A Framework for Generative AI-Driven Assessment in Higher Education

Abstract

The rapid integration of generative artificial intelligence (AI) into educational environments raises both opportunities and concerns regarding assessment design, academic integrity, and quality assurance. While new generation AI tools offer new modes of interactivity, feedback, and content generation, their use in assessment remains insufficiently pedagogically framed and regulated. In this study, we propose a new framework for generative AI-supported assessment in higher education, structured around the needs and responsibilities of three key stakeholders (branches): instructors, students, and control authorities. The framework outlines how teaching staff can design adaptive and AI-informed tasks and provide feedback, how learners can engage with these tools transparently, and how institutional bodies can ensure accountability through compliance standards, policies, and audits. This three-branch multi-level model contributes to the emerging discourse on responsible AI adoption in higher education by offering a holistic approach for integrating AI-based systems into assessment practices while safeguarding academic values and quality.

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