From Assessment Reform to AI-Ready Teaching: The ARP Loop as a Model for Professional Adaptation in Online Higher Education

Abstract

The paper presents the implementation path at DOBA University of Applied Sciences, Slovenia starting from an assessment-first approach focused on redesigning assignments and criteria to ensure academic integrity. While initial measures (training, policies, guidelines) supported alignment and AI literacy, consistent changes emerged only after introducing a mandatory AI statement defining the conditions of GenAI use in each assignment. Building on this, the institution developed the AI-Ready Pedagogical Loop (ARP Loop), a participatory and iterative model supporting teacher adaptation and rapid, practice-based integration of AI. The ARP Loop follows a problem-first logic (pedagogical need → solution → tool) through cycles of co-design, piloting, and refinement. The case study shows that this approach improves constructive alignment, supports teacher agency, and enables pedagogically meaningful use of GenAI. The paper also proposes an evaluation framework and transferable design implications for sustainable AI integration.