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G’day! If you’re keen to learn about AI, you might have already had a crack at Teachable Machine or Sci-kit AI (scikit-learn). Both are brilliant in their own way—Teachable Machine offers a slick, user-friendly interface, while Sci-kit AI lets you build models with just a few lines of code. But here’s the thing: neither tool shows you what’s happening under the hood. That’s where MyComputerBrain™ (MCB) steps in.
Teachable Machine: Great for Getting Started, But What’s Behind It?
Teachable Machine is a fantastic way to dip your toes into AI. Its interactive design makes it easy for anyone to train models for image, sound, and even pose recognition. However, while its friendly interface is a real winner for beginners, it tends to hide the inner workings of the model. You get the output without really seeing how the AI makes its decisions—a bit like watching a magic trick without knowing the secret.
Sci-kit AI: Quick, Simple, and a Bit of a Black Box
On the flip side, Sci-kit AI is excellent if you’re after quick results with minimal fuss. Its code-driven approach means you can build and test models in no time. But just like Teachable Machine, it often leaves you wondering: what exactly is happening inside those few lines of code? The internal processes—the nitty-gritty of data handling, feature selection, and algorithm tuning—remain largely hidden from view.
MyComputerBrain: Let’s Open That Black Box!
MyComputerBrain™ is an Aussie-made educational platform designed by the Digital Technologies Institute. It’s built to demystify AI by taking you step-by-step through the inner workings of neural networks. Here’s how MCB complements the other tools:
Interactive, Hands-On Learning: MCB doesn’t just let you use AI—it shows you how it ticks. The platform offers interactive experiments that break down complex AI concepts into manageable lessons. You can watch in real time as data flows through a neural network, seeing every decision and adjustment as it happens.
Embedded Safe AI: One of the real beauties of MCB is its in-house developed safe AI, which runs right in your browser. This isn’t just about safety and data privacy; it’s about giving you a clear window into how AI operates. You get to see how a neural network learns and adapts, which is something you won’t get with Teachable Machine or Sci-kit AI.
Guided Learning with Immediate Feedback: MCB is all about making AI accessible. It breaks down learning tasks into guided steps, providing immediate feedback along the way. This approach helps you build a solid understanding of AI’s internal processes, bridging the gap between using AI tools and truly understanding them.
Complementary to Existing Tools: While Teachable Machine and Sci-kit AI are great for getting started, they often leave you in the dark about what’s really happening under the hood. MCB fills that gap, allowing students to explore the mechanics of AI. It’s like having a behind-the-scenes tour of an AI’s brain!
Let’s look at a typical scenario in which an AI classifies emoji into happy and sad. Here is the experiment in Teachable Machine, where you simply upload or capture images for “happy” and “sad,” train the model with a click, and instantly see the results. It’s straightforward and great for quick demos, but you don’t get to see how the AI is making its decisions. By contrast, MyComputerBrain shows you every layer of the neural network—each neuron, each connection—and how they interact to produce the classification. You’re not just getting a final “happy” or “sad” outcome; you’re actually watching the AI learn, step by step, which helps demystify that so-called “black box.”
Here’s another scenario from the field of STEM: using AI to find the best-fit curve for data from a chemistry class. In this case, a student measured reaction times at different temperatures—30, 40, 50, 60, and 70 degrees Celsius—and plotted the results. With Sci-kit AI, you can whip up a few lines of code, train a model, and get a neat curve over your data points. It’s quick, but again, you don’t really see how the algorithm is tuning itself. MyComputerBrain, however, peels back the layers by showing how each neuron in the network adjusts its weights in real time to match the measured data. It’s like getting a backstage pass to watch the AI “learn” the relationship between temperature and reaction time, which helps students truly grasp what’s happening behind that best-fit curve.
Wrapping It Up
In a nutshell, if you want to get a deeper, more transparent insight into how AI really works, MyComputerBrain™ is the perfect mate to Teachable Machine and Sci-kit AI. By opening up the black box, MCB not only enhances your understanding but also inspires confidence in your ability to tackle more advanced AI challenges down the track.
Today's students are conditioned by rapid feedback loops and continuous digital engagement through social media, online gaming, and instant messaging. This continuous stimulation significantly influences their cognitive patterns, creating a preference for immediate responses and shorter, intense periods of activity.
Teachers often respond to this trend by attempting to slow down the instructional pace, assuming students struggle to maintain attention or absorb detailed content. While well-intentioned, this approach may inadvertently result in further disengagement by students. For example, assignments or projects spread over several weeks can lead students to repeatedly lose focus, requiring them to reacquaint themselves with tasks after each interruption. Frequent context switching results in lower-intensity work and less effective learning.
Instead of slowing down, I suggest leveraging AI to create faster, shorter, and highly intense learning cycles that align with students' neurological conditioning.
The Potential of 15-Minute AI-Enhanced Learning Cycles
Consider a scenario—such as a student working on an assignment, project, or even preparing for an exam. Traditionally, this work might occur intermittently over extended periods, causing repeated cognitive disruptions each time students re-engage.
Now, imagine the following AI-enhanced scenario:
The student uploads their task description and marking criteria into an AI-supported platform.
They complete an initial draft or attempt and submit it to the AI tool.
Within seconds, AI provides specific, criteria-aligned feedback.
The student immediately incorporates the feedback, resubmitting their improved effort, and repeating this iterative process every 15 minutes.
From a neuroscientific viewpoint, this rapid, iterative feedback process stimulates continuous dopamine release—the neurotransmitter responsible for motivation, pleasure, and reinforcement. Such consistent dopamine-driven engagement maintains immediate attention and significantly enhances long-term memory consolidation through repeated practice and incremental improvements.
Reducing Context Switching and Enhancing Long-Term Memory
Traditional teaching methods, which spread learning activities over weeks or months, can inadvertently fragment student attention, resulting in frequent cognitive disruptions. Short, focused learning cycles minimise these disruptions, maintaining sustained engagement. Additionally, repeated, iterative feedback from AI tools can help strengthen neural pathways, promoting long-term retention and deeper understanding.
AI as a Supportive Tool, Not a Replacement
In reality, teachers face practical constraints in providing continuous personalised feedback. AI can support teachers by providing immediate, consistent feedback, enabling educators to dedicate their energy towards individual student needs, strategic instructional support, and deeper cognitive challenges.
Neuroscientific Benefits of Accelerating Learning with AI:
Dopamine-driven engagement: Rapid cycles activate reward centres, maintaining motivation and sustained attention.
Focused, intense learning sessions: Encouraging deeper cognitive processing and retention.
Minimised cognitive disruptions: Maintaining continuous momentum enhances productivity and cognitive efficiency.
Improved memory consolidation: Frequent iterative practice and feedback reinforce neural connections.
Supporting Evidence from Learning Theory
Educational psychology and cognitive neuroscience support the effectiveness of this rapid-cycle, iterative approach. Cognitive Load Theory highlights that short, focused learning cycles reduce extraneous cognitive load by minimising context switching, leading to improved cognitive processing. Similarly, Feedback Intervention Theory demonstrates that immediate, specific feedback significantly enhances performance and retention. Repeated iterative practice aligns with Ebbinghaus' Forgetting Curve, indicating frequent revisits to material at short intervals can dramatically enhance long-term retention.
I believe integrating AI-driven, short learning cycles into education effectively aligns with students' neuro-cognitive profiles, enhancing immediate engagement and long-term retention.
What are your thoughts? Could shorter, AI-supported learning cycles better suit how students' brains are wired today?
This post was first published on LinkedIn on 21/3/2025
Welcome to our March newsletter with all the latest things that are happening at the Digital Technologies Institute this month:
Teacher Professional Development
Online webinars
Teacher PD continues with an exciting lineup of sessions. Recordings of these sessions are typically available within 24 hours of the webinar and are available to registered participants.
When | What | Domain |
March 11 | Artificial Intelligence | |
March 12 | Digital Literacty, Artificial Intelligence | |
March 18 | Artificial Intelligence | |
March 25 | Artificial Intelligence | |
March 26 | Digital Literacy |
Self-directed learning
In addition to the online webinars, the DTI Classroom hosts an interactive AI workshop containing everything teachers need to get started with AI teaching. Have a look.
Student Learning
MyComputerBrain
MyComputerBrain is our online learning system that allows students to explore the exciting world of DIgital Technologies. Our latest release is a Generative AI Course that explains how GenAI works and its possibilities and limitations. Teachers can purchase student accounts for as little as $5. Explore MCB and check out the Getting Started guide. You can also read about this course in our blog post.
Video Explainers
Scroll down to the bottom of MyComputerBrain. You will find four incredible AI explainer videos beautifully crafted for your students. If you then head over to our main website, there is an entire section of further AI and digital technologies videos to support your teaching of Digital Tech.
The B4 Computing Kits: A Popular Classroom Resource for Teaching Computing Foundations
A strong understanding of computing fundamentals is essential for students, and the Australian Curriculum: Digital Technologies is designed to build this foundation. The B4 is a widely used resource in Australian classrooms, helping students explore digital systems, data representation, and algorithms in an engaging and accessible way. It also supports the teaching of more challenging concepts such as binary numbers, variables, and algorithm development. Did you know that the B4 comes with physical variables that students can hold in their hands?
Join the many educators already using the B4 to bring computing concepts to life in their classrooms! Explore the resource today and see how it can enhance your students’ learning experience.
I hope you have found something you like. Let us know if there is anything we can help you with.
Warm Regards,
Dr. Karsten Schulz
