A new study published in Frontiers of Digital Education introduces a groundbreaking method for tracking advanced stages of digital transformation in schools—moving beyond outdated metrics like device counts to assess deeper pedagogical change. Authored by Alexander Uvarov of the Moscow City Pedagogical University, the research proposes AI-powered indicators that analyze publicly available digital materials—such as curricula, lesson plans, and project reports—to evaluate progress toward personalized, competence-based learning (PCBL), the hallmark of mature digital renewal.

The School Digital Renewal Process (SDRP) evolves through four stages, from basic infrastructure deployment to systemic reorganization centered on individual student mastery. While early-stage progress is easily measured by connectivity or hardware access, later stages demand indicators that capture shifts in educational content and learning organization. Uvarov’s framework leverages Bloom’s Revised Taxonomy to automatically classify learning objectives by cognitive complexity—identifying whether schools emphasize rote memorization or higher-order skills like evaluating and creating.

In a pilot study, ChatGPT analyzed digital footprints from seven schools across Europe and Latin America, coding learning goals along knowledge and cognitive dimensions. Results showed a strong emphasis on procedural knowledge and application-level tasks, with minimal presence of metacognitive objectives—even in digitally advanced Finnish schools. This suggests a global gap in fostering self-regulated learning despite technological progress.

The study outlines three future scenarios for education: inertial (technology reinforcing traditional models), transformational (schools evolving into personalized learning hubs), and divergent (formal schooling fragmenting amid alternative digital options). Only the transformational path, the author argues, ensures equitable, future-ready education.

Critically, this AI-driven approach eliminates reliance on teacher surveys or classroom observations, offering a scalable, low-cost tool for national and international monitoring—potentially informing indices like the Global Digital Education Index. Uvarov calls for integrating such indicators into policy frameworks to guide schools toward meaningful digital transformation in the AI era.

The work titled “Developing Indicators for School Digital Renewal in the Age of AI”, was published on Frontiers of Digital Education (published on February 3, 2026).
DOI：10.1007/s44366-026-0080-4