Whole class
Shared foundations
Teacher-led enquiry, common datasets and coordinated practical work give every learner access to essential concepts before specialisation.
Secondary education · Ages 11–16
A practical AI and robotics pathway built for whole classes, project teams and international collaboration.
CIRC secondary programmes combine computing, engineering, data, design and responsible innovation. The projects below are designed as feasible models that schools can adapt to timetable, equipment and learner experience. Each model identifies the learning structure, technical scope, team organisation, evidence and implementation conditions.
Key Stages 3–4
Whole-class learning
Team projects
International collaboration
01 / Programme architecture
Three complementary ways to learn AI and robotics
A strong secondary programme does not rely on one competition or one device. Whole-class projects establish shared knowledge, team projects develop ownership and engineering practice, and international projects add authentic communication, comparison and cultural context.
Project teams
Depth and responsibility
Small groups take defined roles, manage a technical brief and produce a tested system with individual and collective evidence.
International
Collaboration across contexts
Partner teams investigate the same challenge locally, exchange structured evidence and combine findings through supervised school channels.
Progression
From rules to intelligent systems
Projects move from algorithms and sensor logic to data-driven models, evaluation, autonomy and responsible deployment.
02 / Whole-class project
How Does AI Make a Decision?
A class investigates classification using one shared, age-appropriate dataset. The project can be delivered without specialist robotics equipment and establishes vocabulary needed for later practical work.
Explore
Rules, patterns and examples
Students compare hand-written rules with a simple trained classifier and examine why the two approaches behave differently.
Investigate
Train and test a model
Groups prepare examples, separate training and test data, run controlled trials and record correct, incorrect and uncertain outputs.
Discuss
Data quality and bias
The class examines missing examples, unbalanced categories, misleading labels and the consequences of using limited data.
Evidence
Class model evaluation
Students produce a confusion matrix or simplified results table, explain a failure case and recommend whether the system is suitable for its intended use.
03 / Whole-class robotics project
Autonomous Delivery Network
The classroom becomes a model transport network. Teams build or program robots for different zones while the whole class agrees common routes, communication rules, safety standards and performance measures.
01
Systems thinking
Students see how navigation, sensors, payload, timing and communication form one system rather than isolated technical exercises.
02
Shared technical standard
The class defines route markers, message formats, stop conditions and test procedures so that separately built components can work together.
03
Operational challenge
Robots complete deliveries while responding to obstacles, changing priorities or simulated service interruptions.
04
Class review
Learners compare reliability, efficiency and safety, then distinguish a successful demonstration from a system ready for repeated use.
04 / Whole-class data project
Smart School Evidence Lab
Students investigate a school question involving energy, air quality, movement, waste, biodiversity or learning spaces. The project links science and geography with computing, statistics and responsible data use.
Question
Define what can be measured
The class turns a broad concern into a researchable question and separates useful evidence from information that would be intrusive or unnecessary.
Collect
Create a shared dataset
Teams gather comparable readings using agreed units, locations, intervals and quality checks.
Analyse
Find patterns carefully
Students visualise data, compare conditions and consider uncertainty, correlation and alternative explanations.
Act
Present an evidence-based proposal
The class develops a dashboard, prototype or recommendation and explains the limits of the available evidence to a school audience.
05 / Team project-based learning
Smart City Rover
Small teams design a mobile robot that inspects a model urban environment, identifies selected hazards or markers and reports useful information. The scope can range from visual coding and fixed routes to Python, computer vision and autonomous navigation.
Product
Define user and mission
Teams select a clear stakeholder—such as a transport planner or maintenance team—and convert the need into testable requirements.
Engineering
Integrate sensing and movement
Students develop chassis, control, sensor and communication functions as separate modules before system integration.
AI
Detect or classify with evidence
Where equipment permits, teams use vision or sensor data to recognise selected conditions and evaluate false positive and false negative results.
Validation
Test beyond the ideal route
A standard course, changed lighting, obstacles and repeated trials reveal reliability, limitations and the need for human override.
06 / Team project-based learning
AI for Sustainable Farming
Students combine environmental sensing, image or tabular data and automated responses to explore how intelligent systems might support plant health while avoiding simplistic technology claims.
01
Biological context
Teams research the needs of a selected crop or growing system and identify which variables can be measured meaningfully.
02
Data pipeline
Students collect, label and inspect data before building rules or a simple model for identifying conditions such as moisture need or visible plant stress.
03
Automated response
A prototype can trigger an alert, irrigation simulation or other controlled action, with thresholds and override clearly documented.
04
Impact review
Teams consider water, energy, cost, reliability and environmental trade-offs before presenting a recommendation.
07 / Team project-based learning
Inclusive Technology Design Lab
Teams co-design a digital or robotic prototype around an accessibility scenario. The project teaches that responsible innovation begins with people and context, not assumptions made by developers.
Research
Understand a bounded user scenario
Students use approved personas, expert resources or supervised consultation and avoid claiming that one user represents everyone.
Prototype
Build an assistive interaction
Possible outcomes include accessible interfaces, reminder systems, navigation aids or adapted robot controls.
Test
Evaluate usability and failure
Teams use task-based tests, accessibility checks and structured feedback to identify barriers and unintended consequences.
Responsibility
Protect dignity, privacy and choice
Students explain consent, data minimisation, human support and why an assistive system must not remove user control.
08 / International team project
AI for Climate-Resilient Schools
School teams in two or more countries investigate how climate or environmental conditions affect their learning environments. They use a common research protocol while designing locally relevant solutions.
01
Shared brief
Partners agree core measures, technical vocabulary, evidence formats, milestones and responsible-data rules before student exchange begins.
02
Local investigation
Each team studies a suitable issue such as heat, air quality, water, energy or biodiversity using data available in its own context.
03
Cross-border design review
Students exchange concise evidence packs and question one another’s assumptions, methods and proposed AI or robotics solutions.
04
Joint output
Partners create a comparative report, bilingual exhibition, prototype showcase or combined recommendations while retaining local differences.
09 / International robotics challenge
Global Search and Support Mission
International teams develop compatible robot or simulation modules for a shared humanitarian scenario. Schools can participate with different hardware by using a common mission specification and evidence rubric.
Distributed roles
One mission, connected sub-teams
Teams may specialise in navigation, perception, mapping, communication, payload or human–robot interaction and document their interface requirements.
Interoperability
Design for another team
Students create data formats, test cases and technical documentation that allow a partner to understand or integrate their module.
Challenge event
Test against common scenarios
Robots or simulations complete standard tasks under agreed conditions, with reliability and explanation valued alongside speed.
Reflection
Compare engineering decisions
A moderated review examines how resources, environment, culture and team organisation influenced each solution.
10 / International AI product studio
Student Teams Build a Multilingual Community App
Mixed-school teams design a mobile or web prototype that helps young people understand a local service, cultural place or sustainability issue across languages. AI may support translation, search or content organisation, subject to verification.
01
Product discovery
Students interview appropriate users, define the communication problem and agree a realistic minimum viable product.
02
Multilingual design
Partner teams review terminology, tone, accessibility and cultural context rather than assuming automated translation is sufficient.
03
Agile collaboration
A shared backlog, weekly review and defined product, design, development, data and quality roles create accountable progress.
04
Public demonstration
Teams present the user journey, working features, AI use, test evidence, limitations and a roadmap for improvement.
11 / Project team operating model
Professional habits adapted for secondary learners
Product lead
Need and priorities
Maintains the problem statement, user stories, requirements and agreed priorities without making every decision alone.
Technical team
Software, hardware and data
Develops modules, records configurations and explains how components interact.
Test lead
Evidence and reliability
Plans repeatable tests, records defects and ensures that unsuccessful trials inform the next iteration.
Communication lead
Documentation and review
Coordinates design records, partner communication and the final evidence-led demonstration.
12 / Curriculum and technical progression
Projects can begin accessibly and grow in technical depth
Foundation
Visual coding and rule-based systems
Sequences, variables, events, Boolean logic, sensor thresholds, simple data and controlled physical outputs.
Intermediate
Python, APIs and structured data
Functions, data handling, device control, web services and modular programs linked to a defined user task.
Advanced
Models, vision and autonomy
Training and test data, classification metrics, computer vision, path planning and documented human oversight.
Across all levels
Responsible design
Privacy, bias, transparency, safety, accessibility, intellectual property, environmental impact and appropriate human control.
13 / Assessment and evidence
Individual learning remains visible inside team achievement
01
Knowledge evidence
Concept checks, annotated diagrams, code explanation and short technical questions confirm individual understanding.
02
Process evidence
Design journal, sprint board, decision log, version history, data record and test reports show how the solution developed.
03
Product evidence
Working prototype, standard challenge results, usability feedback and documented limitations demonstrate performance.
04
Communication evidence
Technical presentation, live demonstration, partner review and individual reflection show judgement and learning transfer.
14 / Feasibility and safeguarding
Implementation conditions are defined before a project begins
01
Equipment and access
Each brief has low-equipment, standard and advanced variants so schools can match available devices, networks, space and technical support.
02
Data protection
Projects minimise personal data, use approved accounts and datasets, define storage and deletion, and prohibit unnecessary biometric or sensitive-data collection.
03
Physical and online safety
Risk assessment covers robot movement, tools, batteries, workshop practice, communication platforms, moderation and student contact.
04
International governance
Schools agree safeguarding leads, teacher-supervised channels, consent, schedules, language support, data exchange and escalation procedures.
15 / Delivery models for schools
A programme can fit a lesson sequence, a term or a multi-school partnership
4–6 lessons
Whole-class enquiry
A focused shared investigation suitable for curriculum enrichment, introductory AI literacy or preparation for a larger project.
8–12 weeks
Team project studio
A complete design-build-test cycle with specialist roles, milestones, portfolio evidence and final review.
One or two terms
International collaboration
Partner matching, shared brief, local development, moderated exchanges, joint review and a final international showcase.
Multi-year
Secondary pathway
Progressive projects across year groups, teacher professional development and reusable equipment, resources and assessment.
Build the next programme
Ready to turn AI and robotics into purposeful student work?
We can shape a class project, school pilot, international team or multi-school pathway around your learners.