Artificial Intelligence-Driven Curriculum Optimization in Xi'an Universities: An Empirical Exploration of Educational Practice

Abstract

This study focuses on the application of artificial intelligence (AI)-driven curriculum optimization in universities in Xi'an, Shaanxi Province, China, aiming to address the evolving demands of higher education amid digital transformation. Employing qualitative research methods including semi-structured interviews with frontline educators and systematic literature analysis, the research delves into teachers' perceptions, attitudes, and practical experiences regarding the integration of AI in curriculum design, teaching implementation, and assessment processes. It identifies key challenges hindering effective AI adoption, such as inadequate teacher technical training and adaptability, ethical risks involving student data privacy and algorithmic bias, uneven institutional infrastructure, and the lack of localized implementation guidelines. Based on the findings, the study formulates targeted strategies-encompassing tailored professional development programs, infrastructure upgrading frameworks, and collaborative academia-industry partnerships-and proposes actionable ethical practice guidelines to ensure responsible AI use. By enriching localized empirical research on AI in education, this study provides valuable references for policymakers in refining educational policies, educators in enhancing teaching practices, and technical developers in optimizing AI tools, ultimately promoting the effective, equitable, and ethical integration of AI in higher education contexts similar to Xi'an. Additionally, it explores the potential of AI to bridge educational disparities between different tiers of universities in Xi'an, offering insights into fostering more inclusive and future-oriented learning environments.