Research on the Application of AI Dynamic Evaluation in the Teaching Reform of Marketing Courses

Abstract

In marketing courses, traditional evaluation methods struggle to capture the cognitive changes in students' strategic analysis and creative generation phases. AI dynamic evaluation offers a solution to this dilemma by collecting behavioral data in real time, generating personalized feedback, and dynamically adjusting teaching content. This study clarifies the differences between dynamic and static evaluation, constructs a cyclic iterative model of evaluation and learning, designs transformation rules from behavioral data to evaluation indicators, proposes strategies for content difficulty adjustment and learning path modification, builds a three-tier multi-dimensional indicator system, explains the positive influence logic of AI evaluation on learning engagement, and redefines the evaluation function in two-way feedback between teachers and students. The research shows that AI dynamic evaluation can transform external scoring into an organic component of the learning process, providing both a technical path and theoretical support for curriculum teaching reform.