As part of ongoing efforts to strengthen digital pedagogy and future-ready STEM education, a Train-the-Trainer (TTT) Teachers Training Programme was successfully conducted for teachers from across Pahang, focusing on Arduino programming using ESP platforms and Edge Impulse for image classification.
The programme was designed to equip teachers with hands-on experience in digital making while introducing fundamental concepts of machine learning, particularly in the context of computer vision and image classification.
Programme Objectives
The main objectives of this TTT programme were to:
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- Familiarise teachers with digital making concepts using Arduino and ESP-based microcontrollers
- Provide foundational understanding of machine learning, specifically image classification
- Introduce Edge Impulse as an accessible platform for developing embedded AI applications
- Enable teachers to confidently integrate AI, IoT and embedded systems into classroom teaching and student projects
- Support the development of future-ready educators aligned with Industry 4.0 and AI-driven education
Hands-On Learning with Arduino and ESP
During the training, teachers were introduced to Arduino programming on ESP platforms (such as ESP32), covering:
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- Basic Arduino IDE setup and programming workflow
- Interfacing ESP boards with peripherals (camera modules, sensors)
- Understanding microcontroller capabilities for edge computing
- Deploying lightweight AI models on embedded devices
This hands-on approach allowed participants to move beyond theory and experience how hardware, software and AI intersect in real-world applications.
Introduction to Edge Impulse and Image Classification
A key highlight of the programme was the introduction to Edge Impulse, a powerful yet beginner-friendly platform for embedded machine learning.
Teachers learned:
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- The fundamentals of machine learning and image classification
- How to collect image datasets using ESP camera modules
- Data labelling and training simple image classification models
- Deploying trained models directly onto ESP devices for on-device inference (edge AI)
Through guided activities, participants successfully implemented basic image classification tasks, gaining confidence in applying AI concepts without requiring advanced programming or mathematical backgrounds.
Building Confidence in Teaching AI and Digital Making
Beyond technical skills, the programme emphasised pedagogical readiness. Discussions and activities focused on:
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- Translating complex AI concepts into classroom-friendly learning activities
- Designing project-based learning (PBL) tasks using Arduino and AI
- Encouraging student creativity, problem-solving and ethical awareness in AI use
- Aligning AI and digital making activities with school STEM curricula
Teachers shared ideas on how these technologies could be adapted for subjects such as Asas Sains Komputer, Reka Bentuk Teknologi, STEM projects and robotics clubs.
Impact and Way Forward
This TTT programme marked an important step in empowering educators in Pahang with practical skills in embedded systems, AI and digital innovation. By strengthening teachers’ confidence and competency, the programme supports the broader goal of cultivating AI-literate students who are prepared for future technological challenges.
Moving forward, participants are expected to:
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- Implement Arduino- and AI-based projects in their schools
- Act as multipliers, training fellow teachers and students
- Contribute to a growing ecosystem of responsible, ethical and sustainable AI education
Conclusion
The Arduino–ESP–Edge Impulse TTT programme demonstrates that machine learning and AI are no longer confined to advanced laboratories. With the right tools and training, educators can bring AI-powered digital making into everyday classrooms—sparking curiosity, innovation and future-ready skills among students.
This initiative reinforces the commitment to strengthening STEM and AI education at the grassroots level, ensuring teachers remain at the heart of Malaysia’s digital and educational transformation.












































































































