Rethinking artificial-intelligence literacy through the lens of teacher educators: The adaptive AI model
Liat Eyal
Abstract
• The article critiques existing models for assessing AI literacy in education , pointing out their limitations such as a linear approach, overlooking contextual factors, and evaluating static situations instead of dynamic skill development. • The research introduces a novel and adaptive Artificial-Intelligence-Literacy Model (AALM) based on three interconnected core axes: contextual fitness, professional needs, and dynamic development. • In collaboration with teacher education experts, a reflective self-assessment tool was developed, enabling teachers to evaluate their AI literacy according to the proposed model. As artificial intelligence becomes increasingly integrated into educational settings, models for measuring related literacy among both teachers and students are rapidly emerging. Yet despite their strengths and benefits, many impose fixed competency levels or overlook contextual factors. Using design-based research, and with the participation of 22 higher-education teacher educators, this study critiques existing models and introduces the novel Adaptive Artificial-Intelligence-Literacy Model (AALM), grounded in case-study analysis. This evaluation framework highlights the dynamic, multi-dimensional nature of artificial-intelligence literacy, organized around three inter-related core axes: contextual fitness, professional needs, and dynamic development. A reflective self-assessment tool is also presented, enabling teachers to evaluate their own artificial-intelligence literacy. This framework offers practical guidance for educational policy and teacher development, advocating for assessment approaches that consider social and cultural contexts, professional needs, and the evolving nature of skills amid rapid technological change. Finally, the case studies illustrate the model's relevance across diverse educational settings.