In AI, scaffolding is a training technique that incrementally increases the complexity of the tasks presented to an AI model. This approach allows the AI to first master simpler tasks before progressing to more challenging ones, mimicking the way humans learn. By doing so, scaffolding helps to ensure a solid foundational understanding and enhances the model's ability to generalize knowledge to more complex situations. It is particularly useful in reinforcement learning and developmental robotics, where the AI interacts with dynamic environments. Through structured stages, the AI can develop robust decision-making skills and adaptability.
The concept of scaffolding in the context of AI emerged in the late 20th century, inspired by educational psychology theories from the 1970s, such as Vygotsky's Zone of Proximal Development. It gained popularity in AI research in the 2000s, particularly within developmental robotics and reinforcement learning communities, as researchers sought more effective ways to train increasingly complex systems.
Key contributors to the development of scaffolding in AI include researchers in developmental psychology like Lev Vygotsky, whose educational theories provided the foundation for scaffolding. In AI, pioneers such as Andrew Ng and his work on reinforcement learning, and roboticists like Jean Piaget, who applied developmental principles to artificial agents, have been instrumental in advancing scaffolding methodologies.