As digital transformation accelerates, quality assurance (QA) must evolve from a reactive function to a proactive system capable of guiding institutions through AI-driven disruption. This paper explores how QA frameworks can shape the future of higher education by ensuring that AI technologies enhance rather than erode academic quality, learning equity, and institutional trust.

We present a European-wide landscape of emerging QA practices related to AI integration and introduce a pilot case study from a Georgian university deploying AI-powered tools in QA processes. These include automated student feedback analysis, curriculum relevance mapping using machine learning, and detection of bias in digital assessments. The case is analyzed through the lens of ethics, transferability, and long-term institutional sustainability. The paper proposes a draft framework of “AI-readiness indicators” for QA systems that can be adapted across national contexts. These indicators address algorithmic transparency, academic oversight, participatory QA mechanisms, and digital inclusion. By combining policy insight and field-level experimentation, the session aims to equip participants with strategic tools to navigate digital disruption while preserving academic integrity. The discussion invites QA professionals and institutional leaders to reflect on how QA can evolve into a forward-looking, ethical, and resilient compass in the AI era.

This paper was presented at EQAF and reflects the views of the named authors only.

ISSN: 1375-3797

Anticipating Change: Designing AI-Ready QA Systems for Future-Proof Universities

Tea Imedadze, Eka Luashvili
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