In many universities today, IoT projects are built on platforms that prioritise speed and convenience. Students choose them because they are familiar, widely used, supported by abundant tutorials, and often come with ready-made mobile applications.
This choice is understandable. Academic timelines are tight, assessment criteria focus on working demonstrations, and familiarity reduces risk.
But this raises an important question for educators and industry alike.
Are we training students to complete projects, or are we preparing them to build real-world IoT systems?
The Risk of Oversimplified Learning
Most student IoT projects successfully demonstrate data collection and visualisation. Sensors connect, dashboards display readings, and results are presented confidently.
Yet many of these projects bypass the deeper mechanics of IoT.
Students often do not fully experience how data moves from device to cloud, how connectivity behaves under real constraints, or how failures propagate across the system. When issues occur, they are difficult to diagnose because the underlying layers are hidden.
In practice, IoT systems rarely operate in ideal conditions. Networks drop. Devices fail. Power becomes limited. Security concerns emerge.
Without exposure to these realities, graduates may struggle when transitioning from academic projects to industry environments.
Why Platform Depth Matters
IoT is not a single skill. It is a combination of multiple technology layers working together.
A complete IoT learning experience should include:
- Hardware and sensors
- Firmware and device configuration
- Connectivity and protocols such as MQTT, CoAP, or REST
- Cloud ingestion and device management
- Dashboards, rules, and alerts
- Data analysis and decision logic
- End-to-end security considerations
Platforms that expose these layers help students understand not only how systems work, but also why they behave differently when conditions change.
This understanding builds confidence, adaptability, and long-term competence.
Platform-as-a-Service and Real Learning
Favoriot was designed as a platform rather than a simplified application.
It requires students to configure devices, manage data flows, and troubleshoot across the full pipeline. While this approach requires more effort initially, it better mirrors real-world IoT deployments.
Students learn how to trace data, identify failure points, and make informed design decisions. These are the skills employers value when systems do not behave as expected.
Ease of use is important. Depth of understanding is essential.
Preparing Students for an AIoT Future
IoT is evolving quickly. Artificial intelligence is increasingly embedded in data pipelines. Edge intelligence is becoming common. Decisions are being made closer to devices rather than exclusively in the cloud.
Graduates entering this environment must understand how data is collected, processed, analysed, and acted upon. Superficial exposure is no longer sufficient.
Education must evolve to reflect this reality.
A Call to Action
For students
Choose platforms that challenge you to understand the full IoT flow. Learn how systems behave when things go wrong, not only when they work.
For lecturers and universities
Review whether current IoT teaching focuses on demonstrations or on system understanding. Consider platforms that expose real-world complexity in a structured, well-supported way.
For the IoT community
Share projects, document lessons learned, and contribute to local ecosystems. Community growth begins with visible examples.
IoT education should build builders, not just presenters.
Those willing to take the harder path today will be better prepared for the systems they will face tomorrow.
If you are interested in adopting Favoriot for teaching, research, or student projects, or in contributing your project stories to IoT World, we invite you to connect with us and be part of the growing ecosystem.
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