محمدشریف شریفی پورقشمی

Artificial Intelligence has is a pivotal tool in enhancing crisis management during natural disasters such as earthquakes. This article examines the role of AI in generating real-time information, predicting stakeholder and population responses, and optimizing decision-making processes. A meta-analysis of existing literature highlights AI's potential to transform traditional crisis management approaches by enabling rapid assessment of disaster scenarios, efficient resource allocation, and communication strategies tailored to situational dynamics. However, the integration of AI in such critical domains requires not only technological advancement but also a paradigm shift in the skills and competencies of crisis management professionals. This paper argues for the necessity of continuous education for stakeholders, emphasizing training in AI tools and data literacy to ensure effective utilization of these technologies in high-stakes environments. Furthermore, it explores the ethical implications of AI use in crisis contexts, including issues of accountability, data privacy, and bias mitigation. To address these challenges through sustained education and interdisciplinary collaboration, stakeholders should harness AI’s full potential while minimizing risks.

This study presents an integrative review of scholarly literature published between 2016 and 2022 to synthesize how AI literacy has been conceptualized, taught, and measured. Drawing on over 40 empirical and theoretical sources, the review maps trends across educational levels—from primary to higher and adult education—and user groups, including students, teachers, professionals, and the general public. Key findings reveal significant diversity in pedagogical strategies, ranging from project-based learning and digital storytelling to flipped classrooms and professional development programs. Conceptualizations of AI literacy vary widely, encompassing technical, ethical, civic, and empowerment-oriented dimensions. Meanwhile, the field continues to face challenges related to definitional ambiguity, limited standardization of assessment tools, and inconsistent integration of ethical and socio-cultural considerations. The review emphasizes the need for convergence around shared frameworks, scalable pedagogies, and cross-contextual evaluation tools. It concludes by identifying future directions for inclusive, empirically grounded, and ethically aware AI literacy initiatives aimed at fostering reflective and informed engagement with AI systems.