Jul 2026· International Journal of Learning and Development· 0 citations
TL;DR
A research-based approach to make the abstract concrete, metaphorically speaking, by putting the concepts into a wheelbarrow so the authors can push them around more successfully in the context of the higher education institutional and classroom settings.
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.
This paper is the second of a two-part series that frames ethical AI use in higher education as a question of learner formation rather than simply as a matter of policy, compliance, or tool governance. Using Torraco’s (2016) theory-building integrative review methodology, this paper synthesizes research across AI-mediated communication, learner development, motivation, epistemic trust, authorship, attention, cognitive agency, self-regulated learning, and institutional design through the lens of the higher education student learning experience. The paper extends Understanding the Machine, Part I: A Practical Framework for AI Ethics in Higher Education by developing the Human Intellectual Life Cycle (HILC), an original conceptual model identifying the inner sequence through which attention deepens into focus, focus matures into intention, intention guides action, and action becomes material for reflection. The HILC clarifies the developmental process through which ethical AI use can be evaluated. The review also introduces intellectual autonomy (IA) as the learner’s capacity to remain the self-directing subject of their own intellectual formation in an AI-mediated environment capable of redirecting that formation. Finally, the paper examines contrary perspectives and offers implications for students, faculty, and university systems, along with recommendations for future empirical research.
Janet L Hanson· International Journal of Lea...· 0 citations
Recent years have seen a growing discrepancy in the field of AI alignment: research and policy recommendations on AI ethics tend to assume a general set of ethical values, yet proliferating practice-specific uses of AI systems on the ground - in the legal, medical and translation domains, among others - have been effectively manifesting ethics of professional practice. This article begins by outlining the reasons why general and professional ethics are increasingly conflicted in contemporary AI systems, and by surveying how the research literature attests to, but has not yet resolved, this conceptual and practical challenge. We then conceptualize the main dimensions of AI models'decision-making in areas of professional practice, emphasizing professional ethics'hierarchically structured relationship with general ethics, and elaborating on the mechanisms through which they reach an equilibrium in situational contexts that involve conflict. It is through this equilibrium, we suggest, that certain professional ethics are prioritized over others and implemented in practice. We then show how our framework can be the basis for a systematic empirical assessment of AI models'professional ethics in various domains, identifying the nuances of the models'favored ethic by examining their production in a series of similar but not identical scenarios. Finally, we propose a formulation for how to intervene in and change AI models'favored ethics in professional practices - while noting the inherent dimension of subjectivity involved in both the evaluation and implementation of professional ethics in AI models.
A conceptual framework offering AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation is proposed.
Arpana Koul· Review of Artificial Intelli...· 0 citations
It is argued that AI-related integrity disputes are better understood as conflicts between competing values than as individual moral failings, and implications for policy design, assessment reform, and faculty development are discussed.
M. Grobler· Proceedings of the Internati...· 0 citations
It is argued that attending to effects complements current approaches by directing ethical inquiry towards the cumulative transformations that emerge through everyday human–LLM interaction.
This paper argues that the existing frameworks for AI literacy are insufficient because they enforce a consumer-orientation toward AI rather than fostering genuine epistemic agency, and proposes a three-part framework of AI literacy based on the notions of contextual use, critical interrogation, and participatory governance.
Brady D. Lund, Z. A. Teel· arXiv.org· 0 citations
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