, and viewpoints of objects in advanced visual scenarios

Content creator note: Key advances from Class 6: (1) "nets" of 3D objects are introduced (unfolding), (2) "congruence" is added alongside symmetry, (3) "hidden relationships and constraints" within logical setups (not just visual patterns), (4) "advanced visual scenarios" for position/orientation interpretation. Problems become more abstract and require multi-step spatial reasoning.

Pattern Recognition

Students will be able to recognise, extend, and predict complex patterns involving:

Multi-rule numerical sequences, including alternating, nested, and dependent patterns
Algebraic patterns using variables, expressions, and functional relationships (NEW), Visual and geometric patterns formed through transformations or growth rules (NEW), Letter and symbol-based patterns involving positional and logical dependencies, Integrated patterns combining numbers, shapes, symbols, and logical conditions

Content creator note: Key advances from Class 6: (1) "predict" is added (not just identify, extend, justify), (2) algebraic patterns with variables and functions are introduced, (3) growth rules for geometric patterns are new, (4) "nested" and "dependent" patterns are explicitly named. This is a significant step toward mathematical thinking with variables.

Decomposition

Students will be able to break down real-world and abstract problems by:

Separating interconnected conditions and constraints into manageable components, Analysing number properties (factors, multiples, ratios, percentages, powers) within layered clues (expanded), Deconstructing problems involving spatial reasoning, measurements, and geometry (NEW framing), Interpreting tables, grids, charts, and flow-based information with multiple dependencies, Breaking multi-step logical situations (movement, exchanges, comparisons, scheduling) into ordered steps
Translating visual or verbal information into structured data for systematic analysis (NEW)

Content creator note: Key advances from Class 6: (1) explicit application to "real-world" problems, (2) number properties expand to ratios, percentages, powers, (3) spatial reasoning/measurements/geometry as a decomposition domain, (4) flow-based information is introduced, (5) scheduling problems are new, (6) translating between representations (visual/verbal to structured data) is a critical new skill.

Algorithmic Thinking

Students will be able to design and follow logical procedures to solve advanced problems involving:

Rule-based sequences and algorithms with conditional branching (NEW), Grid-based navigation and pathfinding with constraints and decision points (NEW), Step-wise transformations involving calculations, swaps, transfers, or positional changes, Ordering and arranging elements (people, objects, events) using multiple attributes and logical clues, Solving problems using if-then reasoning, elimination strategies, and logical consistency checks (NEW)
Creating or analysing procedural steps to reach an optimal or valid solution (NEW)

Content creator note: Major leap at Class 7: (1) students now DESIGN procedures (not just follow/analyse), (2) conditional branching (if-then) is introduced, (3) decision points in navigation, (4) elimination strategies and consistency checks, (5) seeking optimal/valid solutions. This is the transition from following algorithms to creating them.

Learning Outcomes, AI

Learners will be able to:

Distinguish key predictive techniques:

Regression, predicting a number based on patterns in past data
Classification, arranging things in groups based on learned patterns
Clustering, automatically grouping similar items together

Explain key AI domains:

Data Science: manage and extract insights from data
Computer Vision: basics of how machines understand and respond to visual information
Natural Language Processing (NLP): basics and limitations of how computers process natural language inputs

Explain what bias in AI means and identify situations where AI can give unfair results
Demonstrate courteous, safe, and responsible use of technology as part of good digital citizenship
Use safe practices for maintaining data privacy, including giving informed consent before personal data is collected, used, shared, archived, or deleted
Collect and organise simple structured data, interpreting patterns and trends, and create bar charts, line graphs, and pie charts
Apply basic predictive approaches/techniques to a small dataset
Explain uses of AI in healthcare, education, transport, and communication

AI Syllabus (20 hours)

#	Topic	Hours	Key Content
1	AI Domains	5	Predictive techniques: classification, regression, clustering (with hands-on practice on small datasets using AI tools); Computer Vision, NLP, and Data Science; examples like chatbots, image recognition, translation tools
2	AI in Industries	5	Applications in healthcare, education, transport, and communication; how AI improves accuracy, efficiency, and productivity
3	Data Visualisation and Analysis	5	Collecting structured data; creating bar charts, line graphs, pie charts; interpreting patterns
4	Ethics and AI Bias Awareness	5	Introduction to bias in AI; case examples; responsible and fair use of AI; digital citizenship

Pedagogy Suggestions

For CT, Complex puzzles, riddles, and games building on Class 6 CT abilities, Independent activities: data collection, organisation, analysis, varied representations, flow charts, CT resource book aligned chapter-by-chapter with Class 7 Mathematics textbook, Problems that require students to DESIGN procedures, not just follow them

For AI, Hands-on practice with predictive techniques on small datasets, Demonstrations of Computer Vision, NLP, and Data Science applications, Real-world case studies of AI in industries (healthcare, education, transport, communication), Data visualisation exercises: collecting data, creating charts, interpreting patterns, Case studies and discussions on AI bias, specific examples of unfair outcomes, Discussions, debates on responsible AI use and digital citizenship

For Interdisciplinary Projects (40 hours), Cross-subject projects applying CT and AI to problems from Maths, Science, Social Studies, English, Projects should integrate data collection, visualisation, and analysis, Include reflection on ethical dimensions of AI applications

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

3D nets and unfolding: Visualising how 3D shapes unfold into 2D nets
Congruence and proportional reasoning: Identifying congruent shapes and proportional relationships
Compound transformations: Multiple transformations applied in sequence
Algebraic patterns: Patterns using variables, expressions, and functional relationships
Growth rules: Geometric patterns that grow according to rules (e.g., fractal-like, tiling)
Ratios, percentages, powers: Using advanced number properties in decomposition
Flow-based information: Interpreting flowcharts, process diagrams, and scheduling problems
Data translation: Converting between visual/verbal information and structured data
Conditional branching: If-then reasoning in algorithmic procedures
Optimisation: Finding optimal or valid solutions among possibilities

AI Content

Predictive techniques: Regression (predict numbers), Classification (sort into groups), Clustering (auto-group similar items), conceptual understanding with hands-on practice
AI domains: Data Science, Computer Vision, NLP, what they do, examples, limitations
Industry applications: AI in healthcare, education, transport, communication, specific use cases
Data visualisation: Bar charts, line graphs, pie charts, creation and interpretation
AI bias: What bias means, how it leads to unfair results, specific case examples
Data privacy: Informed consent, data lifecycle (collected, used, shared, archived, deleted)
Digital citizenship: Responsible, courteous, safe technology use

Progression from Class 6 to Class 7

Area	Class 6	Class 7
CT Abstract	Multi-step with layered clues; cross-sections, multi-axis symmetry	Complex, multi-layered; adds nets, congruence, advanced visual scenarios
CT Patterns	Identify, extend, justify; cyclic, dependency patterns	Recognise, extend, predict; adds algebraic patterns, variables, growth rules
CT Decomposition	Interdependent clues; cross-referencing data	Real-world and abstract; adds ratios/percentages/powers, flow-based info, data translation
CT Algorithms	Follow, analyse, apply; necessary vs redundant info	Design and follow; adds conditional branching, decision points, optimisation
AI Focus	AI basics, data types, simple patterns, digital safety	AI domains (CV, NLP, Data Science), predictive techniques, industry applications, bias awareness