Auditable AI Literacy Interventions: Embedding Regulatory Principles into Higher Education

Edisy Kin Wai Chan, Beatrice Yan-yan Dang

Keywords: auditable AI interventions, AI literacy, higher education, regulatory alignment, accountability in AI, regulatable machine learning

TL;DR: This paper introduces Auditable AI Literacy Interventions, a framework that integrates audit instruments into higher education to align AI literacy with regulatory principles and prepare learners for accountable, regulatable machine learning.

Abstract: In recent years, artificial intelligence (AI) has become an integral part of education, work, and governance, making AI literacy a critical competency for higher education. Yet, in today’s higher education landscape, courses and programmes involving AI literacy tend to focus primarily on teaching knowledge and skills while overlooking a crucial element: \textit{auditability}---the capacity to document, assess, and demonstrate responsible AI use in ways that align with regulatory standards. In this paper, we introduce the concept of \textit{Auditable AI Literacy Interventions}, which incorporate audit instruments into AI literacy education to parallel standard regulatory practices such as conformity assessments, provenance tracking, and oversight structures. We outline a conceptual framework for designing these interventions, propose practical tools for classroom use, and illustrate how they can be integrated into tertiary level course modules. The main contribution of this work is to reconceptualize AI literacy: it should serve not only as an educational objective but also as a means of preparing institutions for regulatory compliance, thereby aligning higher education with emerging standards for regulatable machine learning.

Submission Number: 53