The School Digital Renewal Process (SDRP) has evolved from infrastructure-focused adoption 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 article proposes a novel, evidence-based approach to constructing indicators that capture shifts in learning content and organization through automated analysis of schools’ digital footprints (publicly available digital materials) using AI tools. Drawing on Bloom’s Revised Taxonomy and empirical data from international schools, we demonstrate the feasibility of tracking second-order educational change without relying on teacher surveys. The framework supports comparative monitoring of digital transformation aligned with the demands of the AI era. The article introduces a groundbreaking innovation: the use of AI tools for gathering and analyzing indicators from publicly available digital sources in education institutions. This approach offers a scalable and cost-efficient way to track and evaluate SDRP at later stages of its development.

The platform integrates multi-omic data to drive both biomedical research and clinical practice.

Its technology, based on Graph Foundation Models, extracts robust conclusions even from the limited and heterogeneous samples typical of the biomedical field.

A Harvard study shows that soft materials, like gels and biological tissue, support boat wake-like surface waves.