In the context of digital transformation, the iterative upgrading of artificial intelligence technology is reshaping the development ecology of archival disciplines, making innovation in archival teaching an urgent necessity. Therefore, it is imperative to explore how AI can drive innovation in archival teaching, aiming to cultivate archival professionals adept at meeting the demands of the new era. Utilizing literature research and case analysis methods, this paper examines the internal logic and current applications of AI-enabled archive teaching. It analyzes typical cases of archive intelligence education from Wuhan University and Renmin University of China, summarizing archive teaching practices in higher education institutions. The study reveals that empowering archival teaching with AI is a prevailing trend. A collaborative teaching mechanism integrating “teachers, resources, and technology” should be established, with a focus on policy and legal frameworks. Innovation in archive teaching should adhere to the principles of student-centeredness and shared teaching resources, emphasizing the need to maintain the essence of the discipline during the technological empowerment process. It is essential to utilize archive natural language processing, machine learning, and other technologies to enhance the effectiveness of archive compilation and digital management teaching. However, caution should be exercised to avoid technological dependence and weakened thinking. Efforts should be made to cultivate composite archive talents possessing both technical skills and professional qualities.

Innovation in Archival Teaching, Artificial Intelligence, Implementation Pathways