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At first glance, artificial intelligence in education offers pertinent technical solutions to address future education needs. [20] AI champions envision a future where machine learning and artificial intelligence might be applied in writing, personalization, feedback or course development.
Experts predict that 2025 will be the year artificial intelligence (AI) truly gets off the ground in K-12 schools. 2024 laid the groundwork for AI to reach a level of “maturity” in education ...
At Princeton High School, students are trying to combat the rapid decline of indigenous languages with some unlikely help: a furry, wide-eyed stuffed animal named Che’w. Many high schools are ...
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. [1]
A cognitive tutor is a particular kind of intelligent tutoring system that utilizes a cognitive model to provide feedback to students as they are working through problems. . This feedback will immediately inform students of the correctness, or incorrectness, of their actions in the tutor interface; however, cognitive tutors also have the ability to provide context-sensitive hints and ...
In this context, it is essential for education to adopt a humanistic approach, particularly in light of the increasing prominence of digital technologies. [7] An example of the application of innovative technology in education is the implementation of an AI-based tutoring system at an entry-level IT school in Pensacola by the U.S. Navy.
Given a description of the possible initial states of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem is to synthesize a plan that is guaranteed (when applied to any of the initial states) to generate a state which contains the desired goals (such a state is called a goal state).
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]