From Crisis to Innovation: Forecasting Educational Technology Research Through Bibliometric Analysis of Pandemic-Era Publications (2020–2024)

Abstract

The COVID-19 pandemic catalyzed a dramatic reshaping of the educational technology landscape, creating an urgent need to analyze the field’s new trajectories and inform future directions. This study addresses that need by uniquely combining bibliometric analysis with machine learning forecasting to map and predict research trends using a comprehensive dataset of 9,630 articles from 20 high-impact journals (2020–2024). Our analysis reveals three significant findings: (1) research is heavily concentrated on scalable technologies like MOOCs and AI, while critical gaps persist in equity, open educational resources, and micro-learning; (2) methodological sophistication is increasing, with a rise in mixed-methods and meta-analytic approaches; and (3) nascent interdisciplinary connections with fields like healthcare offer promising new research frontiers. Forecasting models predict exponential growth in AI and computational thinking research through 2030, while pandemic-specific topics are expected to decline. These results provide a strategic roadmap for researchers, policymakers, and practitioners to prioritize underexplored areas, foster impactful interdisciplinary collaborations, and critically address the ethical implications of emerging technologies to build a more equitable and effective educational future.