Competences for Educators, Administrators, and Students in a Digital Society: A Role-Based Framework for Capability Development
DOI:
https://doi.org/10.36690/2733-2039-2025-4-31-39Keywords:
digital competence, educator competence, administrative competence, student competence, DigComp, DigCompEdu, digital leadership, data governance, digital citizenship, self regulated learning, AI literacy, competence assessmentAbstract
Digital transformation has redefined what it means to teach, govern, and learn, because educational practice now depends on data intensive platforms, hybrid learning models, and digitally mediated communication. This article develops a role based competence model that differentiates competences for educators, administrators, and students while retaining a shared core of digital competence domains applicable across educational systems. A structured narrative review with thematic synthesis was conducted across education, learning sciences, and educational technology governance scholarship, complemented by analysis of widely used competence frameworks. The synthesis shows that educator competences are most robust when defined as pedagogical design and assessment capability rather than tool operation, consistent with TPACK and educator specific competence frameworks. Administrative competences emerge as governance capacity that includes strategy, procurement, privacy and security, data governance, and accountability for equity impacts. Student competences extend beyond functional digital skills to critical digital literacy, self regulated learning, digital citizenship, and emerging AI literacy, aligned with citizen competence frameworks and contemporary literacy research. Competence development is most effective when treated as institutional capability building with role specific pathways, practice based assessment, and governance routines that protect inclusion and safety. Future studies should validate role specific indicators, test causal links between competence growth and learning outcomes, and develop assessment designs that capture judgement, ethics, and equity effects in AI enabled learning environments.
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