Jul 2026· Journal of Management Education· 0 citations· 71 references
TL;DR
By making the pedagogical architecture of ethical AI literacy explicit, the article offers a structured reference for curriculum design, accreditation processes, and lifelong managerial learning, providing actionable guidance for business schools and universities.
Abstract
As Artificial Intelligence increasingly shapes organizational decision-making, managers must move beyond functional literacy toward critical engagement with its ethical, social, and governance implications. Despite growing AI ethics scholarship, a gap persists in how these competencies can be systematically developed within management education. This study addresses this gap by proposing the Ethical AI Literacy Framework for Management Education, a pedagogical architecture grounded in qualitative synthesis. Methodologically, the article conducts a systematic literature review of 581 peer-reviewed studies to map the multidisciplinary landscape of AI ethics in professional training. To overcome thematic fragmentation, an in-depth thematic analysis of 22 core studies was undertaken. Through abductive coding, these contributions were synthesized to identify recurrent pedagogical logics and competence clusters informing the framework’s construction. The resulting framework articulates three developmental blocks: conceptual foundations of AI systems; ethical reasoning and reflection; and applied managerial context—complemented by a cross-cutting dimension dedicated to curricular integration and evaluation. By making the pedagogical architecture of ethical AI literacy explicit, the article offers a structured reference for curriculum design, accreditation processes, and lifelong managerial learning, providing actionable guidance for business schools and universities.
Artificial Intelligence (AI) is increasingly reshaping curriculum development in higher education as a
socio-technical, multidimensional phenomenon rather than a neutral technological tool. This study explores how AI
influences ethical governance, pedagogical design, institutional capacity, and contextual dynamics within curriculum
systems. Despite growing scholarly attention, existing literature remains fragmented, with ethical, pedagogical, and
institutional dimensions often examined in isolation, limiting a comprehensive understanding of their interdependencies.
Using a narrative integrative review design, this study synthesizes findings from 55 peer-reviewed studies
retrieved from major academic databases, including Scopus, Web of Science, ERIC, IEEE Xplore, and ScienceDirect.
The analysis employed systematic coding and thematic synthesis across ethical-policy, pedagogical-design,
and technical-institutional domains. Findings reveal that AI introduces systemic ethical risks, including algorithmic
bias, transparency deficits, and data governance challenges, while simultaneously transforming pedagogical
practices through personalization and adaptive learning. However, these advances also raise concerns regarding
epistemic narrowing and the redistribution of human agency in teaching and learning processes. At the institutional
level, AI implementation is constrained by infrastructure limitations, governance misalignment, and dependence on
external platforms. Contextual factors further demonstrate that AI curriculum implementation is highly cultureand
institution-specific, challenging the feasibility of universal models of adoption. The study identifies a persistent
fragmentation in existing research and proposes the Integrated Challenge Model for AI Curriculum Development
(ICM-AI-CD), which conceptualizes AI integration as a dynamic socio-technical system comprising interdependent
ethical-policy, pedagogical-design, and technical-institutional domains. The study concludes that AI in curriculum
development represents a systemic transformation of higher education, requiring integrated frameworks that capture
its cascading and interdependent effects. This framework provides a foundation for future research, policy development,
and institutional planning in AI-enabled curriculum systems.
Abdul Malik· Applied Business: Issues &am...· 0 citations
Business schools need to prepare graduates for workplaces in which generative artificial intelligence (GenAI) shapes analysis, communication, customer insight, and decision support. Yet the field still lacks a business-school-specific account of what students should learn beyond tool familiarity. This structured narrative review synthesizes recent literature on AI literacy, business-student preparedness, ethical readiness, and curriculum integration in business education. It draws together four areas of evidence: business-student readiness and adoption, AI literacy and competency frameworks, AI ethics literacy and ethical reflection, and business-school curriculum and implementation. The literature indicates that business students increasingly use GenAI, although much current use remains concentrated in general-purpose academic tasks. More advanced business applications, especially data analysis and decision support, appear less developed. Ethics belongs within business AI literacy because privacy, bias, accountability, transparency, disclosure, and professional responsibility shape managerial use of AI. The review proposes a five-domain Business AI Literacy and Ethical Capability Framework: foundational AI and generative AI understanding, business application and decision-support fluency, critical evaluation and verification, ethical and governance judgment, and transparent professional communication and adaptive learning. The framework begins with recurring AI literacy dimensions in the literature and adapts them to business education using business-student and business-school evidence. It also outlines a three-stage curriculum progression model and discusses implications for pedagogy, assessment, faculty development, and institutional policy. The framework gives business schools a practical way to connect AI use with verification, ethics, and professional judgment across the curriculum.
Rich Yueh· International Journal of Bus...· 0 citations
This systematic literature review critically examines the construct of artificial intelligence (AI) literacy as a foundational competency for the 21st-century workforce and educational landscape. As generative AI becomes inextricably linked with global economic and societal operations, the demand for sophisticated human-AI interaction frameworks has surged. Synthesizing contemporary peer-reviewed studies, international policy frameworks, and empirical data from 2020 to 2026, this review identifies the multidimensional nature of AI literacy, encompassing cognitive understanding, practical competency, and rigorous ethical evaluation. The findings highlight the critical role of AI literacy in fostering self-regulated learning in educational settings and boosting creative self-efficacy and productivity in the workforce. Furthermore, the study addresses significant systemic barriers, including the digital divide and the ethical imperatives to mitigate algorithmic bias and safeguard data privacy. Ultimately, this review proposes that AI literacy is no longer an isolated technical skill but a mandatory pillar of modern civic and professional competence, demanding immediate, equitable integration into global educational curricula and organizational training paradigms.
Cyrus D. Santiago· American Journal of Data Sci...· 0 citations
The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design, and offers a replicable model for fashion programs and other disciplines seeking responsible AI integration.
D. Shen· PUPIL International Journal...· 0 citations
The rapid introduction of artificial intelligence (AI) into higher education is transforming the nature and shape of academic knowledge production. This theoretical paper proposes an AI literacy framework for university educators that extends beyond instrumental skills, incorporating epistemic, ethical and institutional facets. A narrative and selective literature review approach was conducted based on peer-review and policy-oriented literature between 2017 and 2025 in indexed academic repositories. The analysis process was based on an epistemic deconstruction of the existing efforts to frame AI literacy, complemented by thematic, gap, and theoretical reconstruction. The review found that existing frameworks focus on technical use and, to a lesser extent, ethical considerations; they rarely discuss the epistemic status of AI-generated outputs or the need for institutions governance. In response, a five-dimensional AI literacy framework -including instrumental, algorithmic, ethical, epistemological, and institutional dimensions- is proposed herein. This framework is founded on three areas of knowledge: technological, philosophical, and institutional. The framework suggests that university educators must be capable of critically reviewing AI results, defending academic integrity, and working within a clear governance structure. This work opens avenues for future empirical and longitudinal studies.
Mayra Bustillos, Yolvy Quintero, Dílida Luengo et al.· Frontiers in Education· 0 citations
Architectural education is being reshaped as Artificial Intelligence (AI) challenges human-centered conceptions of creativity, authorship, and knowledge production. However, current discussions on AI-supported architectural education remain largely focused on tool adoption, productivity, creativity, and student perception, while the policy implications of AI for curriculum design, studio governance, assessment, educator training, and ethical accountability remain underdeveloped. Addressing this gap, the study develops a policy-oriented posthuman framework for interpreting AI integration in architectural pedagogy and translating it into responsible design education principles. The study adopts a two-stage review design that combines a conceptual framing review of posthuman pedagogy with a systematic synthesis of empirical and pedagogical studies on AI, computational design, and architectural education published between 2010 and 2025. The review identifies three interrelated dimensions of AI-supported posthuman learning: distributed agency, in which design intelligence is shared across students, educators, AI systems, datasets, interfaces, materials, and studio environments; situated knowing, in which AI becomes pedagogically meaningful only when embedded in reflective, material, and context-sensitive design inquiry; and ethical entanglement, in which authorship, bias, accountability, originality, dependency, and environmental responsibility become core educational concerns. Based on these findings, the paper proposes ecological intelligence as a design education policy principle: the capacity to think, design, evaluate, and act responsibly within interconnected human, technological, material, environmental, and institutional systems. The contribution of the study is twofold. First, it clarifies the theoretical relevance of posthuman pedagogy for AI-supported architectural education. Second, it translates this theoretical perspective into a policy-oriented pedagogical framework that can inform curriculum development, studio pedagogy, assessment criteria, and ethical governance in architectural education.
