Behind the AI policies: A critical examination of the underlying conceptions of assessment in higher education

Abstract

The emergence of generative artificial intelligence (GenAI) has prompted universities to develop policies regulating its use in teaching, learning, and assessment. Little empirical research has examined how they conceptualise assessment, and the assumptions underpinning their normative frameworks. This study addresses this gap through a comparative analysis of GenAI-related policy documents from 14 universities in Switzerland and Spain. Drawing on Chan's AI Ecological Education Policy Framework and Bacchi's problematisation theory, we use collaborative consensus-based coding to examine pedagogical, governance, and operational dimensions alongside problem representations, assumptions, subject positions, and silences. The findings reveal coexisting and sometimes competing representations of GenAI. Concerns about academic integrity, authorship, transparency, and data protection coexist with calls for AI literacy, assessment redesign, formative feedback, and critical engagement with AI. Assessment emerges as a key site of this tension, with policies combining AI-resistant approaches with attempts to integrate GenAI into educational processes. The analysis also reveals asymmetrical subject positions: teachers and institutions generally define legitimate AI use, whereas students are positioned as accountable users expected to disclose, justify, or verify their use of AI. AI literacy and evaluative judgement are likewise framed mainly as students competencies, leaving teachers corresponding needs less examined. Current GenAI policy therefore represent an important but incomplete response to AI-driven change. Future policy development should move beyond regulating acceptable use towards participatory and pedagogically grounded frameworks that involve students more actively in shaping AI-mediated assessment and support human agency, evaluative judgement, and meaningful learning.