Research suggests that expert understanding is characterized by coherent mental representations featuring a high level of connectedness.
Artificial intelligence (AI) is a trend that is currently leading to controversial discussions.
Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems.
The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness.
Artificial intelligence (AI) is becoming increasingly integrated in user-facing technology, but public understanding of these technologies is often limited.
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing.
In this brief introductory chapter, we present a bird’s-eye view of the conceptual landscape of situated cognition as seen from each of the three angles noted previously: embodiment, embedding, and extension.