Digital competencies in student learning with generative artificial intelligence: Policy implications from world-class universities

Abstract

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. Practitioner Notes 1. When developing digital competencies for teachers and students, practitioners are advised to integrate technical skills, communication, pedagogical strategies, and infrastructure support for teachers and students. 2. Targeted policies to address digital inequity should consider subsidized connectivity, infrastructure development, and digital literacy programs for underserved populations. 3. The education industry should adopt data governance protocols to ensure transparency, accountability, and fairness in AI decision-making processes. 4. Teachers should prioritize critical reflection and ethical evaluation of AI tools in education to promote responsible and inclusive learning. 5. When promoting AI-enabled learning, practitioners should consider personalized education to ensure measurable improvements in the quality and inclusivity of student learning experiences.