From perceived transparency to trustworthiness: Testing the Cognitive–Narrative Mediation (CNM) framework in government AI policy communication

As artificial intelligence (AI) becomes increasingly embedded in public-sector services, governments face a communication challenge that extends beyond disclosure: how to support citizens’ interpretation and evaluation of these systems. Although initiatives such as the Australian government’s AI Transparency Statements (ATS) policy aim to promote accountability, it remains unclear whether conventional text-heavy disclosures effectively support public understanding and trust. This study advances a Cognitive–Narrative theory of AI transparency communication by introducing the Cognitive–Narrative Mediation (CNM) framework, which explains how visual communication formats shape perceived trustworthiness through users’ experiential processing. Drawing on cognitive load theory and narrative engagement research, perceived transparency is conceptualised as a psychologically constructed judgement associated with two experiential pathways: cognitive ease and narrative immersion. In a controlled experiment ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> = 57), we compared a text-only ATS with an equivalent disclosure supplemented by a narrative-style video explainer while maintaining informational equivalence. The video condition produced higher cognitive ease and stronger narrative immersion than the text-only format. Analyses further indicate that perceived transparency was statistically associated with these experiential responses in ways consistent with the dual-pathway structure proposed by the CNM framework, which subsequently informs evaluations of agency trustworthiness across ability, integrity and benevolence dimensions. Because the video explainer represents a bundled change in visual communication format, incorporating multimodal presentation and structured narrative guidance, the findings are interpreted as format-level evidence rather than the isolated influence of narrative features. By shifting attention from disclosure content to disclosure experience, the CNM framework offers a process-oriented account of AI transparency communication and highlights how visual communication design can shape perceived transparency and the formation of institutional trust without altering underlying policy information.