The school digital renewal process(SDRP)has evolved from adoption at the infrastructure level to deep pedagogical transformation centered on personalized,competence-based learning.Traditional indicators,such as device...The school digital renewal process(SDRP)has evolved from adoption at the infrastructure level to deep pedagogical transformation centered on personalized,competence-based learning.Traditional indicators,such as device availability or connectivity,lose relevance at advanced SDRP stages.This paper proposes a novel,evidence-based approach to constructing indicators that capture shifts in learning content and organization through an automated analysis of schools’digital footprints using AI tools,such as publicly available digital resources.Drawing on the Bloom’s revised taxonomy and empirical data from international schools,we demonstrate the feasibility of tracking second-order changes without relying on teacher surveys.The framework supports the comparative monitoring of digital transformation aligned with the demands of the age of AI.The paper introduces a groundbreaking innovation:the use of AI tools for gathering and analyzing indicators from publicly available digital resources in schools.This approach offers a scalable and cost-efficient method of tracking and evaluating SDRP at the later stages of development.展开更多
文摘The school digital renewal process(SDRP)has evolved from adoption at the infrastructure level to deep pedagogical transformation centered on personalized,competence-based learning.Traditional indicators,such as device availability or connectivity,lose relevance at advanced SDRP stages.This paper proposes a novel,evidence-based approach to constructing indicators that capture shifts in learning content and organization through an automated analysis of schools’digital footprints using AI tools,such as publicly available digital resources.Drawing on the Bloom’s revised taxonomy and empirical data from international schools,we demonstrate the feasibility of tracking second-order changes without relying on teacher surveys.The framework supports the comparative monitoring of digital transformation aligned with the demands of the age of AI.The paper introduces a groundbreaking innovation:the use of AI tools for gathering and analyzing indicators from publicly available digital resources in schools.This approach offers a scalable and cost-efficient method of tracking and evaluating SDRP at the later stages of development.
