Artificial Intelligence and Trusted Digital Credentials in Cross-Border Qualification Recognition
Abstract:
The continuous integration of Artificial Intelligence (AI) into higher education is redefining how qualifications are assessed and recognized across borders. This paper addresses the question of whether AI can strengthen the transparency, consistency, and efficiency of recognition procedures while remaining anchored to legal, ethical, and quality assurance principles. The analysis adopts a qualitative and comparative methodology, combining the review of international legal frameworks with selected institutional case studies. It examines instruments such as the Lisbon Recognition Convention, the Council of Europe Framework Convention on Artificial Intelligence, and the European AI Act, alongside initiatives such as CIMEA’s ARDI and DiploMe platforms and the Europass Digital Credentials Infrastructure. The paper highlights both the opportunities introduced by AI, including reduced administrative burden and enhanced fraud detection, and the emerging risks, particularly those related to algorithmic opacity, bias, and due process. It also considers how trusted digital credential ecosystems and Distributed Ledger Technology (DLT) can strengthen document integrity and verifiability, drawing on DiploMe’s blockchain-based publication and verification mechanisms, including digitally signed statements and QR-code or link-based “fast verification” features. Building on this founda tion, the paper outlines operational requirements for competent authorities and higher education institutions, including risk-tiering of AI uses, minimum documentation and explainability expectations, clearly defined human oversight responsibilities, and accessible complaint and appeal pathways for applicants. It further discusses data quality and interoperability constraints when AI tools interact with digital credential infra structures, emphasizing safeguards for cross-border data exchange, privacy-preserving design, and measures to prevent automation bias in decision-making. It concludes by proposing the development of a new subsidiary text to the Lisbon Recognition Convention to ensure the ethical, transparent, and accountable use of AI and trusted digital credentials in qualification recognition, and it offers a practical roadmap for piloting compliant AI-assisted recognition processes without eroding procedural fairness or institutional trust. In doing so, the paper positions AI-supported recognition not as a replacement for expert judgment, but as a regulated deci sion-support layer that must remain accountable to applicants’ procedural rights and to the mutual-trust logic of cross-border recognition.
