Advancing Automatic Recognition through Digital Transformation:Balancing Automation with a Human-Centric Approach

Abstract

The digital transformation (DT) of academic credential recognition aims to contribute to the policy goal of Automatic Recognition (AR), a long-standing commitment established through normative frameworks like the Lisbon Recognition Convention (LRC), the European Higher Education Area (EHEA) and the Council Recommendation on promoting automatic mutual recognition of qualifications and learning periods abroad. While DT provides key opportunities for efficiency, transparency, and consistency, AR itself - defined primarily as a reduction of separate recognition procedures - must be explicitly differentiated from full technological automotion, a risk arising from over-reliance on digitalization. This article first reconstructs the history of AR policies and subsequently analyzes the binding (inter) national regulations governing DT and Artificial Intelligence (AI), specifically noting that the EU AI Act classifies AI systems used in education and qualification evaluation as high-risk. The research question addresses how DT can successfully support AR’s procedural streamlining without escalating into total automotion that bypasses the principles of human-centric evaluation and the mandatory requirement for human oversight imposed by international frameworks (EU, Council of Europe, UNESCO). Adopting normative document analysis and utilizing the case study of CIMEA (the Italian ENIC-NARIC center), the study identifies crucial pillars for a compliant DT strategy that effectively supports AR: Human Oversight and Accountability, Human-Centric Design, Ethics-by- Design and Quality-by-Design, Robust Data Governance and Privacy, Transparency and Explainability, and AI Literacy/Upskilling for staff.