Application Models and Effectiveness Evaluation of Artificial Intelligence Assistants in Information Technology Classes

Abstract

With the rapid advancement of artificial intelligence technology, the application potential of intelligent assistants in the educational field has become increasingly prominent. Information technology classes, as an academic environment emphasizing practical skills and innovative thinking, represent a typical scenario for the application of intelligent assistants. This study focuses on the application models and effectiveness evaluation of artificial intelligence assistants in information technology classes. By deconstructing their educational functions and analyzing their alignment with subject-specific characteristics, an application framework centered on contextualization, adaptability, and integration has been developed. This framework delineates key elements of intelligent assistants in terms of role positioning, activity sequences, content ecology, and interaction channels, while also designing a concrete pathway spanning from system customization to classroom implementation and further to data-driven optimization. On this basis, the research further proposes a multidimensional evaluation system encompassing cognitive skills, behavioral engagement, emotional experience, and social interaction, employing a mixed-methods approach to systematically analyze application outcomes. The findings provide theoretical support and practical guidance for the deeper integration of intelligent assistants in information technology education.