Abstract:

This paper examines how Records of Processing Activities (RoPA), required under the General Data Protection Regulation (GDPR), can be transformed from static compliance documents into actionable design instruments for Artificial Intelligence (AI) systems in higher education. While universities increasingly deploy AI-based tools for learning analytics, admissions support and academic integrity monitoring, their GDPR documentation often remains disconnected from the technical and organisational controls implemented in practice and from the pedagogical objectives these systems are meant to pursue. Building on the Technical–Regulatory Correspondence Matrix (TRCM), we propose an organizational and technical approach that links RoPA entries to concrete, verifiable controls for AI lifecycles, including data governance mechanisms, explainability artefacts and human oversight procedures. The approach explicitly targets typical university actors—data protection officers, CIOs, quality assurance units and programme directors—providing them with a shared reference for aligning legal documentation, institutional policies and AI engineering decisions. We argue that, when systematically structured, GDPR records of processing can provide a natural backbone for mapping legal principles—such as purpose limitation, data minimisation, transparency and accountability—to implementable requirements in Machine Learning Operations (MLOps) pipelines and day-to-day classroom practices.

The paper contributes:
(i) a conceptual model showing how RoPA fields can be extended to capture AI-specific risks, technical dependencies, model documentation and evidence sources;
(ii) a set of mapping patterns that connect RoPA-based registers to metrics, logs, documentation bundles and decision dossiers required for trustworthy and explainable AI;
(iii) an illustrative mini-case in a university learning analytics scenario demonstrating how decision provenance, model lineage and student-facing explanations can be anchored in existing GDPR governance structures.

By reframing RoPA as a living interface between legal documentation, institutional governance and AI engineering practice, the proposal supports universities in moving from checklist-oriented compliance to evidence-based, auditable and pedagogically aligned AI deployments, and offers a concrete artefact for teaching students how to translate GDPR obligations into implementable controls for AI in education. The approach is illustrated with concrete, reusable institutional artefacts.The proposed perspective is deliberately practical: it results in checklists, templates and traceability views that can be reused across multiple AI initiatives, and that help universities evidence compliance during audits and accreditation processes. At the same time, the work contributes to the wider debate on the impact of AI on education by showing how robust GDPR governance can be leveraged not as a barrier to innovation, but as an enabler of trustworthy, student-centred AI ecosystems in higher education.

Keywords:

Global Issues in Education, Higher education governance, General Data Protection Regulation (GDPR), Records of Processing Activities (RoPA), Artificial Intelligence in Education (AIED), Organizational and legal aspects, Learning analytics, Explainable Artificial Intelligence (XAI).