Research on the Construction of a Dynamic Teaching Evaluation System Based on Generative Artificial Intelligence

Abstract

Digital technology is moving so fast that we’re running into a real shortage of top-tier innovative talent. This shift is making it hard for traditional teaching assessment models to keep up, and it’s also bringing some deep-rooted, long-standing issues to the surface. Generative AI is great at handling massive datasets, which gives us a fresh way to look at dynamic teaching assessment. In this paper, we’re going to explore how we can use this technology to build a more dynamic evaluation framework for education. The introduction of artificial intelligence has changed the underlying logic of the entire evaluation system. Old-school evaluation systems used to be straightforward and top-down, but that just doesn't work anymore. We now need more voices involved. The focus has shifted away from just testing surface-level knowledge toward actually digging into how students think. What's more, those old, slow grading methods are being replaced by systems that offer real-time feedback. Because of this, assessment shouldn't just be an end-of-term event. It needs to be woven into every part of the learning journey—before, during, and after class—to keep the evaluation process ongoing. Of course, teachers have to stay in the driver’s seat throughout the teaching process. This is non-negotiable. We really need to be careful about the risks and ethical baggage that can come with using new tech. Overall, this new system aims to break past the limits of traditional quantitative assessment. It brings intelligent tech back to its core purpose: supporting human growth. By blending practical tech with the human side of education, we’re proposing an evaluation model that is both more scientific and genuinely effective for training talent in this digital age.