AI evidence extraction

Artificial Intelligence in Assessment: A Bibliometric Review of Research Development, Thematic Patterns and Research Clusters

Ashmimi Maisara Asha’ari, Anis Diyana Halim, Ketang Wiyono · International Journal of Instruction · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.29333/iji.2026.19238a

Methodology & findings

Study design

Bibliometric analysis of 68 Scopus-indexed publications from 2011 to July 2025, including citation analysis, h-index and g-index calculation, keyword co-occurrence analysis, and thematic mapping to identify research clusters and trends..

Main result

The study found that "The dataset recorded 1,581 total citations, with an h-index of 22 and g-index of 38, indicating steady growth that intensified after 2021 as AI adoption expanded in post-pandemic education." Additionally, "Keyword co-occurrence analysis revealed three dominant thematic clusters: technology-enhanced learning and automated feedback, machine learning with ethical considerations, and AI-supported formative assessment in science and engineering education."

Research paradigm

Positivist/Empiricist (bibliometric analysis of quantitative publication data)

Author conclusions

The authors conclude that "this study provides a comprehensive overview of global trends, leading contributors, and emerging themes that inform future research and policy directions in AI-driven educational assessment." They further note that "the findings highlight AI's transformative potential for adaptive, feedback-oriented learning" while acknowledging persistent implementation challenges.

Risk of bias

Database selection bias: limited to Scopus-indexed publications, potentially excluding non-indexed journals and grey literature; Language bias: likely English-language publications predominate in Scopus; Publication bias: only published work included, not unpublished or negative results; Geographic bias: overrepresentation of English-speaking countries (US, UK, Germany); Publication bias (indexed publications may skew toward positive findings)

Open questions raised

Future research directions include:
addressing accessibility and equity in AI-based assessment systems
improving curricular integration of AI tools in educational contexts
enhancing teacher readiness and professional development for AI-driven assessment
establishing ethical governance frameworks for AI in education
strengthening interdisciplinary collaboration between AI researchers and education practitioners.

Data: not_statedCode: not_statedExtracted from: pdf

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