Understanding the Machine Part I: A Practical Framework for AI Ethics in Higher Education

Abstract

Ubiquitous conversations in the literature and media around AI ethics will benefit from rigorous examination of the terminology used. Terms such as ethics, integrity, authorship, and fairness are abstract constructions, which means we each bring our unique interpretations to these discussions. The practical conceptual framework offered in this paper addresses this diversity of interpretations directly by proposing a research-based approach to make the abstract concrete, metaphorically speaking, by putting the concepts into a wheelbarrow so we can push them around more successfully in the context of the higher education institutional and classroom settings. Five enduring concerns are identified and organized into a framework that proposes specific areas that can become at risk in the formation of a learner when using AI: truth, integrity, justice, independence, and responsibility. Four conceptual metaphors are provided that transform abstract concepts into accessible, practice-oriented principles drawn from research in AI-mediated communication (AI-MC), learner development, self-determination theory, epistemic trust, and institutional design. Implications are offered for student practice toward self-authorship and program completion in the age of AI, faculty pedagogy, institutional design, and a companion Part II theory-building integrative review is referenced.