Comparison of Different AIED Models and Evaluation Methods
DOI:
https://doi.org/10.54097/yh47p531Keywords:
AI, Machine learning, Student models, Education.Abstract
In the educational field, although machine which attempts to learn AI is still in their early stages, the approach has yet to show remarkable results when facing complex challenges without obvious cut-off points, such as grading students’ papers or exploring enormous and complicated data collections. AI can also be used to create virtual learning environments, intelligent testing systems, and automated grading systems. AI in educational fields refers to the application of AI technology to enhance and support the studying processes, such as tracking students’ behavior and constructing models that can accurately hypothesize students’ achievements. It can include the use of AI-powered tutoring systems, personalized learning platforms, and data analysis tools that can help teachers and administrators better understand student needs and progress. This paper mainly concentrates on the field of artificial intelligence tutoring and summarizes the methods by which intelligent tutors assess student performance by comparing some student models and the input, output, and model forms of methods for evaluating fine-grained interactions of intelligent tutors. This article also provides basic information and new perspectives for studying which methods to use to model and evaluate tutorial learning.
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