ALGORITHMIC GOVERNANCE IN EDUCATION: A BIBLIOMETRIC AND CRITICAL ANALYSIS OF ARTIFICIAL INTELLIGENCE INTEGRATION IN TEACHING AND LEARNING SYSTEMS
DOI:
https://doi.org/10.65222/Keywords:
Abstract
The rapid integration of Artificial Intelligence (AI) in education has significantly reshaped teaching, learning, and institutional governance. This study provides a bibliometric and critical analysis of research on algorithmic governance in education, focusing on publications indexed between 2015 and 2025. Drawing on a systematic review of peer-reviewed literature, the paper identifies key thematic clusters, emerging trends, and critical gaps.
The methodological approach combines bibliometric mapping techniques with qualitative thematic analysis, enabling both quantitative identification of research patterns and in-depth conceptual interpretation. Specifically, the study examines publication dynamics, citation structures, thematic clustering, and geographical distribution of research outputs.
The results indicate a sharp increase in scientific production after 2020, with a growth rate exceeding 50% in certain years, alongside a shift toward ethical and governance-related concerns. Learning analytics and AI-driven personalization emerge as the most cited domains, while algorithmic bias and transparency represent the fastest-growing areas of critical inquiry.
The study advances the literature by proposing an integrative governance-oriented framework that connects technological innovation with ethical accountability and institutional decision-making. In practical terms, the findings support the development of policy-oriented approaches to AI implementation in education.
The study contributes to the literature by offering a multi-layered understanding of AI integration in education, combining bibliometric evidence with critical theoretical insights. It further provides actionable implications for policymakers, educational leaders, and technology developers aiming to implement AI in a responsible and sustainable manner.
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