AI Smart Plate Pal: Designing Solutions with Computational Thinking
AI Smart Plate Pal: Designing Solutions with Computational Thinking
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In this AI × Computational Thinking × Design Thinking unit, students investigated challenges within their school cafeteria and identified two key needs: reducing staff fatigue and encouraging students to avoid food waste. Using empathy, observation, algorithmic thinking and AI-assisted ideation, learners designed “AI Smart Plate Pal,” a digital companion that reminds students to finish their meals, tracks leftovers and helps cafeteria staff manage routines more efficiently.
In this AI × Computational Thinking × Design Thinking unit, students investigated challenges within their school cafeteria and identified two key needs: reducing staff fatigue and encouraging students to avoid food waste. Using empathy, observation, algorithmic thinking and AI-assisted ideation, learners designed “AI Smart Plate Pal,” a digital companion that reminds students to finish their meals, tracks leftovers and helps cafeteria staff manage routines more efficiently.
《AI 小盤友：用運算思維設計有同理心的校園創新》
Central Idea
Central Idea
We use computational thinking and AI tools to design solutions that make our community more sustainable and caring.
Lines of Inquiry
Lines of Inquiry
How problems in our daily environment can be observed and understood
How computational thinking supports the design of meaningful solutions
How AI can encourage positive habits and reduce waste in our community
Key Concepts
Key Concepts
Causation（Why is it like this?）
Responsibility（What is our responsibility?）
Change（How can things improve?）
Learning Journey
Learning Journey
Empathy & problem discovery
Learners observed lunchtime routines and interviewed cafeteria staff, identifying repetitive tasks and fatigue. They also noted how leftover food contributes to waste and unhealthy eating habits.
Computational thinking
Learners decomposed the problem into two parts: supporting staff and reducing student food waste. They created flowcharts to represent detection processes, reminders, and feedback loops.
AI-assisted ideation
Learners used AI tools to generate interface ideas, explore detection features, and refine the “smart reminder” functions that encourage students to finish their lunch when possible.
Prototyping & interface design
Learners built prototypes for leftover detection, friendly alerts to support healthier, more sustainable lunchtime habits.
Presentation & reflection
Learners explained how empathy, computational thinking and AI helped them design a solution with real impact—supporting cafeteria staff and promoting responsible, healthy eating among students.
The Creation of AI Smart Plate Pal
The Creation of AI Smart Plate Pal
Using AI image recognition, Micro:bit programming, KOI AI camera and LEGO engineering, P5 students designed “Smart Plate Pal” to reduce food waste and assist canteen staff through an automated plate-sorting system.
Tools & Skills Used in This Unit
Tools & Skills Used in This Unit
Digital Tools Used
AI Tools
KOI AI Camera — Used to detect leftover vs clean plates and to train a simple machine learning model based on cafeteria images.
AI training interface — Students captured example images and trained the model to classify different types of plates.
Programming & Hardware
Micro:bit — Programmed decision logic (if–else, reminders, sorting actions).
MakeCode — Used to code detection processes, feedback loops, and reminder triggers.
Engineering & Prototyping
LEGO engineering kits — Built structural prototypes, plate-sorting mechanisms, and sensor positions.
Design Thinking Tools
Flowcharts — Represented algorithm steps, detection cycles, and response actions.
Sketching & ideation templates — Used for early-stage concept development.
Documentation & Presentation
iPad / digital photo tools — Used to document cafeteria observations and prototype testing.
YouTube — Presented final functional demonstrations of “AI Smart Plate Pal.”
Creative & Technical Skills Developed
Computational Thinking
Decomposition — Breaking complex cafeteria issues into smaller, solvable parts.
Algorithmic thinking — Designing step-by-step decision processes for plate detection.
Flowcharting — Visualizing detection logic, reminders, and automated decision paths.
AI Literacy
Dataset collection — Capturing plate images for training.
Model training & iteration — Adjusting datasets to improve accuracy.
Real-time classification — Using KOI camera to recognize plates during prototype testing.
Understanding limitations of AI — Noticing misclassifications and refining the data sample.
Engineering & Prototyping
Structural building with LEGO — Creating stable frames for sensors and plate slots.
Sensor integration — Aligning the AI camera with plate entry points.
Interface prototyping — Designing alerts and reminder mechanisms for users.
Design Thinking
Empathy & problem discovery — Understanding staff routines and student habits.
Ideation (AI-assisted) — Generating and refining concepts that reduce food waste.
Iteration & refinement — Improving prototypes based on user feedback.
Communication
Explaining CT strategies — Presenting how algorithmic thinking guided decisions.
Reflecting on responsible use of AI — Connecting the project to ethical and sustainable school practices.
Showcasing impact — Demonstrating how the solution supports cafeteria staff and promotes healthy habits.
Student Evidence of Learning
Student Evidence of Learning
4th Hong Kong Youth STEAM Competition & Exhibition 2024 ("From AI to SI")
4th Hong Kong Youth STEAM Competition & Exhibition 2024 ("From AI to SI")
Third Prize AI innovation project created by P5 students using AI image recognition, Micro:bit programming, and LEGO engineering to reduce food waste and assist cafeteria staff through an automated plate-sorting system. Their project reflects careful observation, empathy, and a genuine desire to support the staff through meaningful problem-solving.
2024第四屆全港青年 STEAM 比賽暨展覽(由 AI 到 SI) ——三等獎
Third Prize – AI Smart Plate Pal Demonstration
Third Prize – AI Smart Plate Pal Demonstration
(Social Intelligence Innovation Category)
(Social Intelligence Innovation Category)
Students programmed the Micro:bit and connected the KOI AI camera to teach it how to recognize different plates—plates with leftovers, clean plates, or when no plate is detected. These learning results were saved as a machine learning model. Later, the KOI AI camera can read this model and identify new plates in real time. Then decide which action to take.
Assessment & Reflection
Assessment & Reflection
Assessment emphasized CT application, design process, and responsible use of AI. Formative tasks included algorithm drafts, flowcharts, and prototype iterations. Peer critique supported refinement. Summative assessment evaluated problem insight, effectiveness of CT strategies, clarity of design, and the potential impact on sustainability.
Teacher Role
Teacher Role
Facilitated CT strategies, guided design thinking, supported AI ideation tools, and encouraged responsible innovation for real-life school issues.
All students' faces and identifying information have been removed in accordance with child protection and privacy guidelines.
All students' faces and identifying information have been removed in accordance with child protection and privacy guidelines.
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