AI Literacy Framework (ALiF): A Comprehensive Approach to Developing AI Competencies in Educational and Healthcare Settings

Abstract

Introduction: Artificial intelligence rapidly transforms professional environments, yet structured approaches to developing AI literacy across diverse stakeholder groups remain limited. This paper introduces the AI Literacy Framework (ALiF), addressing the critical gap between AI adoption and competency development in educational and healthcare settings. Methods: The development of ALiF involved a comprehensive literature review, analysis of empirical studies, and synthesis of existing frameworks. The methodology identified recurring competency patterns, progression pathways, and role-specific needs across educational and healthcare contexts. Results: The framework comprises five core components: Technical Understanding, Critical Evaluation, Practical Application, Ethical Considerations, and Data Literacy. These components are structured across three progression levels (Foundation, Intermediate, Advanced) and adapted into five role-specific frameworks for learners, educators, researchers, clinicians, and administrators. A multi-modal assessment approach was developed, incorporating self-assessment, practical tasks, portfolio evaluation, and peer review. Discussion: ALiF addresses the limitations of existing frameworks by integrating technical skills with ethical awareness, establishing clear progression pathways, and providing role-specific adaptations. The framework offers a systematic approach to AI literacy development tailored to institutional resources and priorities, supporting effective, ethical, and innovative use of AI across professional contexts.

Ähnliche Arbeiten