A Conceptual Framework for Artificial Intelligence Readiness in Management Education

Abdul Hamid Arribathi; Fitra Putri Oganda; Efa Ayu Nabila; Zabenaso Queen

doi:10.34306/itsdi.v8i1.741

Authors

Abdul Hamid Arribathi Universitas Raharja Indonesia https://orcid.org/0000-0002-6303-0587
Fitra Putri Oganda Universitas Raharja Indonesia https://orcid.org/0000-0002-4590-0657
Efa Ayu Nabila Universitas Raharja Indonesia https://orcid.org/0000-0002-6446-2613
Zabenaso Queen Mfinitee Incorporation South Africa https://orcid.org/0009-0003-0763-9931

DOI:

https://doi.org/10.34306/itsdi.v8i1.741

Keywords:

Artificial Intelligence, Technology Readiness, Collaborative Learning, Management Education, AI Readiness

Abstract

Artificial Intelligence (AI) is increasingly transforming educational and professional environments, highlighting the importance of individual readiness for responsible technology adoption. This study examines individual AI readiness in management education through a literature-based conceptual review of the Artificial Intelligence Readiness Scale (AIRS). The analysis focuses on four dimensions: self-efficacy and cognition, optimism, collaborative learning and working, and comfort. By synthesizing relevant literature, this study develops a conceptual framework connecting these dimensions with educational competencies, contextual learning indicators, and strategies for AI readiness development. The findings suggest that AI readiness extends beyond technical proficiency by incorporating cognitive, attitudinal, collaborative, and psychological factors. The proposed framework highlights the relevance of AI literacy, analytical reasoning, collaborative problem-solving, and psychological comfort in AI-supported management education. Furthermore, the conceptual evaluation identifies potential strengths and limitations of AIRS, particularly in distinguishing perceived readiness from demonstrated competence and actual AI adoption. These findings provide practical considerations for educational institutions seeking to design targeted training programs and responsible AI integration strategies. As the proposed framework has not been empirically validated, further research is required to evaluate its applicability and effectiveness among students, lecturers, and management professionals.

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