Artificial Intelligence (AI) is rapidly transforming various sectors, including education. As educators, understanding the basics of how AI learns can help you better integrate these technologies into your teaching methods and enhance student learning experiences. In this blog post, we will delve into the different training methods AI uses, explained in a way that is accessible to those without a technical background.
AI models learn through various methods, adapting to different data and scenarios. These methods can be broadly classified into several categories: supervised training, semi-supervised training, self-supervised training, unsupervised training, reinforcement learning, RLHF (Reinforcement Learning from Human Feedback), active training, and meta-training. Each of these methods has unique characteristics and applications.
Unsupervised Training
Unsupervised Training involves training the AI on unlabelled data to find patterns and structures within the data. Unlike supervised training, there are no known answers provided during training.
Application in Education: Unsupervised training can be used to personalise learning experiences. By analysing student performance data, the AI can identify patterns and suggest tailored training paths that address individual strengths and weaknesses.
Self-Supervised Training
Self-Supervised Training is a form of unsupervised training where the AI generates its own labels from the input data. Typically, it involves teaching the model to predict part of the input data from other parts of the same data.
Application in Education: This method can be used to develop AI systems that enhance language training. For instance, an AI could be trained to predict missing words in sentences, helping students improve their vocabulary and comprehension skills.
Semi-Supervised Training
Semi-Supervised Training combines elements of both supervised and unsupervised training. The model is trained on a small set of labelled data supplemented by a larger set of unlabelled data. This approach is particularly useful when acquiring labelled data is expensive or time-consuming.
Application in Education: This method can be used to improve AI-driven tutoring systems. By using a small amount of labelled data (e.g., correctly solved problems) and a larger set of unlabelled data (e.g., student attempts), the AI can better understand and assist students in solving similar problems.
Supervised Training
Supervised Training is one of the most common types of AI training. In this method, the model is trained on labelled data, which means the data comes with known answers. For example, if you are teaching an AI to recognise different animals, you would provide it with images of animals along with labels such as “cat,” “dog,” or “elephant.” The AI uses these labels to learn and make predictions on new, unseen data.
Application in Education: Supervised training can be used to develop AI tools that grade assignments or quizzes. By training the AI on a dataset of graded assignments, it can learn to provide consistent and accurate grading for new submissions.
Reinforcement Learning (RL)
Reinforcement Learning (RL) is an approach where models learn by trial and error, receiving rewards for correct actions. The AI aims to maximise the cumulative reward over time.
Application in Education: Reinforcement learning can be used to create interactive educational games that adapt to a student’s skill level. The AI can learn to provide challenges that are neither too easy nor too difficult, keeping students engaged and motivated.
RLHF (Reinforcement Learning from Human Feedback)
RLHF involves training AI models using human feedback to align outputs more closely with human values. This method ensures that the AI’s actions and decisions are more aligned with what humans consider appropriate or valuable.
Application in Education: This method can be used to develop AI systems that provide feedback on student essays or creative projects. By incorporating human feedback, the AI can offer more nuanced and contextually relevant suggestions.
Active Training
Active Training is a type of training where the AI can query a user (or another information source) to obtain the desired outputs at new data points. This approach is beneficial in scenarios where labelling new data is expensive or laborious.
Application in Education: Active training can be used to improve AI-driven assessment tools. The AI can ask teachers to label a few critical examples, which can then be used to enhance the model’s accuracy and efficiency in grading.
Meta Learning
Meta Learning, often called “learning to learn,” involves designing models that can learn new tasks with minimal data by identifying the common structure between different tasks.
Application in Education: Meta learning can be used to create adaptive learning platforms that quickly adjust to new subjects or topics. This ensures that students receive relevant and effective instruction, even as their learning needs evolve.
Deep Learning
Deep Learning is a subset of machine learning that uses artificial neural networks to model complex patterns in large datasets. Deep learning models are capable of learning from unstructured data such as images, audio, and text.
Application in Education: Deep learning can be used to develop AI-driven tools that analyse student performance data, identify learning gaps, and recommend personalised learning resources.
Transfer Learning
Transfer Learning involves training a model on one task and then applying it to a different but related task. This approach leverages the knowledge gained from the first task to improve performance on the second task.
Application in Education: Transfer learning can be used to develop AI systems that adapt to different educational contexts. For example, a model trained on one subject can be fine-tuned to provide support in another subject area.
Federated Learning
Federated Learning is a distributed machine learning approach where the model is trained across multiple devices or servers without exchanging raw data. Instead, the model is trained locally on each device, and only the model updates are shared with a central server.
Application in Education: Federated learning can be used to develop AI tools that respect student privacy while providing personalised learning experiences. By training models locally on student devices, educators can ensure data security and confidentiality.
Conclusion
Understanding these diverse AI training methods can help educators leverage AI tools more effectively in the classroom. By integrating AI-driven solutions, teachers can enhance their instructional methods, provide personalised learning experiences, and ultimately improve student outcomes. As AI continues to evolve, its potential applications in education will only expand, making it an invaluable asset for educators worldwide.
Stay tuned for more insights on how AI can revolutionise education and empower both teachers and students.