Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
On artificial intelligence technology adoption in management and higher education
0
Zitationen
2
Autoren
2026
Jahr
Abstract
Purpose Examine implications of artificial intelligence (AI) adoption within organizations and management education. Considering technological parallels, we propose a structured, ethical integration to enhance users’ critical thinking, employability, and adaptive professional skills while mitigating risks like cognitive offloading. Design/methodology/approach This viewpoint paper synthesizes literature on technological adoption patterns, cognitive offloading effects, and emerging AI impacts on decision-making. Drawing on historical examples, it proposes practical applications for embedding AI in management coursework, anchored in critical-thinking frameworks. Findings Accelerating AI adoption brings enhanced rationality closer to realization yet risks over-reliance on decision-support and diminished independent reasoning. Higher education should prioritize foundational critical thinking before extensive AI use. Proposed practices include sequencing AI as an instrumental tool, constrained collaboration, and collegial evaluation. Structured AI usage fosters ethical practices, prompt literacy, transparency, and preparation for AI-integrated workplaces. Research limitations/implications Future research could quantitatively validate the proposed effects on cognitive offloading and critical thinking among students using AI for decision-support. Practical implications Management educators should integrate guided AI applications in assignments. Doing so sets ethical boundaries, reinforces student authorship, and builds competencies (e.g., advanced information processing, emotional regulation, and boundary-setting) for AI usage in organizations. Originality/value The paper offers a critical-thinking-anchored framework for embedding AI in management education. It links historical adoption patterns to current AI dynamics and extending discussions on enhanced rationality to pedagogical practice. Further, it provides actionable guidance to balance AI benefits with risks in ways that support long-term career development.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.758 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.666 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.220 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.896 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.