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Construction and Empirical Analysis of an Artificial Intelligence-Based Educational Assessment Model
Abstract
With the rapid development of artificial intelligence (AI), traditional educational evaluation models are increasingly inadequate in data processing, dynamic feedback, and personalized analysis—limiting their ability to support quality monitoring in adult and vocational education. This study addresses this gap by constructing an AI-based educational assessment model tailored to diverse learning contexts. First, a multi-level, quantifiable evaluation framework grounded in data-driven principles is developed, and then machine learning and deep learning algorithms are optimized to enhance model adaptability and accuracy. Empirical analyses verify the model's performance in evaluating learning processes and outcomes, demonstrating its advantages in improving efficiency and feedback precision. The discussion highlights practical challenges and refinements, emphasizing the model's value in providing scientific decision support for educators and administrators in adult education settings.
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