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ITS vary greatly in design, implementation, and educational focus. When ITS are used in a classroom, the system is not only used by students, but by teachers as well. This usage can create barriers to effective evaluation for a number of reasons; most notably due to teacher intervention in student learning.
[citation needed] Extensive work has also been done in reasoning by analogy using induction and abduction. [1] Other important topics include reasoning under uncertainty and non-monotonic reasoning. An important part of the uncertainty field is that of argumentation, where further constraints of minimality and consistency are applied on top of ...
Ai offers scholars and students automatic assessment and feedback, predictions, instant machine translations, on-demand proof-reading and copy editing, intelligent tutoring or virtual assistants. [17] The "generative-AI supply chain", [24] brings conversational coherence to the classroom, and automates the production of content. [25]
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
In 2012, the Ministry of Education in Buenos Aires looked into modifying the cell phone prohibition use in the classroom that had been in effect since 2006. In addition to using SMILE, educators can now create executable programs on mobile devices to help facilitate learning in the classroom.
This may involve reasoning over observed events or example data provided for training purposes. For example, machine learning systems may use inductive reasoning to generate hypotheses for observed facts. Learning systems search for generalised rules or functions that yield results in line with observations and then use these generalisations to ...
The current class of AI models, which work by statistically predicting the next word, have struggled with abstract math, which requires greater reasoning capabilities resembling human intelligence.
The history of computational thinking as a concept dates back at least to the 1950s but most ideas are much older. [6] [3] Computational thinking involves ideas like abstraction, data representation, and logically organizing data, which are also prevalent in other kinds of thinking, such as scientific thinking, engineering thinking, systems thinking, design thinking, model-based thinking, and ...
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