The core idea behind artificial neural networks is simple: by imitating the way the human brain works, machines can learn and think like humans.Artificial Neural Networks (ANNs) are a core technology behind many modern applications of artificial intelligence, including image recognition, language translation, recommendation systems, and intelligent tutoring tools. Inspired by the structure and function of the human brain, ANNs attempt to replicate how biological neurons process information, but in a mathematical and computational form. This blog explains what artificial neural networks are, what they look like, how they work, and how they may support learning in the future.

What is an artificial neural network?

An artificial neural network is a computational model inspired by biological nervous systems and consists of a large number of interconnected simple processing units (neurons). The connections between these neurons have adjustable weights, allowing the network to adjust its behavior by learning from the data. A typical neural network contains three basic layers: input layer (receives data), hidden layer (performs processing), and output layer (generates results). As the number of hidden layers increases, the network becomes “deeper”, which is where the term “deep learning” comes from – modern neural networks typically contain at least 4 layers, and even more.

Basic principles（What do Neural Networks look like, how do they work?）

Each connection of an artificial neural network has a weight, there are no connections between neurons in the same layer, and the output layer is usually a fully connected layer. The network learns by adjusting weights and biases, often using the backpropagation algorithm. Activation functions play an important role in neural networks, introducing nonlinear factors that enable the network to learn complex patterns.
Common activation functions include sigmoid and tanh. Distributed computing is a major feature of neural networks. Unlike traditional explicit programming, neural networks process information through the cooperative work of many neurons. This distributed architecture gives the neural network powerful pattern recognition and function approximation capabilities(Kanwisher et al., 2023).

What do Neural Networks look like?

A typical artificial neural network consists of three main types of layers:

1. Input Layer – Receives raw data, such as images, text, or numerical values.
2. Hidden Layers – Perform intermediate computations and extract features from the input data.
3. Output Layer – Produces the final result, such as a classification or prediction.

The simplest networks may have only one hidden layer, while deep neural networks (often referred to as deep learning models) can have dozens or even hundreds of hidden layers. Visually, neural networks are often represented as diagrams of circles (neurons) connected by arrows (weights).

How Do Artificial Neural Networks Work?

Neural networks operate through two main processes: forward propagation and training through backpropagation.

During forward propagation, input data passes through the network layer by layer. Each neuron computes a weighted sum of its inputs, applies an activation function (such as ReLU or sigmoid), and passes the result to the next layer. The output layer then generates a prediction.

Training occurs by comparing the network’s prediction with the correct answer using a loss function. The error is then propagated backward through the network using an algorithm called backpropagation. This process adjusts the weights to minimize error. Over many iterations and large datasets, the network gradually improves its performance

How will they support our learning or teaching in the future?

👩‍🏫Application direction and core functions have been improved.
a. Personalized learning path: Analyze academic data, dynamically recommend learning content, and customize exclusive paths.
b. Immersive learning experience creates real learning situations through technologies such as VR and AI Agent(Russell, S., & Norvig, P., 2021).
c. Education management and decision-making Multi-modal data tracks the learning process and provides a basis for teaching optimization.

👩‍🏫Personalized learning and intelligent tutoring
Artificial neural networks can construct accurate learner portraits by analyzing students’ learning behavior, knowledge mastery and other data, thereby achieving true individualized teaching(LeCun et al., 2015).

👩‍🏫Teaching model reform and classroom empowerment
Artificial intelligence technology is liberating teachers from repetitive labor and giving classrooms a more diverse form(Lok et al., 2021).
· Reduce the burden on teachers. For example: Practice in Xuhui District shows that AI has generated more than 20,000 academic analysis reports, and pilot school teachers’ lesson preparation efficiency has increased by 40%.
· Create immersive classrooms. For example, the No. 4 Experimental Primary School in Haidian District, Beijing, uses AI to evaluate compositions in Chinese classes and uses AI to generate student cartoon images in art classes.

👩‍🏫 Teacher role evolution and human-machine collaboration
Artificial intelligence is not meant to replace teachers, but to change the role of teachers from imparters of knowledge to guides, enablers and providers of emotions for learning(Shi, Y., 2022).
· From corrector to teaching researcher: With AI handling daily tasks, teachers can save a lot of time.
· “AI+famous teachers” dual-teacher model: The “double-teacher” model of collaboration between teachers and AI is currently being explored(Mutascu & Hegerty, 2023).

Conclusion

Artificial neural networks are a foundational technology in artificial intelligence, modeled loosely on the human brain and capable of learning complex patterns from data. Their layered structure, learning mechanisms, and adaptability make them powerful tools across many domains. As these systems continue to evolve, they are expected to play an increasingly important role in supporting education and lifelong learning.

Reference List:

Kanwisher, N., Khosla, M. & Dobs, K. (2023) ‘Using artificial neural networks to ask “why” questions of minds and brains’, Trends in neurosciences (Regular ed.), 46(3), pp. 240–254.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Lok, K.L., So, A., Opoku, A. & Song, H. (2021) ‘Globalized service providers’ perspective for facility management outsourcing relationships: Artificial neural networks’, Management decision, 59(1), pp. 134–151.

Mutascu, M. & Hegerty, S.W. (2023) ‘Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach’, Journal of economics and finance, 47(2), pp. 400–416.

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.

Shi, Y. (2022) ‘Application of Artificial Neural Network in College-Level Music Teaching Quality Evaluation’ Kuruva Lakshmanna (ed.), Wireless communications and mobile computing, 2022(1), .