Artificial Neural Networks

Artificial neural networks (ANNs) are a key technology behind many of the digital tools we use today, including those in education, search engines, and AI systems. Although artificial neural networks are often described as complex in academic and public discussions, the basic idea behind them is quite simple (Goodfellow, Bengio and Courville, 2016). Neural networks are designed to recognise patterns in data by learning from examples, instead of following fixed rules. Understanding how they work helps demystify AI and makes it easier to reflect on how it may support learning in the future.

Artificial neural networks are loosely inspired by the structure of the human brain. They are made up of layers of connected “nodes” (or artificial neurons) where each connection has a weight that changes as the system learns. Information moves through the network from input to output. The system improves by adjusting its internal connections as it learns from data (Goodfellow, Bengio and Courville, 2016).

Figure 1: An AI-generated basic structure of an artificial neural network

Figure 1 shows a simple representation of a neural network. Each node receives information, processes it, and passes it on. During training, the network compares its output to the correct answer and adjusts its connections to reduce error. This process ia often referred to as backpropagation - it allows the network to improve over time (LeCun, Bengio and Hinton, 2015). What is important here is that learning emerges from patterns in data, not from explicit instructions.

Neural Networks in Everyday Learning Tools

Image 1: AI-supported learning in a university classroom, with students engaging independently while working alongside peers.

In practice, neural networks are already used in many learning tools, such as recommendation systems, speech recognition, automated feedback, and adaptive learning platforms. For example, when a system suggests a resource based on previous activity or recognises spoken input in a learning app, a neural network is often involved. These systems work best when they are trained on large and varied datasets, which allows them to recognise subtle patterns and respond flexibly (Goodfellow, Bengio and Courville, 2016).

Supporting Reflection & Feedback

For my own learning, neural networks are more useful for supporting reflection and feedback than for replacing human judgement. Neural-network-based systems can identify patterns in how learners interact with content, such as repeated misunderstandings or preferred ways of engaging with material. These kinds of patterns can be difficult for learners to notice on their own, particularly over longer periods of time (Luckin et al., 2016). In this sense, neural networks support learning by making patterns visible, rather than by making decisions on behalf of the learner (Goodfellow, Bengio and Courville, 2016).

Figure 2: An AI-supported feedback loop showing how learner interaction informs reflection and adaptive support.

At the same time, neural networks have limitations. They can reflect bias or make mistakes because they learn from data, especially if they are not carefully monitored (LeCun, Bengio and Hinton, 2015). For this reason, human judgement is still essential. Understanding how they work helps me use AI more critically.

Overall, artificial neural networks are best understood as pattern-learning systems that can support, but not replace, human learning. By understanding how these systems work and where their limits are, learners can use AI more thoughtfully and effectively (Luckin et al., 2016).

Reference List

Goodfellow, I, Bengio, Y and Courville, A (2016) Deep Learning. Cambridge, MA: MIT Press.

LeCun, Y, Bengio, Y and Hinton, G (2015) ‘Deep learning’, Nature, 521(7553), pp. 436–444.

Luckin, R, Holmes, W, Griffiths, M and Forcier, LB (2016) Intelligence Unleashed: An Argument for AI in Education. London: Pearson.

AI Acklowedge Statement

AI tools were used to support the development of this blog in a transparent and limited way. I used prompts such as “What is an artificial neural network and how does it work?”, “How can neural networks support reflection and feedback in learning?”, and “How might neural networks support my learning in the future?” to help explore and clarify ideas. I also used prompts to help refine sentence clarity, structure the blog, and support reflective thinking.

In addition, AI was used to generate images and diagrams based on my own prompts, including requests such as “a simple diagram of an artificial neural network with input, hidden, and output layers” and “a visual feedback loop showing how AI supports reflection and adaptive learning”. All prompts were designed by me to match the concepts discussed in the blog.

All ideas, interpretations, and conclusions are my own. I refined all content, and made the final decisions about presentation. The blog represents my independent learning and academic judgement.