Research on the Reform of Open Education Teaching Based on Adaptive Learning in the Era of Artificial Intelligence

Abstract

The rapid development of artificial intelligence technology has provided an opportunity to reshape the teaching ecosystem in open education. This article focuses on the concept of "adaptive learning", in the context of the artificial intelligence era, and explores the systematic reform of open education teaching models. The research first constructed an integrated learning framework that combines cognitive diagnosis, dynamic paths, resource push, immediate feedback, and emotional support. Through data-driven and teacher experience collaboration, it realizes large-scale personalized teaching. Secondly, based on the teaching practice of public courses in multiple universities, the article collected and analyzed the entire process behavior data of learners, used deep models to dynamically optimize teaching strategies, and established an interpretable and iterative teaching loop. On this basis, the research focuses on educational equity and the mechanism of human-computer collaboration, ensuring that while technology is empowered, the dominant position of teachers and the warmth of the learning community are maintained. Through qualitative interviews and teaching observations, the article found that adaptive learning significantly enhanced the initiative, satisfaction, and knowledge transfer ability of learners, forming a new classroom culture that integrates online and offline elements and reshapes the roles of teachers and students. The research conclusion states that in the open education teaching reform of the artificial intelligence era, it should be driven by data intelligence, centered on learners, and based on educational equity, promoting the transformation from "standardized supply" to "precise services", providing replicable models and sustainable paths for building a lifelong learning society.