The transparency trap: Generative AI and the rise of interpretive load

Abstract This commentary interrogates the renewed centrality of ‘transparency’ in higher education's response to generative artificial intelligence (AI) and introduces interpretive load as a necessary extension to existing assessment theory. While transparency is conventionally framed as a vehicle for fairness and clarity, research demonstrates that written criteria and policies have never been fully legible nor neutral. The advent of GenAI has intensified this paradox: institutions publish increasingly elaborate rules and declarations, yet students face greater uncertainty about how to understand and act on them. Drawing on empirical studies of policy trajectories, workload, academic integrity, cross‐cultural perspectives and the sociology of assessment, I argue that contemporary AI policies shift substantial cognitive, emotional, ethical and procedural labour onto students, who must interpret ambiguous norms before they can begin any task. The commentary situates interpretive load within the established architecture of assessment workload, arguing that it constitutes a distinct form of front‐end labour produced by institutional and instructional design rather than by students' academic choices. I argue that interpretive load is structurally uneven: linguistic resources, cultural orientations, policy literacy and institutional fluency shape students' capacity to navigate fragmented GenAI rules. Documentary transparency further falters under contradictory institutional imperatives—encouraging innovation while safeguarding originality—and under the technical volatility of detection tools. To address these limitations, I propose a shift from declarative to dialogic transparency: an institutional practice that redistributes interpretation through programme‐level translation, teachable disclosure conventions, clearer due‐process guarantees and iterative policy‐practice feedback loops. I argue that interpretive load represents the next equity frontier in assessment and that redesigning for shared sense‐making is now essential for fair and ethical AI‐era assessment. Context and implications Rationale for this study: GenAI policies rely heavily on transparency, yet little work examines transparency as a producer of labour and inequality. Why the new findings matter: Introducing interpretive load exposes the hidden cognitive and ethical work created by AI‐era policies and reframes transparency as a structural—not merely communicative—challenge. Implications for practitioners, policymakers and researchers: This commentary offers a conceptual lens for redesigning assessment in the AI era. For practitioners, it clarifies why students struggle with ambiguous rules and how dialogic transparency can reduce inequity. For policymakers, it demonstrates the limits of document‐centric approaches and the need for due‐process clarity, coordinated programme‐level norms and iterative revision. For researchers, it identifies interpretive load as an emergent analytic category that connects transparency, workload and equity, opening new directions for empirical study of how institutional design shapes students' interpretive labour.