Auditable AI literacy interventions: embedding regulatory principles into higher education

Chan, Edisy Kin Wai and Dang, Beatrice Yan-yan (2025) Auditable AI literacy interventions: embedding regulatory principles into higher education. NeurIPS 2025 Workshop on Regulatable ML, , San Diego, United States. 8 pp .

Record type: Conference or Workshop Item (Paper)

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.

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