Digital Competence in Student Learning with Generative Artificial Intelligence: Policy Implications from World-Class Universities - Zhang, Youliang; Tian, Zhen (Journal of University Teaching and Learning Practice, 2025)
In the context of digital transformation and given the recent emergence of Generative Artificial Intelligence (GAI), it is vital to identify the skills needed for using this technology in teaching and learning. This study investigates the digital competence required for utilizing GAI in learning and the corresponding policy implications. Adopting the DigComp framework, a qualitative content analysis of regulatory documents from 88 globally distributed world-class universities was conducted to uncover students’ digital competence levels in using GAI and identify influential factors. Findings indicate that these higher education institutions (HEIs) place a strong emphasis on digital literacy, safety, and critical thinking when regulating students’ competence in the use of GAI technologies. However, it is also evident that communication and collaboration competencies are often overlooked in the implementation of GAI technologies within educational settings. Moreover, as the world-class universities primarily focus on enhancing students’ output capability and assessing their learning outcomes, challenges arise in terms of content creation and problem-solving competence when implementing GAI technologies. Consequently, key policy implications and recommendations are provided for educational policymakers and practitioners to address these gaps and enhance the effective integration of GAI in learning environments across various global contexts.