Artificial Neural Networks (ANNs) form the backbone of modern artificial intelligence. Teaching this subject effectively is not just about mathematical rigor but also about creating experiences that allow students to explore, experiment, and reflect. By designing engaging lab activities and viva sessions, educators can help students connect theory with practice, transforming abstract concepts into practical skills that are essential in today’s AI-driven world.

The Pedagogical Challenge

Teaching ANN is often challenging because it involves a blend of mathematics, computer programming, and conceptual abstraction. Students may struggle with the leap from perceptrons to multilayer neural networks, or from gradient descent theory to backpropagation coding. Traditional lectures, while essential, often leave gaps in intuition. To bridge this, labs must create hands-on opportunities where students can see neural networks in action, tweak parameters, and immediately observe outcomes.

Lab Experiments That Build Intuition

A good starting point is to design simple experiments that gradually build toward complexity. For example, students can first implement a perceptron classifier for linearly separable data, such as distinguishing between AND/OR logic gates. Once they grasp the basics of forward propagation, the next lab can extend to multi-layer perceptrons (MLPs) trained on datasets like MNIST handwritten digits or a simple healthcare dataset.

To reinforce backpropagation, instructors can encourage students to manually calculate weight updates for a tiny two-layer network before coding it. Later, moving to frameworks like TensorFlow or PyTorch, students can appreciate how high-level libraries automate these steps. Visualization exercises, such as plotting loss curves or decision boundaries, further deepen intuition.

Encouraging Critical Thinking in Viva

Lab experiments alone are not enough; viva questions ensure students internalize the “why” behind the “how.” Instead of asking only definitions, instructors can challenge students with conceptual and applied questions. For example, asking “What happens if we remove hidden layers?” forces them to recall the limitations of linear models. Questions like “Why does vanishing gradient occur in deep networks?” test their understanding of optimization challenges.

Additionally, scenario-based viva questions—such as “If your model is overfitting a small dataset, how would you address it?”—help students apply knowledge to real-world problems. This transforms the viva from rote recall into an opportunity for critical reasoning and application-oriented discussion.

Blending Theory with Modern Practice

While lab activities often focus on coding, it is important to connect these exercises with the broader AI ecosystem. Assigning students to train a small network on a real-world dataset, such as medical images or sentiment analysis, demonstrates the relevance of ANN concepts. At the same time, viva sessions can highlight cutting-edge topics like convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformer-based architectures, encouraging students to see ANN not as an isolated subject, but as the foundation of modern AI.

Conclusion

Teaching ANN through thoughtfully designed labs and viva sessions creates a holistic learning journey. Labs allow students to experiment, visualize, and code their way into understanding, while viva ensures they can explain, justify, and reason about neural networks at a conceptual level. Together, these methods move students from passive learners to active practitioners, equipping them with both the technical expertise and the critical thinking skills necessary for future AI challenges.

Teaching ANN: Lab Ideas & Viva