Advanced Stage — AI & Robotics Engineering

Objective: Build a clear problem-solving workflow before writing code.

Key concepts: algorithms, decomposition, computational thinking

Materials: computer, Python/Thonny or equivalent, notebook

Hands-on plan

1. Choose a simple real problem and describe the desired result.
2. Break it into inputs, processing steps and outputs.
3. Write pseudocode before touching Python.
4. Translate the pseudocode into a short Python program.
5. Test with two different inputs and compare the results.
6. Refactor one step to make the logic clearer.

Student challenge: Write a small program that converts a real-life process into a sequence of decisions.

Evidence of learning: Student can explain problem → algorithm → code → test.

After class: Write pseudocode for a simple calculator.

Objective: Use variables and basic data types to create interactive programs.

Key concepts: variables, strings, numbers, booleans, input/output

Materials: computer, Python/Thonny

Hands-on plan

1. Create variables for name, age, marks and a boolean choice.
2. Use input() and print() to make the program interactive.
3. Perform arithmetic with numeric variables.
4. Format a clear output.
5. Test invalid or unexpected input and discuss what happens.

Student challenge: Build a personal information/score calculator.

Evidence of learning: Student explains at least three data types and modifies the program.

After class: Add one extra input and output.

Objective: Create programs that make multiple decisions.

Key concepts: if, elif, else, comparisons, logical operators

Materials: computer, Python

Hands-on plan

1. Build a pass/fail condition.
2. Add multiple ranges with elif.
3. Use and/or for combined conditions.
4. Create a menu with several choices.
5. Test boundary values such as exactly at a threshold.
6. Document one edge case.

Student challenge: Build a smart eligibility/decision checker.

Evidence of learning: Boundary tests work correctly.

After class: Add one new condition.

Objective: Use repetition and functions to reduce repeated code.

Key concepts: for, while, functions, parameters, return values

Materials: computer, Python

Hands-on plan

1. Create a loop that processes a sequence of values.
2. Use while for a condition-based loop.
3. Turn repeated logic into a function.
4. Add a parameter and return value.
5. Combine two functions into one small program.
6. Debug a deliberate indentation/logic error.

Student challenge: Build a menu-driven utility with at least two functions.

Evidence of learning: Student explains why functions improve code organization.

After class: Create one reusable function of your own.

Objective: Complete a small Python application and explain its structure.

Key concepts: integration, validation, debugging

Materials: computer, Python

Hands-on plan

1. Choose quiz or calculator.
2. Plan inputs, processing and outputs.
3. Build the first working version.
4. Add validation or scoring.
5. Test at least five cases.
6. Present the program and explain one improvement.

Student challenge: Interactive Quiz / Calculator

Evidence of learning: Working program + test evidence + explanation.

After class: Add one feature after feedback.

Objective: Read a simple circuit and build it safely.

Key concepts: resistor, LED, capacitor, transistor, breadboard, polarity

Materials: breadboard, resistors, LEDs, transistor, multimeter if available

Hands-on plan

1. Identify common components and their symbols.
2. Trace a schematic from power to output.
3. Build an LED-resistor circuit.
4. Use a multimeter to check continuity/voltage where available.
5. Introduce breadboard rows and power rails.
6. Find and correct one intentional wiring error.

Student challenge: Build and document a clean LED circuit.

Evidence of learning: Correct wiring and explanation of current path.

After class: Draw the circuit and label components.

Objective: Understand how a small control signal can switch a load.

Key concepts: digital input, switching, transistor as electronic switch

Materials: push button, transistor, LED, resistor, breadboard

Hands-on plan

1. Test a push button as an input.
2. Build button-controlled LED.
3. Introduce the idea of a transistor as a switch.
4. Use a transistor to control an LED/load within safe limits.
5. Compare direct control and transistor control.
6. Document the circuit.

Student challenge: Build a transistor-switched indicator.

Evidence of learning: Student explains control signal vs switched load.

After class: Sketch the transistor switching path.

Objective: Understand relay switching and safe low-voltage automation.

Key concepts: relay, NO/COM/NC, power separation, protection

Materials: relay module, low-voltage load/LED, controller, diode if required by module/load

Hands-on plan

1. Identify relay terminals and contacts.
2. Test relay output before attaching a load.
3. Use a safe low-voltage load for demonstration.
4. Create ON/OFF control.
5. Discuss inductive loads and why protection matters.
6. Test repeated switching.

Student challenge: Build a safe low-voltage automatic switch.

Evidence of learning: Student identifies NO/COM/NC and explains safe switching.

After class: Draw relay block diagram.

Objective: Measure basic electrical quantities and reason about power requirements.

Key concepts: voltage, current, resistance, power, measurement

Materials: multimeter, resistors, batteries/regulated supply, sample circuits

Hands-on plan

1. Measure voltage across a safe source.
2. Measure resistance of selected resistors.
3. Explain current vs voltage with a practical analogy.
4. Discuss why motors need suitable power sources.
5. Calculate simple power values from given voltage/current.
6. Identify an unsafe connection scenario.

Student challenge: Create a simple power-budget table for a project.

Evidence of learning: Measurements and calculations are sensible and documented.

After class: Calculate power for two example loads.

Objective: Combine circuit construction and control into a small embedded project.

Key concepts: embedded circuit integration

Materials: Arduino/compatible controller, LED, button/potentiometer, transistor/relay as appropriate

Hands-on plan

1. Choose button or potentiometer as input.
2. Build the LED output circuit.
3. Write/upload the control logic.
4. Add a second mode such as dim/bright or manual/automatic.
5. Test and document the circuit.
6. Explain the hardware/software interaction.

Student challenge: Smart LED Controller

Evidence of learning: Working controller + circuit diagram + explanation.

After class: Add one new control mode.

Objective: Understand how sensors produce data that a controller can read.

Key concepts: digital/analog readings, sampling, sensor behavior

Materials: Arduino, LDR, IR/PIR, potentiometer or similar sensors

Hands-on plan

1. Compare a digital sensor state with an analog value.
2. Read a changing sensor value.
3. Print labeled readings through Serial Monitor.
4. Record several measurements.
5. Discuss noise and variation.
6. Choose an appropriate threshold.

Student challenge: Build a sensor test bench.

Evidence of learning: Student can distinguish digital and analog data.

After class: Collect five readings and interpret them.

Objective: Measure distance and evaluate measurement error.

Key concepts: HC-SR04, timing, distance, error

Materials: Arduino, HC-SR04, ruler

Hands-on plan

1. Wire trigger and echo correctly.
2. Read distance values.
3. Compare with ruler measurements.
4. Calculate simple absolute error for sample readings.
5. Test different surfaces/angles.
6. Discuss limitations.

Student challenge: Distance measurement instrument.

Evidence of learning: Student records actual vs measured values and explains error.

After class: Measure three additional distances.

Objective: Collect temperature/humidity data in a structured way.

Key concepts: DHT11/DHT-type sensor, sampling, structured data

Materials: Arduino, DHT11, serial output, spreadsheet optional

Hands-on plan

1. Read temperature and humidity.
2. Print time/value pairs.
3. Collect a short data set.
4. Transfer or manually record data.
5. Look for changes and patterns.
6. Discuss sensor stabilization and limitations.

Student challenge: Smart Environment Monitor.

Evidence of learning: Data set is labeled and interpretable.

After class: Collect readings at three times.

Objective: Read multiple sensors and create a combined decision.

Key concepts: multiple inputs, normalization, thresholds

Materials: Arduino, LDR, PIR/IR, ultrasonic or DHT11

Hands-on plan

1. Test each sensor independently.
2. Assign a variable to each reading.
3. Create separate thresholds.
4. Combine two sensor conditions.
5. Display/log status.
6. Test conflicting inputs.

Student challenge: Multi-Sensor Environment Monitor.

Evidence of learning: Two or more sensors influence system behavior correctly.

After class: Add one extra condition.

Objective: Turn sensor readings into a simple analysis.

Key concepts: data visualization, interpretation, evidence

Materials: computer, sensor data, spreadsheet/Python optional

Hands-on plan

1. Choose one collected data set.
2. Clean obvious recording errors.
3. Create a simple table/plot.
4. Identify a trend or anomaly.
5. Explain what the data can and cannot prove.
6. Present one evidence-based conclusion.

Student challenge: Smart Environment Data Report.

Evidence of learning: Chart/table + short interpretation.

After class: Propose one additional measurement to improve confidence.

Objective: Move from basic sketches to structured embedded programs.

Key concepts: setup/loop, libraries, pin modes, code organization

Materials: Arduino UNO, IDE, LED, button

Hands-on plan

1. Review setup() and loop().
2. Configure digital I/O.
3. Use named constants/variables for pins.
4. Create functions for repeated actions.
5. Compile/upload and read serial messages.
6. Organize code into clear sections.

Student challenge: Build a structured LED/button controller.

Evidence of learning: Code is readable and modular.

After class: Refactor one repeated section.

Objective: Map analog input to a controlled output.

Key concepts: analogRead, PWM, mapping, calibration

Materials: Arduino, potentiometer, LED, servo optional

Hands-on plan

1. Read potentiometer values.
2. Map the range to LED brightness.
3. Create low/mid/high zones.
4. Calibrate using observed values.
5. Control a servo if available.
6. Test response across the full input range.

Student challenge: Analog Control Interface.

Evidence of learning: Input smoothly changes output.

After class: Add a second output mode.

Objective: Understand responsive embedded timing rather than relying only on long delays.

Key concepts: millis concept, interrupts concept, non-blocking timing

Materials: Arduino, buttons, LEDs/buzzer

Hands-on plan

1. Demonstrate delay() and its limitations.
2. Use millis()-based timing for a blinking/status task.
3. Explain interrupts conceptually and use a simple button example where appropriate.
4. Create two activities that run without blocking each other.
5. Test responsiveness.
6. Document the state logic.

Student challenge: Responsive multi-task controller.

Evidence of learning: Two behaviors operate without long blocking delays.

After class: Add a third timed behavior.

Objective: Use serial communication as a debugging and data tool.

Key concepts: Serial Monitor, formatted logs, debugging

Materials: Arduino, sensor, computer

Hands-on plan

1. Print startup information.
2. Print sensor values with labels.
3. Print state changes only when they occur.
4. Create a simple command from Serial Monitor.
5. Use logs to locate a logic error.
6. Clean the final debug output.

Student challenge: Serial Debug Console.

Evidence of learning: Student uses logs to diagnose a deliberately introduced fault.

After class: Add one user command.

Objective: Integrate sensor, controller and actuator into an automation system.

Key concepts: soil moisture, relay/pump, thresholds, safety limit

Materials: Arduino, soil moisture sensor, relay, mini water pump, safe low-voltage supply, plant model

Hands-on plan

1. Calibrate dry and moist readings.
2. Create the moisture threshold.
3. Test relay without pump first.
4. Connect and test low-voltage pump safely.
5. Add maximum runtime/safety cutoff.
6. Demonstrate automatic watering logic and document it.

Student challenge: Automated Plant Watering System.

Evidence of learning: Complete sensor→decision→pump cycle with safety cutoff.

After class: Add manual watering mode.

Objective: Understand motor-driver architecture and build a controlled drive base.

Key concepts: DC motor, H-bridge, driver, chassis, direction

Materials: two DC motors, L298N or suitable driver, chassis, wheels

Hands-on plan

1. Identify motor terminals and driver inputs.
2. Test one motor direction.
3. Test both motors.
4. Create forward/reverse/turn functions.
5. Check mechanical alignment.
6. Document motor pin mapping.

Student challenge: Two-Wheel Drive Base.

Evidence of learning: Robot moves predictably in four basic directions.

After class: Create a reusable movement library.

Objective: Use servo positioning in a practical mechanism.

Key concepts: servo positioning, torque/load, mechanical range

Materials: SG90/servo, Arduino, simple mechanism

Hands-on plan

1. Test safe angle range.
2. Create named positions.
3. Attach a light mechanism.
4. Control angle from a variable or sensor.
5. Test repeated movement.
6. Avoid mechanical overtravel.

Student challenge: Servo Mechanism.

Evidence of learning: Stable repeatable positions.

After class: Add sensor-triggered movement.

Objective: Build the sensor logic behind a line follower.

Key concepts: IR reflectance, sensor array, error/correction

Materials: IR line sensors, robot chassis, motor driver, track

Hands-on plan

1. Calibrate sensors on black/white surfaces.
2. Read multiple sensors.
3. Create a position/error concept.
4. Write basic correction rules.
5. Run at low speed.
6. Tune sensor placement.

Student challenge: Basic Line Follower.

Evidence of learning: Robot follows a simple track.

After class: Reduce oscillation through one controlled change.

Objective: Build autonomous obstacle-response behavior.

Key concepts: ultrasonic, servo scan, decision logic

Materials: robot, ultrasonic, servo, motor driver

Hands-on plan

1. Read forward distance.
2. Stop at threshold.
3. Scan left/right with servo.
4. Compare available space.
5. Choose a turn.
6. Test on a small course.

Student challenge: Autonomous Obstacle Avoider.

Evidence of learning: Robot detects and responds to obstacles without manual commands.

After class: Add a reverse-and-scan state.

Objective: Introduce feedback control through a practical line-following example.

Key concepts: feedback, error, Kp/Ki/Kd concepts, tuning

Materials: line-following robot, sensor array, Arduino

Hands-on plan

1. Define target line position.
2. Calculate conceptual error.
3. Explain proportional correction.
4. Show what derivative can do for sudden changes.
5. Discuss integral and why it may be unnecessary initially.
6. Tune one parameter at a time and record results.

Student challenge: PID/Feedback Tuning Challenge.

Evidence of learning: Student can explain error and why tuning changes motion.

After class: Record two tuning trials and observations.

Objective: Understand the role of ESP32 in connected systems.

Key concepts: ESP32, GPIO, Wi-Fi, connected device architecture

Materials: ESP32/NodeMCU, USB, LED, sensor, Wi-Fi

Hands-on plan

1. Compare Arduino and ESP32 at a high level.
2. Identify GPIO and power pins.
3. Upload a basic sketch.
4. Connect to Wi-Fi.
5. Control an LED locally.
6. Draw device→network→user architecture.

Student challenge: Connected LED Node.

Evidence of learning: ESP32 connects and controls a local output.

After class: Add one sensor reading.

Objective: Use wireless commands to control a device.

Key concepts: wireless commands, serial communication, device control

Materials: ESP32/HC-05 or supported wireless setup, robot/LED

Hands-on plan

1. Test wireless communication.
2. Define command characters/messages.
3. Map commands to outputs.
4. Add a stop/safe command.
5. Test invalid commands.
6. Document command protocol.

Student challenge: Wireless Device Controller.

Evidence of learning: Reliable command-to-output behavior.

After class: Add a second output.

Objective: Create a simple local web interface for hardware control.

Key concepts: HTTP concept, local web interface, GPIO control

Materials: ESP32, Wi-Fi, LED/relay-safe low-voltage output

Hands-on plan

1. Connect ESP32 to Wi-Fi.
2. Serve a simple control page.
3. Add ON/OFF controls.
4. Map requests to GPIO outputs.
5. Test from a browser.
6. Improve labels and status feedback.

Student challenge: Web-Controlled Device.

Evidence of learning: Browser controls the hardware reliably.

After class: Add a second control.

Objective: Display live sensor information on an IoT dashboard.

Key concepts: sensor data, webpage display, data refresh

Materials: ESP32, DHT11/ultrasonic/LDR, browser

Hands-on plan

1. Read the sensor on ESP32.
2. Format data with units.
3. Serve the values to the webpage.
4. Refresh/update values.
5. Add NORMAL/WARNING status.
6. Test sensor disconnect behavior.

Student challenge: IoT Weather / Environment Dashboard.

Evidence of learning: Live sensor values shown clearly.

After class: Add a second sensor card.

Objective: Build a connected weather/environment monitoring prototype.

Key concepts: integration, data display, dashboard

Materials: ESP32, DHT11, optional display/cloud/dashboard

Hands-on plan

1. Connect and validate DHT11.
2. Create dashboard fields.
3. Add Wi-Fi connection status.
4. Display temperature/humidity.
5. Add a threshold alert.
6. Test reconnect/error behavior.
7. Document the system architecture.

Student challenge: IoT Weather Station.

Evidence of learning: Working connected monitoring system.

After class: Add one visualization or additional sensor.

Objective: Understand AI/ML concepts before building a model.

Key concepts: AI, ML, datasets, features, labels, training/testing

Materials: computer, sample dataset, AI/ML tool

Hands-on plan

1. Compare rule-based programming with learning from examples.
2. Introduce dataset, feature and label.
3. Show training vs testing concept.
4. Discuss accuracy and errors.
5. Identify appropriate real-world AI uses.
6. Choose one small classification problem.

Student challenge: AI Use-Case Map.

Evidence of learning: Student explains dataset, training and prediction at a basic level.

After class: Find one AI use case and identify its input/output.

Objective: Prepare a small dataset for a simple classification task.

Key concepts: data quality, labels, train/test split

Materials: computer, small dataset, spreadsheet/Python

Hands-on plan

1. Define classes/labels.
2. Collect or inspect sample data.
3. Remove obvious errors/duplicates.
4. Separate training and testing examples.
5. Discuss imbalance and bias.
6. Document dataset assumptions.

Student challenge: Build a Small Labeled Dataset.

Evidence of learning: Dataset has clear labels and documented assumptions.

After class: Add five more examples.

Objective: Train and evaluate a simple classification model.

Key concepts: classification, features, evaluation

Materials: computer, beginner ML tool/Python as appropriate

Hands-on plan

1. Load prepared data.
2. Select features.
3. Train a simple classifier.
4. Test on unseen examples.
5. Record correct/incorrect predictions.
6. Discuss why a model can fail.

Student challenge: Simple ML Classifier.

Evidence of learning: Student can demonstrate prediction and explain an error.

After class: Improve data or features and retest.

Objective: Explore image recognition and understand its limitations.

Key concepts: images, computer vision, recognition, limitations

Materials: computer camera, vision tool/model

Hands-on plan

1. Introduce image as data.
2. Run a guided object/face recognition example.
3. Test different lighting/backgrounds.
4. Observe false positives/negatives.
5. Connect recognition to an application.
6. Discuss responsible handling of image data.

Student challenge: AI Object Recognition.

Evidence of learning: Working recognition demo + limitation analysis.

After class: Test one additional condition.

Objective: Create an AI-enabled recognition application.

Key concepts: AI model + application logic

Materials: computer/camera, AI/vision tool, optional Arduino/ESP32 output

Hands-on plan

1. Choose two or more object categories or a supported recognition task.
2. Prepare/test examples.
3. Run recognition.
4. Add an application response.
5. Measure a small number of test cases.
6. Document one improvement.

Student challenge: AI-Based Object Recognition.

Evidence of learning: Working prototype with test evidence.

After class: Connect recognition to a safe physical/digital action if available.

Objective: Integrate sensors and connected control into a smart-home model.

Key concepts: IoT, sensors, automation, manual override

Materials: ESP32/Arduino, PIR/LDR, relay module, low-voltage load/LED

Hands-on plan

1. Choose a room scenario.
2. Select sensor and output.
3. Build automatic behavior.
4. Add manual control.
5. Test conflicting commands.
6. Define safe default state.

Student challenge: Smart Home Model.

Evidence of learning: Automatic + manual control works.

After class: Add one additional room/device.

Objective: Design an agriculture monitoring/automation prototype.

Key concepts: soil moisture, irrigation, monitoring

Materials: soil moisture, DHT11, pump/relay, ESP32/Arduino

Hands-on plan

1. Measure soil condition.
2. Add environmental reading.
3. Define irrigation threshold.
4. Control a safe low-voltage pump.
5. Display/log status.
6. Discuss water-saving improvements.

Student challenge: Smart Plant Monitoring / Irrigation.

Evidence of learning: Sensor-based decision with documented threshold.

After class: Add a warning or manual override.

Objective: Build an educational monitoring prototype while distinguishing it from medical diagnosis.

Key concepts: sensor data, thresholds, user interface, responsible use

Materials: available safe educational sensors, Arduino/ESP32, display

Hands-on plan

1. Choose a non-invasive educational measurement available in the kit.
2. Read and display sample values.
3. Create a status threshold for demonstration only.
4. Design a simple user interface.
5. Discuss measurement limitations.
6. Explain why a classroom prototype is not a medical device.

Student challenge: Educational Health Monitor.

Evidence of learning: Working measurement display + limitations clearly stated.

After class: Improve the display and add logging.

Objective: Build an environmental monitoring prototype and interpret sensor data cautiously.

Key concepts: air/environment sensors, data, alerts

Materials: MQ-series gas sensor or environmental sensors, ESP32/Arduino, display

Hands-on plan

1. Warm-up and observe sensor behavior as required by the module.
2. Read values and label them as sensor readings, not direct safety certification.
3. Create a warning threshold for demonstration.
4. Display/log readings.
5. Test different conditions safely.
6. Discuss calibration and sensor limitations.

Student challenge: Environmental Monitor.

Evidence of learning: Live reading + alert logic + limitations.

After class: Add a second environmental variable.

Objective: Integrate multiple real-world functions into one coherent system.

Key concepts: multi-sensor integration, IoT, alerts

Materials: ESP32/Arduino, selected sensors, outputs, dashboard

Hands-on plan

1. Select two or three sensors.
2. Create a system block diagram.
3. Implement each input separately.
4. Integrate decision logic.
5. Add local/web status.
6. Test failure cases.
7. Present the system architecture.

Student challenge: Smart Real-World System.

Evidence of learning: Multiple inputs and outputs operate as one system.

After class: Add one meaningful improvement.

Objective: Choose a meaningful problem suitable for a student-built technology solution.

Key concepts: problem definition, users, observation, constraints

Materials: notebook, design sheet

Hands-on plan

1. Observe school/home/community situations.
2. List at least five problems.
3. Choose one with a clear user.
4. Write problem statement.
5. Define constraints such as cost, size, safety and available parts.
6. Write success criteria.

Student challenge: Problem Definition Sheet.

Evidence of learning: Clear problem, user, constraints and success criteria.

After class: Interview/ask one user for feedback if appropriate.

Objective: Generate multiple solutions and select one.

Key concepts: brainstorming, alternatives, block diagrams

Materials: paper/whiteboard, component list

Hands-on plan

1. Create at least five solution ideas.
2. Compare them against success criteria.
3. Choose one concept.
4. Draw input→processing→output block diagram.
5. List sensors, controllers, actuators and communication needs.
6. Identify the highest-risk part.

Student challenge: Innovation Concept Review.

Evidence of learning: Five ideas + selected architecture.

After class: Revise one part after mentor review.

Objective: Convert an idea into an executable engineering plan.

Key concepts: BOM, milestones, prototype, testing plan

Materials: project materials, planning sheet

Hands-on plan

1. Prepare bill of materials.
2. Create wiring/system diagram.
3. Break build into milestones.
4. Plan software modules.
5. Write five test cases.
6. Identify safety and failure conditions.

Student challenge: Prototype Plan.

Evidence of learning: Complete BOM, architecture, milestones and tests.

After class: Prepare parts and code skeleton.

Objective: Build the first meaningful prototype and learn from testing.

Key concepts: prototype construction, testing, feedback

Materials: student-selected components

Hands-on plan

1. Build the simplest working subsystem.
2. Test each module separately.
3. Integrate modules.
4. Run planned test cases.
5. Record failures.
6. Make one controlled improvement.
7. Review with mentor.

Student challenge: Innovation Prototype.

Evidence of learning: Core function demonstrated and test evidence recorded.

After class: Prepare a before/after comparison.

Objective: Improve reliability and create professional project documentation.

Key concepts: iteration, reliability, documentation

Materials: prototype, documentation tools

Hands-on plan

1. Review failures and test results.
2. Prioritize the most important improvement.
3. Implement and retest.
4. Update circuit/system diagram.
5. Update code structure/comments.
6. Document limitations and future scope.

Student challenge: Innovation Review Build.

Evidence of learning: Improved prototype + updated documentation.

After class: Prepare capstone build schedule.

Objective: Finalize a complete real-world project plan.

Key concepts: project scope, integration, feasibility

Materials: student-selected components, proposal template

Hands-on plan

1. Define problem and user.
2. Define objective and measurable success criteria.
3. Select technology stack: Arduino/ESP32/Python/AI as appropriate.
4. Create architecture and BOM.
5. Plan milestones for build, test and presentation.
6. Mentor approves scope.

Student challenge: Capstone Proposal.

Evidence of learning: Approved problem, architecture, BOM and milestones.

After class: Prepare hardware/software workspace.

Objective: Build the main system through modular testing.

Key concepts: hardware/software integration

Materials: approved capstone hardware

Hands-on plan

1. Build hardware subsystem 1.
2. Test it independently.
3. Build subsystem 2.
4. Integrate software.
5. Connect communication/AI if part of scope.
6. Record issues and fixes.
7. End with a working core function.

Student challenge: Capstone Build Sprint.

Evidence of learning: Core system works end-to-end.

After class: List remaining bugs/features.

Objective: Use structured testing to improve the final prototype.

Key concepts: test cases, fault isolation, reliability

Materials: capstone, test sheet

Hands-on plan

1. Run planned test cases.
2. Record expected vs actual results.
3. Identify highest-priority failure.
4. Change one variable at a time.
5. Retest.
6. Optimize reliability, response or presentation.
7. Freeze the main build before final documentation.

Student challenge: Reliability Test.

Evidence of learning: Student can show test evidence and explain at least one debugging decision.

After class: Prepare final demo checklist.

Objective: Prepare a clear technical explanation of the project.

Key concepts: documentation, architecture, results, limitations

Materials: capstone, laptop/poster, report template

Hands-on plan

1. Write problem and motivation.
2. Explain system architecture.
3. Add circuit/block diagram.
4. Explain code/AI/IoT contribution.
5. Show test results.
6. Document limitations and future work.
7. Practice a 3–5 minute technical presentation.

Student challenge: Project Demo Rehearsal.

Evidence of learning: Complete report + practiced demonstration.

After class: Final rehearsal.

Objective: Demonstrate the complete project and communicate the engineering process.

Key concepts: demonstration, viva, reflection, portfolio

Materials: completed project, report, demo setup

Hands-on plan

1. Present problem and solution.
2. Demonstrate the system.
3. Explain key components and software.
4. Answer technical questions.
5. Explain one failure and improvement.
6. Discuss future scope.
7. Complete final reflection and portfolio entry.

Student challenge: Final Capstone Showcase.

Evidence of learning: Working demonstration or well-documented prototype + technical explanation.

After class: Plan next advanced specialization.