#AI@Work: Learning to Think
AIEd brings AI into the learning environment. It uses AI to represent knowledge, reason, explain, plan, for machine learning, and with natural language. This is where AIEd was. This is what thrived. AI educational assets did not depend on excellent modeling. Their use depended on the organization’s context for use. As acceptance and deployment of AIEd continues, education will broaden the horizons. It will use AI to support higher level learning. AI is dynamic by nature. This distinguishes it from other technologies.
Intelligent Tutor Systems (ITS) are valuable for learning well-defined knowledge and skills. New ITS will move away from closed domains and tutoring tasks. Higher order learning requires arguing, describing and explaining. It requires predicting, analyzing, assessing and synthesizing. Traditional ITS provides support of knowing and doing. The learning environment plays an important part in connecting higher levels of learning and AI. Learning science and research supports the fact that learning does not take place in isolation. Social activities are critical for developing thinking. Interactions are a critical part of any learning environment (Andriessen & Sandberg, 1999).
The ideas on knowledge are shifting. What AI can support and how it gets supported are intertwined with pedagogy, linguistics and social contexts. The inclusion of “Big Data” and the restructuring of data from file cabinets to data lakes has opened up possibilities for AIEd. Access to the data is much more flexible. Using the data is easier. Data for AI and learning is not static. It is everchanging and growing. The evolution of AI requires ongoing research in both the Education and AI fields. The view of what knowledge is stands intricately woven into the perceptions of how to teach. Facts and opinions are distinctly different. Knowledge is either universally objective and factual or local, subjective and personal. That knowledge is transmitted by telling characterizes most educational systems. It is top down, authoritarian and judgmental. The wisdom of the expert is used as the “bar” against which the learner is measured. It is rather outdated with AI in the house. There is no question that huge gaps exist between AI, the educational community and learning sciences and that even bigger opportunities exist.
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