Treffer: Digital Transformation Driving Educational Reforms in Higher Education: A Paradigm Shift in Teaching and Learning.

The digital transformation of universities is a strategic choice to deepen educational and teaching reforms and strengthen talent cultivation. Leveraging the information and communication technology (ICT) strengths of Xidian University and the theoretical and practical achievements of educational informatization, we explored and practised methods for digital transformation to drive profound reforms in higher education. First, with the ideas of "data-driven, AI-empowered" as the core, we addressed systemic issues in educational and teaching reforms by establishing the intelligent education brain and the "intelligent teaching platform." This facilitated data-driven process of reengineering, forming the five-duals nurturing model or Xidian model, harnesses digital transformation to drive educational reforms. Second, in response to the precise and personalized demands of talent cultivation, we constructed a resource provision model and a cobuilt sharing system based on human–technology collaboration. This generated a precise and personalized teaching model that was comprehensive and data-driven. Finally, to address the major formative issues in educational and teaching assessment, we built a data-driven evaluation system for the entire process. We have persistently pursued the integration of ICT with education and teaching, upholding the human–technology collaboration model for talent cultivation and development. Our approach has reshaped a first-class undergraduate education system in the era of the digital economy, establishing a benchmark university for "artificial intelligence + education" and creating a comprehensive solution that is replicable and adaptable. [ABSTRACT FROM AUTHOR]

Copyright of Beijing International Review of Education is the property of Brill Academic Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)