Scientometric Analysis of Adaptive Learning Literature: Insights from the OpenAlex Database

Scientometric Analysis of Adaptive Learning Literature: Insights from the OpenAlex Database

Library and Information Science

This scientometric study conducts a comprehensive analysis of adaptive learning literature using the OpenAlex database to map the intellectual landscape, research trends, and global collaboration patterns from 2001 to 2025. By analyzing 22,234 publications, the study reveals exponential growth in research output, with a peak of 2234 publications in 2024, driven by advancements in AI and educational technology. The United States dominates in citations (63,782) and collaboration strength (2762), while China leads in document production (2354) but with lower citation impact. Key findings highlight the centrality of institutions like the Chinese Academy of Sciences (16,248 link strength) and Aalto University (3110 citations) in global networks. Keyword co-occurrence identifies “Computer Science” (11,929 occurrences) and “Artificial Intelligence” (8656) as dominant themes, reflecting the field’s technical focus. The study also uncovers disparities in open-access adoption, with 40% of influential works remaining paywalled despite a shift toward open models in STEM. Visualizations of co-authorship networks (104 countries, 1338 links) demonstrate expanding global collaboration, including emerging contributions from developing nations. The analysis underscores the need for greater interdisciplinary integration, pedagogical exploration, and equitable access to bridge gaps in adaptive learning research. These insights provide a roadmap for future studies, emphasizing the role of international partnerships and emerging technologies in shaping the field.

Prasanna Kumar Konch

Hirak Jyoti Hazarika

Education for Information

Sage

Digital

English

Research Paper

DOI: 10.1177/01678329251364338