Jrcb4 The Impact of Artificial Intelligence on Learning final

A general policy challenge, thus, is to increase among 
educators and policymakers awareness of AI technologies and their potential 
impact. One way of doing this is to participate in processes that generate images of 
future, develop concepts that can be used to describe them, and design scenarios and 
experiments where such imagined futures can be tested. A rather simple proposal for 
policy development, thus, is to launch explicitly future-oriented processes that generate 
understanding of the possibilities of the present. 
AI provides new means for research on learning, but it is also important to rethink the 
capabilities of AI systems using existing knowledge about learning.
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In particular, almost 
all currently developed AI systems rely on associative and behaviouristic models of 
learning. The long history of neural AI contains many attempts to go beyond these 
simple models of learning. Learning sciences could have much to offer to research 
on AI, and such mutual interaction would enable better understanding about 
how to use AI for learning and in educational settings, as well as in other 
domains of application. 
Data that is needed for machine learning is often highly personal. If it is used for 
assessing student performance, data security can become a key bottleneck in using AI, 
learning analytics, and educational data mining. As neural AI systems do not understand 
the data they process, it is also easy to forge data that fools the decision process.
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AI 
security is an important topic, but it is also challenging as neural AI systems typically use 
complex internal representations of data that are difficult or impossible to interpret. 
Because of this there is now considerable interest in creating “explainable AI.” The 
current systems, however, lack all the essential reflective and metacognitive capabilities 
that would be needed to explain what they do or don’t do.
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To rephrase Descartes, it is, 
therefore, as futile to ask a clock on the wall why it just struck seven or eight as it is to 
ask a deep learning AI system why it gave a specific grade to a student. Clocks are not 
built to explain their ticking, and AI systems, as we know them, have no explanatory 
capabilities. At best they can support humans in explaining what happened and why. As